POSTTRAUMATIC STRESS AND ADAPTATION IN PATIENTS FOLLOWING ACUTE CARDIAC EVENTS

Transcription

1 POSTTRAUMATIC STRESS AND ADAPTATION IN PATIENTS FOLLOWING ACUTE CARDIAC EVENTS Anna Wikman Department of Epidemiology and Public Health University College London 2009 Thesis submitted to the University of London for the degree of Doctor of Philosophy 1

2 I, Anna Wikman, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Signature Date 2

3 Abstract The aim of this thesis is to investigate emotional recovery following an acute coronary syndrome (ACS), and the factors that predict the development of posttraumatic stress symptoms. The overall objective of this work is to increase understanding of recovery and adaptation following ACS. This thesis will present data from two prospective studies of psychological aspects of ACS; the Acute Coronary Syndrome Emotional Triggering (ACCENT) study and the Tracking Recovery after Acute Cardiac Events (TRACE) study. Although most commonly diagnosed in individuals that have experienced traumatic events such as war, natural disasters or assault, there is now increasing evidence of posttraumatic stress disorder (PTSD) in individuals after onset, diagnosis, or treatment for physical illness. ACS, like other nonmedical trauma, is life-threatening, sudden and often unexpected. Many patients report an intense fear of dying, and emotional distress such as anxiety and depression during the acute phase and in the immediate aftermath is common. Although most patients will fully recover from this emotional distress, some do not recover and distress can persist for a significant period of time. The persistent and severe psychological distress experienced by some patients may actually satisfy criteria for a diagnosis of PTSD. PTSD risk factor research indicates that individuals experiences during traumatic events play a significant role in differentiating between those who subsequently go on to develop PTSD and those who do not. Within this thesis, data will be presented on the longer term prevalence and predictors of posttraumatic stress in patients following ACS, as well as the underlying biological and cognitive correlates which may increase vulnerability to emotional distress and risk of future cardiac events. 3

4 Table of contents Abstract... 3 Table of contents... 4 List of tables List of figures Publications Acknowledgments List of abbreviations CHAPTER 1. Literature Review: Psychosocial risk factors and Cardiovascular Disease Overview of Cardiovascular Disease Psychological factors in the development of CHD Depression Anxiety Anger and Hostility Psychological consequences of CHD Rates and prognostic implications of depression following CHD Anxiety Type D personality Pathways between negative affect and CHD Overlapping affective dispositions Summary CHAPTER 2. Literature Review: Posttraumatic Stress Disorder Introduction to Posttraumatic Stress Disorder Current understanding of PTSD Models of PTSD Emotional processing theory Dual representation model Ehlers and Clarke s cognitive model Summary of current models of PTSD PTSD following non-medical trauma Medical events as traumatic stressors PTSD following medical trauma PTSD as a consequence of Acute Coronary Syndrome

5 2.7.1 Prevalence of PTSD following ACS Predictors of posttraumatic stress symptoms following ACS Distinctive features of ACS-related PTSD Consequences of PTSD following ACS The relationship between PTSD and depression The role of PTSD in the development of coronary heart disease Psychophysiology of PTSD Cortisol Heart rate Chapter summary CHAPTER 3. Methodology ACCENT study Introduction and hypotheses Participants Study design and procedure My role in study design, data collection and analysis Psychosocial measures Socio-demographic information Clinical data Psychological measures Beck Depression Inventory (BDI) Posttraumatic Stress Symptoms Self Report Scale (PSS-SR) Hospital Anxiety Scale (HADS-A) Medical Outcome Short Form 36 (SF36) Cook and Medley Hostility Scale (Ho) Type D (DS16) Fear, helplessness and horror Acute stress Acute stress disorder Data storage Statistical analyses CHAPTER 4. Results ACCENT Study Results Data analyses Patient characteristics Prevalence of posttraumatic stress symptoms at 12 and 36 months

6 4.1.4 Comparisons of psychological variables between PTSD and non-ptsd groups Predictors of posttraumatic stress symptom severity at 12 months Predictors of posttraumatic stress symptoms at 36 months post ACS Discussion Strengths and limitations Summary CHAPTER 5. Methodology TRACE study Introduction and hypotheses Acute post-acs emotional responses and their relationship with short (2 week) and long term (six months) posttraumatic stress reactions Introduction to illness representations Causal attributions and CHD Illness representations and post-mi recovery The relationship between illness representations and post-mi depression and quality of life Posttraumatic stress and illness representations Biological determinants of early emotional responses to ACS Cortisol Heart rate variability The relationship between posttraumatic stress responses and post ACS adaptation Influence of partner distress on patient posttraumatic stress reactions Study design My role in study design, data collection and analysis Participants Procedure Time 1 assessment Time 2 assessment Time 3 follow up assessment Measures Measures Time Socio-demographic information Clinical data Psychosocial measures Profile of Mood States (POMS)

7 Medical Outcome Short Form 12 (SF-12) Measures Time Psychosocial measures DISH Social Network Social Support Causal Beliefs Illness Perception Questionnaire Revised Health behaviours Smoking Alcohol consumption Diet Physical activity Adherence to medications Biological measures Salivary cortisol Heart rate variability Measures Time Psychosocial measures Health behaviours Data storage Statistical analyses CHAPTER 6. Results TRACE Study I Data analysis Patient characteristics Posttraumatic stress symptoms 3 4 weeks post ACS (time 2) Acute admission predictors of posttraumatic stress symptoms at time Multivariate predictors of posttraumatic stress symptoms at time Psychosocial predictors of posttraumatic stress at time Illness representations and current mood state in relation to posttraumatic stress reactions at time Posttraumatic stress symptoms and salivary cortisol Posttraumatic stress symptoms and heart rate variability at time Discussion Predicting short term posttraumatic stress symptoms from patients acute post-acs emotional responses

8 6.6.2 Salivary cortisol and heart rate variability in the immediate aftermath of ACS predictors of acute emotional reactions Summary CHAPTER 7. Results TRACE Study II Data analysis Patient characteristics Posttraumatic stress symptoms six months (time 3) post ACS Multivariate predictors of posttraumatic stress symptoms at time Cognitive predictors of posttraumatic stress symptoms at time Multivariate predictors of posttraumatic stress symptoms at six months Posttraumatic stress symptoms, health behaviour and psychosocial adjustment at time Partner posttraumatic stress reactions and post ACS patient emotional recovery Posttraumatic stress at six months and salivary cortisol Time 2 heart rate variability and six-month posttraumatic stress Discussion Predicting six-month posttraumatic stress symptoms from patients emotional and cognitive post-acs reactions Adjustment The influence of post ACS biological dysfunction on patients posttraumatic stress responses at six months The association of partner distress with patients posttraumatic stress reactions TRACE: Study strengths and limitations Summary CHAPTER 8. General discussion of research carried out in this thesis ACCENT and TRACE studies Aims Accent the relationship between emotional reactions to ACS and long-term posttraumatic stress TRACE the relationship between acute emotional reactions to ACS and posttraumatic stress 3 4 weeks and six months post trauma Comparability of ACCENT and TRACE findings Predictor variables ACCENT and TRACE Cognitive factors TRACE

9 8.1.4 Biological dysfunction post ACS and later posttraumatic stress Cortisol Heart rate variability General thesis limitations Study design Measurement issues PTSD assessment Cortisol assessment Response rate, sample size and power Clinical implications Directions for future research Key message of thesis Conclusion References Appendix I: ACCENT 12 and 36 month Interview.308 Appendix II: ACCENT 12 and 36 month questionnaire Appendix III: TRACE patient information sheet Appendix IV: TRACE consent form.320 Appendix V: TRACE time 1, time 2 and time 3 questionnaires Appendix VI: TRACE time 3 telephone interview.365 Appendix VII: TRACE cortisol diary.367 9

10 List of tables TABLE 2.1 DSM-IV DIAGNOSTIC CRITERIA FOR POSTTRAUMATIC STRESS DISORDER TABLE 2.2 PREVALENCE OF PTSD FOLLOWING ACS TABLE 2.3 RISK FACTORS FOR PTSD FOLLOWING ACS TABLE 3.1 MEASURES OBTAINED AT EACH TIME POINT TABLE 4.1 PATIENT CHARACTERISTICS 12 MONTH SAMPLE TABLE 4.2 PSS-SR SCORES TABLE 4.3 PSYCHOLOGICAL VARIABLES BY PTSD CASENESS TABLE 4.4 PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS TABLE 4.5 CORRELATIONS BETWEEN PSYCHOLOGICAL PREDICTOR VARIABLES TABLE 4.6 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT 12 MONTHS TABLE 4.7 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT 36 MONTHS TABLE 5.1 REASONS FOR EXCLUSIONS AND REFUSALS TABLE 5.2 MEASURES OBTAINED AT EACH TIME POINT TABLE 5.3 CRONBACH S ALPHA FOR MEASURES ADMINISTERED IN TRACE TABLE 6.1 PATIENT CHARACTERISTICS TABLE 6.2 PSS-SR SCORES AT TIME TABLE 6.3 CORRELATIONS BETWEEN BASLINE VARIABLES (TIME 1) AND TIME 2 POSTTRAUMATIC STRESS SYMPTOMS TABLE 6.4 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT TIME TABLE 6.5 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT TIME TABLE 6.6 CORRELATIONS BETWEEN PSYCHOSOCIAL RISK FACTORS AND TIME 2 POSTTRAUMATIC STRESS SYMPTOMS TABLE 6.7 PSYCHOSOCIAL PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT TIME TABLE 6.8 CORRELATIONS BETWEEN TIME 2 PSYCHOLOGICAL VARIABLES AND TIME 2 POSTTRAUMATIC STRESS SYMPTOMS TABLE 6.9 ILLNESS REPRESENTATION CHARACTERISTICS AT TIME TABLE 6.10 CORRELATIONS BETWEEN TIME 2 ILLNESS REPRESENTATION DIMENSIONS TABLE 6.11 CORRELATIONS BETWEEN TIME 2 ILLNESS REPRESENTATIONS AND TIME 2 POSTTRAUMATIC STRESS SYMPTOMS TABLE 6.12 COGNITIVE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT TIME

11 TABLE 6.13 POSTTRAUMATIC STRESS SYMPTOMS AND SALIVARY CORTISOL AT TIME TABLE 6.14 POSTTRAUMATIC STRESS SYMPTOMS AND TOTAL CORTISOL OUTPUT AT TIME TABLE 6.15 INDICES OF HEART RATE VARIABILITY AT TIME TABLE 6.16 ASSOCIATIONS BETWEEN TIME 2 HEART RATE VARIABILITY AND TIME 2 POSTTRAUMATIC STRESS TABLE 6.17 PTSD AND HEART RATE VARIABILITY AT TIME TABLE 7.1 PATIENT CHARACTERISTICS AT TIME TABLE 7.2 PSS-SR SCORES AT SIX MONTHS POST ACS TABLE 7.3 PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS TABLE 7.4 CORRELATIONS BETWEEN PSYCHOLOGICAL PREDICTOR VARIABLES TABLE 7.5 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT SIX MONTHS TABLE 7.6 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT SIX MONTHS TABLE 7.7 TIME 3 ILLNESS REPRESENTATIONS TABLE 7.8 CORRELATIONS BETWEEN TIME 2 ILLNESS REPRESENTATIONS AND TIME 3 ILLNESS REPRESENTATIONS TABLE 7.9 CORRELATIONS BETWEEN TIME 2 ILLNESS REPRESENTATIONS AND TIME 3 POSTTRAUMATIC STRESS SYMPTOMS TABLE 7.10 MULTIVARIATE COGNITIVE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT SIX MONTHS TABLE 7.11 MULTIVARIATE COGNITIVE PREDICTORS OF POSTTRAUMATIC INTRUSION SYMPTOMS AT SIX MONTHS TABLE 7.12 MULTIVARIATE COGNITIVE PREDICTORS OF POSTTRAUMATIC AVOIDANCE SYMPTOMS AT SIX MONTHS TABLE 7.13 MULTIVARIATE COGNITIVE PREDICTORS OF POSTTRAUMATIC AROUSAL SYMPTOMS AT SIX MONTHS TABLE 7.14 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT SIX MONTHS CONCURRENT ILLNESS BELIEFS TABLE 7.15 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC INTRUSION SYMPTOMS AT SIX MONTHS CONCURRENT ILLNESS BELIEFS TABLE 7.16 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC AVOIDANCE SYMPTOMS AT SIX MONTHS CONCURRENT ILLNESS BELIEFS TABLE 7.17 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC AROUSAL SYMPTOMS AT SIX MONTHS CONCURRENT ILLNESS BELIEFS

12 TABLE 7.18 COMBINED MULTIVARIATE PREDICTOR MODEL OF SIX MONTH POSTTRAUMATIC STRESS SYMPTOMS TABLE 7.19 UNADJUSTED COMBINED MULTIVARIATE PREDICTOR MODEL OF SIX MONTH POSTTRAUMATIC STRESS SYMPTOMS TABLE 7.20 HEALTH BEHAVIOUR CHANGE TIME 2 TO TIME TABLE 7.21 PHYSICAL AND MENTAL HEALTH STATUS CHANGE TIME 2 TO TIME TABLE 7.22 THE RELATIONSHIP BETWEEN TIME 2 POSTTRAUMATIC STRESS SYMPTOMS AND PHYSICAL AND MENTAL HEALTH STATUS CHANGE TABLE 7.23 CORRELATIONS BETWEEN PATIENT AND PARTNER EMOTIONAL REACTIONS AT TIME TABLE 7.24 CORRELATIONS BETWEEN PATIENT AND PARTNER EMOTIONAL REACTIONS AT TIME TABLE 7.25 PARTNER PSS-SR SCORES AT TIME 2 AND TIME TABLE 7.26 PARTNER POSTTRAUMATIC STRESS AS A PREDICTOR OF PATIENT POSTTRAUMATIC STRESS AT SIX MONTHS TABLE 7.27 POSTTRAUMATIC STRESS SYMPTOMS AT TIME 3 AND SALIVARY CORTISOL AT TIME TABLE 7.28 HEART RATE VARIABILITY (TIME 2) AND PTSD AT TIME TABLE 7.29 POSTTRAUMATIC STRESS SYMPTOMS (ORIGINAL CRITERIA TIME 3) AND HEART RATE VARIABILITY AT TIME TABLE 8.1 OVERVIEW ACCENT AND TRACE STUDIES TABLE 8.2 SUMMARY OF PREDICTOR VARIABLES ACCENT AND TRACE

13 List of figures FIGURE 2.1 HYPOTHALAMIC-PITUITARY-ADRENAL AXIS FIGURE 3.1 FLOWCHART OF PATIENT RECRUITMENT FIGURE 4.1 THE RELATIONSHIP BETWEEN DEPRESSION SCORES AT BASELINE AND 12 MONTH POSTTRAUMATIC STRESS SYMPTOMS FIGURE 5.1 TRACE STUDY DESIGN FIGURE 6.1 TYPE D PERSONALITY AND POSTTRAUMATIC STRESS SYMPTOMS FIGURE 6.2 PROFILE OF SALIVARY CORTISOL THROUGHOUT THE DAY AT TIME FIGURE 6.3 THE RELATIONSHIP BETWEEN POSTTRAUMATIC STRESS SYMPTOMS AND TOTAL CORTISOL OUTPUT CONTROLLING FOR DEPRESSION FIGURE 6.4 THE RELATIONSHIP BETWEEN ACUTE STRESS AND CORTISOL AT TIME FIGURE 8.1 KEY FINDINGS OF ACCENT AND TRACE INVESTIGATIONS

14 Publications Some of the research described in this thesis has been published, and other sections have been submitted for publication. In addition, some of the research described has been presented in conferences. Publications: Wikman, A., Bhattacharyya, M., Perkins-Porras, L., Steptoe, A. (2008) Posttraumatic stress symptoms 12 and 36 months post acute coronary syndrome. Psychosomatic Medicine; 70: Bhattacharyya, M., Perkins-Porras, L., Wikman, A., Steptoe, A. (2008) The long-term effects of acute triggers of acute coronary syndromes on adaptation and quality of life. International journal of cardiology, [In press, corrected proof, available online]. Conference presentations: *American Psychosomatic Society Annual Conference, Chicago, USA, Posttraumatic stress and anxiety symptoms in partners of patients following acute coronary syndrome. *American Psychosomatic Society Annual Conference, Baltimore, USA, Posttraumatic stress symptoms 12 and 36 months following ACS. *Erice International School of Ethology: The inevitable link between heart and behaviour workshop, Erice, Sicily, PTSD at 12 and 36 months post ACS. 14

15 Acknowledgments First and foremost, I wish to express my gratitude and thanks to my thesis supervisor, Professor Andrew Steptoe, for his continued and invaluable support and guidance throughout this process. I am also grateful for the support offered by a number of my colleagues, who have contributed to the collection and processing of data, as well as offered me advice and support when needed, these include Dr Gerry Molly, Dr Nadine Messerli-Burgy, Dr Linda Perkins-Porras, Dr Mimi Bhattacharyya and Dr Emily Williams. I would like to extend my gratitude to Professor Chris Brewin for the much appreciated advice on PTSD he has offered on many occasions. Thanks also to Dr Samantha Dockray for her many helpful suggestions. I would like to thank all the patients who agreed to participate in the research projects. I am also grateful to the Medical Research Council for the funding I have received to complete this thesis, and the British Heart Foundation for funding these projects. A special thank you goes to my very dear friends and colleagues Nina Grant and Gemma Randall. Their presence have made every day in the office a joy. Without their friendship, support and encouragement this thesis would never have been completed, and I am forever grateful. Finally I would like to thank my partner, Vicent Garcia, for being understanding when I have been unreasonable, for being supportive when I have not deserved it, and for always providing excellent IT support, which has been so desperately needed. 15

16 List of abbreviations ACCENT Acute coronary syndrome emotion and triggers study ACE Angiotensin converting enzyme ACS Acute coronary syndrome ACTH Adreno corticotrophin hormone ANS Autonomic nervous system ASD Acute stress disorder AUC Area under the curve BDI Beck depression inventory BMI Body mass index BPM Beats per minute CABG Coronary artery bypass graft CAD Coronary artery disease CAPS Clinician Administered PTSD scale CAR Cortisol awakening response CHD Coronary heart disease CI Confidence interval CRP C-Reactive protein CVD Cardiovascular disease DBP Diastolic blood pressure DISH Depression Interview and Structured Hamilton DS14 Type D personality scale -14 DS16 Type D personality scale -16 DSM Diagnostics and statistical manual DTS The Davidson Trauma Scale ECG Electrocardiogram ED Emergency department EE Expressed emotion GRACE Global registry of acute coronary events HADS Hospital anxiety and depression scale HF High frequency HO Cook-Medley hostility scale HPA Hypothalamic pituitary adrenal axis HR Heart rate HRV Heart rate variability IBI Inter beat interval IES Impact of events scale IL-10 Interleukin - 10 IL-6 Interleukin - 6 IPQ-R Illness perception questionnaire - revised LF Low frequency LVEF Left ventricular ejection fraction MARS Medication Adherence Report Scale MI Myocardial infarction NA Negative affectivity NSTEMI Non-ST elevation myocardial infarction OR Odds ratio PDS Posttraumatic Stress Diagnostic Scale POMS Profile of mood states PSS Posttraumatic Stress Disorder Symptom Scale PTSD Posttraumatic stress disorder 16

17 QOL Quality of life QRS Q, R, S waves RMSSD root of the mean square difference RR Relative risk R-R Beat to beat SAM Semantically accessible memory SBP Systolic bloody pressure SCID Structured clinical interview SD Standard deviation SDNN standard deviation of normal mean SE Standard error SES Socioeconomic status SF12 Medical Outcome Short Form 12 SF36 Medical Outcome Short Form 36 SI Social inhibition SRI Simple risk index STAI State trait anxiety inventory STEMI ST elevation myocardial infarction TRACE Tracking recovery after cardiac events study UA Unstable angina UFC Urinary free cortisol VAM Verbally accessibly memory VLF Very low frequency 17

18 CHAPTER 1. Literature Review: Psychosocial risk factors and Cardiovascular Disease 1.1 Overview of Cardiovascular Disease Cardiovascular disease (CVD) is by far the most common cause of death, and premature death, in the United Kingdom (UK). Approximately one in three deaths each year are caused by CVD. Coronary heart disease (CHD) accounts for almost half (48%) of deaths from CVD. CHD claims around 94,000 lives each year. CHD kills approximately one in five men and one in six women each year. However these figures have declined over the past 30 years (CVD down by 24% in past 10 years in patients under 75 years, CHD down by 46% in past 10 year in patients under 65). These trends are primarily due to reductions of risk factors, particularly smoking, though improved clinical care has also increased survival following acute coronary syndrome (ACS) (British Heart Foundation [BHF], 2008). CHD occurs when the artery supplying blood to the heart becomes partially or wholly blocked. This is caused by fatty deposits (cholesterol plaques) building up on the inside lining of the arteries (atherosclerosis). When this narrowing of arteries exceeds 50 70%, the blood supply beyond the plaque cannot meet the oxygen demand of the heart on exertion. This causes symptoms of chest pain (angina), which is temporary and treatable, though not everyone will experience pain. If arteries are narrowed in excess of 90 99%, angina may occur even in a resting state (this is referred to as Unstable Angina UA). CHD can result in a heart attack (Myocardial Infarction MI) if the blood supply to the heart is stopped for long enough to cause damage (death of heart muscle tissue). A MI will generally occur suddenly, due to a rupture of an atherosclerotic plaque. Every six minutes someone dies from a heart attack. 18

19 The term acute coronary syndrome encompasses a spectrum of unstable coronary heart disease that includes UA and two forms of MI. The type of MI is determined according to the appearance of the electrocardiogram (ECG/EKG) as ST segment elevation myocardial infarction (STEMI) or non-st segment elevation myocardial infarction (NSTEMI). Combined data from prevalence studies of MI suggest that 4% of men and 2% of women have had a MI (BHF, 2008). The improved survival rates following ACS have led to an increase in the prevalence of patients with ACS, so issues of emotional adjustment and quality of life are becoming increasingly important. There are around 1.4 million people over the age of 35 in the UK who have survived a heart attack (BHF, 2008). Besides the traditional cardiovascular risk factors such as smoking, hypertension and hypercholesterolaemia, four different types of psychosocial factors have been found to be most consistently associated with an increased risk of CHD: work stress, lack of social support, depression and personality (particularly hostility). This chapter will focus on the role of psychosocial factors, in particular psychological factors, in the development of CHD. Psychological consequences of CHD will be discussed in detail and possible mechanisms underlying the association will be evaluated. 1.2 Psychological factors in the development of CHD Negative emotions have been claimed to be a cause of CHD as well as a consequence of the disease. There is growing evidence that negative emotions have cumulative pathophysiological effects that can ultimately lead to CHD events, via accumulation of damage through a steady activation of neurohormonal systems and other mechanisms (Everson-Rose & Lewis, 2005; Kubzansky et al., 2005; Rozanski et al., 2005). Negative emotions may have direct physiologic effects on the development of CHD through the repeated activation of the sympathetic nervous system and 19

20 hypothalamic-pituitary adrenocortical axis (HPA) activation, immune dysregulation, and vascular inflammation. Alternatively negative emotions may affect the development of CHD through indirect pathways such as their negative influence on health behaviours such as smoking, exercise or adherence to medications (Steptoe, 2007). By identifying such plausible causal pathways the argument that negative emotions, such as depression, contribute to the development of CHD can be strengthened. As discussed above a number of pathways have been proposed including behavioural and lifestyle processes, psychosocial factors such as social support, and more direct biological processes. These are discussed more fully in section 1.4 below in the context of linking negative emotions with mortality and morbidity in patients with existing cardiac disease. A body of research has to date shown strong positive associations between three main negative emotions and increased risk of CHD in initially disease free individuals: depression, anxiety and anger. The following sections will discuss the role of depression, anxiety, anger/hostility and type D personality in the development of CHD Depression A number of prospective studies have noted an increased risk of MI and cardiovascular mortality among depressed, but otherwise healthy individuals. However it is important to note that the use of the term depression in the literature sometime refers to depressive symptoms and sometimes to depressive illness. Most studies investigating depression and the aetiology of CHD have used self-report measures to assess symptoms of depression. Other studies have used diagnostic interviews, whereby a clinical diagnosis can be assigned. Only by adopting this method can one refer to the depression observed as depressive illness. In reviewing the literature there appears to be a trend towards positive associations among studies that have involved 20

21 a clinical diagnosis of depression by interview, compared with studies utilizing selfreport questionnaire measures of depressed mood. Several large representative epidemiological studies that have controlled for a broad range of CHD risk factors have established a positive association between depression and CHD. Anda and colleagues (1993) reported a relative risk (RR) of 1.5 for fatal CHD, adjusting for several other factors (e.g. age, gender, BMI, standard CHD risk factors etc), in a sample of >2800 followed up for 12 years, where 11.1% of the sample had depressed affect at baseline. A meta-analysis of 11 published studies demonstrated a strong positive association between depression and incident CHD, with a RR of 2.69 (95% Confidence Intervals [CI]: ) for individuals with clinically relevant levels of depression, and a RR of 1.49 (95% CI: ) for individuals with depressed mood (Rugulies, 2002). A systematic review by Wulsin and Singal (2003) reported a combined overall RR of depression of 1.64 (95% CI: ) for the onset CHD. This risk was greater than the risk conferred by passive smoking (RR= 1.25), however, it was less than the risk conferred by active smoking (RR= 2.5) observed in this review. Findings from the INTERHEART study (Rosengren et al., 2004; Yusuf et al., 2004) showed that psychosocial distress conferred a higher relative risk for MI than did hypertension, abdominal adiposity, diabetes and several other traditional risk factors. Compared with lower levels of depressive symptoms, a high level was associated with a 2.5 fold relative risk. This relationship remained even when all other risk factors were included in the model simultaneously. Some studies have demonstrated a dose-response relationship between depression and future CHD suggesting that individuals with sub-clinical levels of depression may still be at increased risk for CHD. Pratt et al (1996) found major depression to be associated with a 4.5-fold increased risk of MI, whereas dysphoria was associated with a 2.7-fold increased risk. A year follow up of 730 health men and women by Barefoot and Schroll (1996) demonstrated a relative risk of 1.71 and 1.59 for fatal/non-fatal MI and all cause mortality, respectively, for each 2-Standard 21

22 Deviation increase in depression score. These authors concluded, based on the graded relationship observed between depression scores and CHD risk, as well as the apparently long lasting effect, that depression should be considered as a continuous variable, representing a psychological trait not as an episodic psychiatric condition or threshold effect. This intensity effect has been replicated by Pennix et al (2001). More recent research further reinforces the conclusions that higher depression among healthy populations at baseline confers a heightened risk of CHD. A 2007 study from Sweden (Janszky et al., 2007) prospectively followed patients who were hospitalized for depression. The RR of developing an acute MI was 2.9, and this risk persisted for decades after the initial hospitalization. A prospective UK cohort study of initially disease free individuals revealed major depression to be associated with a higher rate of death from CHD. Specifically, patients who had depression currently or in the past 12 months had a 2.7-fold increased risk of dying than those who had never had depression or who had had it more than 12 months previously (Surtees et al., 2008). Frasure-Smith and Lesperance (2005) reviewed 21 aetiological and 43 prognostic publications and concluded that despite the multiple methodological differences between studies, data from prospective adequately powered aetiological and prognostic studies with objective outcome measures (at least one outcome other than angina or self-reported chest pain) and utilizing recognized indices of depression were highly consistent in their support of depression as a risk factor for the development of CHD (as well as the worsening of established CHD). Steptoe (2007) reviewed 27 longitudinal observational studies published between 1964 and 2005, and pointed overall towards a positive association between depression and CHD, although inconsistencies were present. A meta-analysis of cohort studies measuring depression with follow up for fatal CHD/incident MI (aetiological) or all-cause mortality/fatal CHD (prognostic) by Nicholson and colleagues (2006) found significant associations between depression 22

23 and CHD. The pooled RR across 21 aetiological studies was 1.81 (95% CI: ) for future CHD. Adjusted results were included for 11 studies, with adjustments reducing the crude effect marginally from 2.08 (95% CI: ) to 1.90 (95% CI: ). In the 34 prognostic studies included in this analysis, the pooled RR was 1.80 (95% CI: ). However, these authors concluded that due to the biased availability of adjustments, incomplete adjustments, and the possibility of reverse causation, these findings cast doubt on the significant associations observed between depression and future CHD, and whether depression can be considered an independent risk factor is yet to be established. However, a more recent meta-analysis by Van der Kooy and colleauges of 28 epidemiologic studies with nearly 80,000 patients showed depression to be an independent risk factor for the onset of a wide range of CVD (Van der Kooy et al., 2007). The authors acknowledge that the evidence is related to a high level of heterogeneity across studies, and only the overall combined risk of depression for the onset of myocardial infarctions (n=8, RR=1.60, 95% CI: ) was homogenous Anxiety Anxiety has been defined as a state of emotional distress resulting from feelings of being unable to predict, control, or obtain desired outcomes (Barlow, 2004). Anxiety often involves feelings of apprehension and fear characterized by physical symptoms such as palpitations, sweating, and feelings of stress. Anxiety disorders are serious medical illnesses. Unlike the relatively mild, brief anxiety caused by a stressful event such as a public speaking or a job interview, anxiety disorders are chronic, relentless, and can grow progressively worse if not treated. Though relatively fewer studies have investigated anxiety as a risk factor for CHD compared with depression, there is emerging evidence of a prospective relationship between anxiety and CHD in initially disease free individuals. Several large 23

24 studies have noted a relationship between phobic anxiety and sudden cardiac death. An early report by Haines et al (1987) found that high levels of phobic anxiety as measured by the Crown-Crisp index were associated with a RR for fatal CHD of 3.77 (95% CI: ) in a sample of 1457 men. A later study found a dose-response relationship between phobic anxiety and fatal CHD in men, with further analyses revealing this association to be specific to the outcome of sudden cardiac death (RR= 2.5, 95% CI: ). These findings were independent of smoking, alcohol use and a broad range of cardiovascular risk factors. No association was observed between phobic anxiety and either non-fatal MI or total CHD (Kawachi et al., 1994a). Using data from a 32 year follow up of the Normative Ageing Study of 2271 men, aged 21 to 80 years in 1961, Kawachi et al (1994b) observed an age adjusted RR of 3.20 (95% CI: ) for fatal CHD, and an RR for sudden cardiac death of 5.73 (95% CI: ). As risk behaviours such as smoking and excessive alcohol consumption have been related to anxiety, these analyses were adjusted for those variables and other standard CHD risk factors. The authors found that effects became non-significant after taking into account smoking, alcohol consumption and standard CHD risk factors ( RR= 1.94, 95% CI: for fatal CHD; RR= 4.46, 95% CI: for sudden cardiac death). Albert et al (2005) reported findings from a prospective study of women with no history of cardiovascular disease or cancer. At 12 year follow up, women who had scored 4 or higher on the Crown-Crisp Index were at a 1.59 fold (95% CI: ) marginally increased risk of sudden cardiac death and a 1.31 fold (95% CI: ) marginally increased risk of fatal CHD compared with those who scored 0 or 1. After control for possible confounding risk factors (hypertension, diabetes, and elevated cholesterol), a trend toward an increased risk persisted for sudden cardiac death (p=.06). Further analyses in the Normative Ageing Study have examined an additional dimension of anxiety chronic worrying as a risk factor for CHD (Kubzansky et al., 1997). Compared with men reporting the lowest levels of anxiety, men with the highest 24

25 levels were at approximately 2.5 times the risk (95% CI: ) for non-fatal MI, but men with moderate anxiety were also at increased risk (RR=1.70, 95% CI: ). However, it was not possible to determine in this study whether the risk stemmed from the actual content of worry or the severity of worry. Similar effects have been observed in a sample of women. Eaker et al (1992) reported findings from a 20 year follow up of 749 initially health women. A significant association of anxiety symptoms with MI and fatal CHD among homemakers was found, however, this was not observed among employed women. These findings showed that reporting any symptom of anxiety (self-report) was associated with a 6 fold increased risk compared with those who reported no anxiety. This effect persisted after controlling for a wide range of other CHD risk factors. Findings such as these are particularly striking, considering selfreport measures are likely to capture sub-clinical symptomatology as well as more severe distress. This is of notable importance, considering the evidence showing anxious or depressed individuals experience multiple difficulties, even when they may not formally qualify for a clinical diagnosis (Kessler et al., 2003). One explanation for the evidence linking [phobic] anxiety with risk of CHD might be the influence of treatment drugs prescribed such as benzodiazepines, tricyclic antidepressants, and barbiturates (Thorogood et al., 1992). However, it is difficult to tease apart the risk conferred by drug treatments and the risk associated with underlying anxiety. For example, Kawachi et al (1994a) found similar sizes of effect of anxiety in their subgroup of drug free men compared with those who were on drug treatment on CHD risk, making the explanation of effects of drugs less likely. Another explanation for the association of anxiety and future CHD risk is that chronically anxious patients have low heart rate variability (HRV), with decreased capacity for heart rate change in response to stress (Miu et al., 2009). Diminished heart rate variability has been identified as a potent risk factor for sudden cardiac death in patients recovering from myocardial infarction (Bigger et al., 1992) and is a significant independent predictor of mortality in high risk groups (Makikallio et al., 2001a, 2001b). 25

26 Overall the evidence has largely demonstrated positive associations, albeit mainly in studies of male participants, and most effects have been independent of standard cardiovascular risk factors and demographic indices. Inconsistencies do however exist, probably due to varying sample sizes, differences in follow up periods, type of measures used (clinical interview vs. self-report measures), and adequacy of adjustment for confounders Anger and Hostility Anger and hostile feelings are strongly associated with each other; generally, anger is considered the emotional aspect of hostility. In turn, hostility is more representative of a more enduring disposition or personality style. Hostility is defined as a cynical, suspicious and resentful attitude towards others, often leading to negative social exchanges and more opportunities to experience anger. In contrast, not all individuals with high levels of anger can be characterized as hostile. Much of the early work in this area focused on the type A behaviour pattern. Type A is defined as an action-emotion complex that can be observed in any person who is aggressively involved in a chronic, incessant struggle to achieve more and more in less and less time, and if required to do so, against the opposing efforts of other things or persons (Friedman & Rosenman, 1959). In other words, type A personality includes elements of impatience, hard driving goal oriented behaviour, irritation and anger. Early studies supported an association between type A personality and risk of CHD. In one major longitudinal study (Rosenman et al., 1975) it was observed that individuals with type A behaviour were more than twice as likely to suffer CHD than those without type A characteristics. Another important study was the Framingham Heart Study (Haynes et al., 1980), where type A personality was found to predict future CHD among men with white-collar professions and in women working outside the home. Later work, however, did not support the early evidence for a link between type A behaviour pattern and 26

27 future CHD (Matthews & Haynes, 1986). Type A is now considered a weak and inconsistent predictor of CHD disease. Hostility is a component of Type A personality, and is considered the toxic component of Type A, with regards to CHD risk (Rozanski et al., 1999). Similar to findings with depression and anxiety, although there are fewer studies, both crosssectional and prospective studies reveal an association between anger/hostility and clinical indices of CHD. In cross-sectional studies, various indices of anger have been shown to correlate with CHD risk (e.g. Mittleman et al., 1995; Moller et al., 1999). Prospective studies provide robust evidence of an association. For example, an early study of initially disease free men, hostility predicted 10 year risk of acute MI and CHD mortality (Shekelle et al., 1983). Kawachi et al (1996) reported that compared with men reporting the lowest levels of anger, the RR among men reporting the highest levels of anger were 3.15 (95% CI: ) for total CHD (nonfatal MI plus fatal CHD) and 2.66 (95% CI: ) for combined incident coronary events including angina pectoris. This study demonstrated a dose-response relationship over a 7 year follow up period. It is interesting to note that levels of risk increased significantly for men who reported only two to four symptoms and dramatically for those reporting more than five symptoms. Williams et al (2000) studied 12,986 individuals [men and women] without known CHD at baseline and reported a strong graded relationship between increasing trait anger and subsequent MI and CHD mortality. A 1996 meta-analysis of 45 studies by Miller et al (1996) showed that chronic hostility is an independent risk factor for CHD as well as all-cause mortality, with the relationship being strongest among younger patients. A more recent meta-analysis also showed that hostility yielded a significant association with CHD (Myrtek, 2001). Although numerous studies have supported an association between hostility and CHD, controversy persists due to the rarity of large-scale prospective cohort studies of initially healthy populations. Surtees et al (2005) addressed this issue in a prospective investigation of the association between hostility and cardiovascular (and all-cause) 27

28 mortality among 20,550 men and women, years of age, participating in the European Prospective Investigation into Cancer and Nutrition in Norfolk (EPIC-Norfolk), United Kingdom study. These authors found that hostility was not associated with cardiovascular mortality, after adjustment for age and prevalent disease, in either men (RR= 1.09 for a 1 SD decrease in hostility score [representing increased hostility]; 95% CI: ) or in women (RR= 1.00; 95% CI: ). Subgroup analysis suggested hostility may be associated with cardiovascular mortality (independent of age, prevalent disease and cigarette smoking) for participants reporting very high hostility and for those aged less than 60 years. Overall, evidence from methodologically sound population-based studies suggests a role of anger and hostility in the increased risk of CHD in initially healthy populations. However, in common with the research on anxiety and CHD, the majority of studies have been of White males. Many reviews have been conducted of the association between anger/hostility and CHD, but findings have been disparate. Schulman and Stromberg (2007) compared seven meta-analyses, and showed that the diverse conclusions about the role of anger and hostility in CHD, is largely due to the varied study inclusion criteria. Chida and Steptoe (2009) conducted a review and metaanalysis of prospective cohort studies, addressing issues of methodological study quality, follow-up periods, participant characteristics, and whether studies used initially healthy participants or those with established CHD. These authors concluded that anger and hostility are significantly associated with development of CHD, as well as disease progression among those with existing CHD. In fact, they showed that the effect was marginally greater in studies of CHD populations compared with initially disease free populations. In addition, the association of anger and hostility with CHD was stronger among men then women. However, studies included in these analyses were observational in nature, and therefore causality cannot be established. When controlling for a broad range of behavioural co-variates (possible mediating pathways), 28

29 the effect of hostility was no longer significant in either studies of disease free individuals at baseline, or those with established CHD. 1.3 Psychological consequences of CHD Rates and prognostic implications of depression following CHD Having a heart attack is a distressing experience and the psychological consequences of an ACS may be profound and persistent. Many patients report feeling acutely upset and a proportion develops marked depressive symptoms. Major depressive disorder (MDD) develops in approximately 15% of cardiac patients (post MI and CABG), with a further 20% reporting either minor depression or elevated levels of depressive symptoms (Davidson et al., 2004; King, 1997; Lett et al., 2004; Rozanski et al., 1999). Depression is associated with significant impairment of functioning, which can at times exceed that of an individual s physical illness (Wells et al., 1989). The impact of depression on clinical recovery following MI has been extensively studied since Frasure-Smith and colleagues reported its prospective association with cardiac prognosis (Frasure-Smith et al., 1993). In this study 222 MI patients were assessed between 5 and 15 days following admission and were followed up 6 months later. At the 6 month stage, approximately 5% of the sample had died from cardiac causes. Depression measured at baseline was a significant predictor of mortality (Odds Ratio [OR]: 5.74, 95% CI: ). The impact of depression remained after control for left ventricular dysfunction and previous MI (OR: 4.29, 95% CI: ). Frasure- Smith et al argued that the impact of depression on mortality following MI is at least equivalent to that of left ventricular dysfunction and history of previous MI. The data linking depression and adverse outcomes among patients with established cardiac disease are particularly striking with a 1998 review noting that 11 of 11 studies reported worsened outcome (Glassman & Shapiro, 1998). 29

30 Depression appears to be common and persistent in MI patients with approximately one in three experiencing at least mild-to-moderate depressive symptoms during hospitalization (Thombs et al., 2006). Depression has now gained status as a risk factor alongside biomedical risk factors (Rumsfeld & Ho, 2005). Findings reported by Kaptein et al (2006) suggested a potential high-risk group of patients. This group of patients who had significant levels of depression during hospitalization for MI, and whose symptoms increased in the subsequent year, were at higher risk of recurrent cardiac events (Hazard Ratio [HR]: 2.5, 95% CI: ) compared with patients with no depressive symptoms. These patients were also more likely to report a history of previous depression and experienced more severe initial depressive symptoms at baseline. Lesperance and colleagues (2002) observed an increased risk of cardiac death in patients with Beck Depression Inventory (BDI) scores that started below the traditional cut-off point for identifying mild depression. Patients baseline depression was associated with long term cardiac survival. Improvement of depressed mood over time had little effect on prognosis in those patients with moderate to severe depression (BDI > 19) whereas improvement of symptoms in patients with mild to moderate depressed mood at baseline was associated with better cardiovascular prognosis. Although depression following an acute cardiac event is common it is important to bear in mind that approximately 50% of these patients will have had previous episodes of depressive symptoms or that the post-mi depressive symptoms are a continuation of pre-mi depression (Freedland et al., 1992; Spijkerman et al., 2005). One early report found that 40% of depressed MI patients with a history of depression died by 18 months post the event in comparison with only 10% of patients with a first time depression, while the group of patients with a history of depression but no depression during hospitalization had the lowest mortality (2.2%) (Lesperance et al., 1996). Other reports suggest that first time depression post MI increases the risk of mortality. Grace et al (2005) reported that patients with self-reported depressed mood 30

31 following ACS but no history of depression had a 1.78 fold increased risk of 5-year mortality compared with depressed ACS patients with a history of depression. Similarly, de Jonge and colleagues (2006) found that patients with first time depression following MI had an increased risk of new cardiovascular events (HR: 1.76, 95% CI: ) compared with non-depressed patients. Patients with recurrent post-mi depression were no more likely to experience recurrent cardiac events than were non-depressed patients (HR: 1.39, 95% CI: ). One recent study found no relationship between depression and increased cardiac mortality at all. Dickens et al (2007) measured depression before the MI and then 12 months later, and found that neither increased risk of cardiac death following MI. These data therefore suggest that depression in the weeks soon after MI onset may be particularly critical. This notion is further reinforced by data collected as part of this thesis. The importance of depressive symptoms observed in the early aftermath of ACS in relation to later adverse psychosocial outcomes is demonstrated in chapter 4 in this thesis. Although more recent systematic reviews have shown that depression following an ACS is associated with a 2-fold increased risk of cardiac and all-cause mortality in patients with established CHD (van Melle et al., 2004; Barth et al., 2004), the findings are not universally consistent (Lane et al., 2001; Mayou et al., 2000). However, the studies not to have found associations have typically been rather smaller scale than others, and may have been underpowered Anxiety Anxiety is often overlooked as a psychosocial risk factor in CHD. Much of the focus remains on the role of depression. However, accumulating evidence indicates that depression in CHD is often accompanied by symptoms of anxiety (Denollet et al., 2006c), and that anxiety predicts cardiac events in post MI patients over and above the effect of depression (Grace et al., 2004; Strik et al., 2003). Considering the frequent 31

32 co-occurrence of depression and anxiety, greater focus should be directed to investigating the influence of anxiety on prognosis in patients with recognized CHD. Anxiety symptoms were assessed soon after admission in the two samples of ACS patients I studied, and up to 3 years post the event. The importance of anxiety symptoms, and posttraumatic stress (an anxiety disorder) in relation to post ACS adjustment was assessed; these data are presented in chapters 4 and 6. Frasure-Smith and colleagues reported an increased risk of cardiac events after MI associated with anxiety (Frasure-Smith et al., 1995), in contrast to Jiang et al (2001) who used the same measure and found no relationship with mortality. From longitudinal investigations (3 5 year follow up), anxiety was associated with increased occurrence of adverse events post MI (Strik et al., 2003). A review by Januzzi et al (2000) highlighted the importance of studying anxiety in the context of CHD, as it was found to increase risk of all-cause mortality three-fold following MI. This review also found that anxiety almost doubled the risk of re-infarction at 5 years follow up, and increased the risk of sudden cardiac death by a factor of 6. Studies of prognosis following cardiac surgery have found anxiety to predict post-operative recovery after CABG sugery (Rothenhausler et al., 2005). However, among these studies, symptoms of anxiety assessed post-operatively have emerged as stronger predictors of adverse outcomes following cardiac surgery then symptom levels recorded pre-operatively. Oxlad et al (2006) reported that, following CABG, 6 month cardiac related hospital readmissions were predicted by pre-operative depression levels and post-operative anxiety, after controlling for a broad range of medical confounders. A more recent study by Szekely et al (2007) demonstrated that trait anxiety, as measured by the Spielberger State Trait Anxiety Inventory, was associated with increased mortality and cardiovascular morbidity following CABG and valve surgery. 180 patients who underwent cardiac surgery were followed up at 6, 12, 24, 36 and 48 months post discharge. 42% of the sample were classified as presenting clinically significant anxiety symptoms. Trait anxiety emerged as an independent predictor of post-discharge 32

33 cardiovascular events and 4 year mortality. This study further supports the evidence of stronger predictive value of post-operative anxiety scores than of pre-operative values. At 6 month follow up, trait anxiety scores were found to be more strongly associated with cardiovascular events than were values obtained pre-surgery. In this study, anxiety and depression were strongly correlated, however, only anxiety was significantly associated with increased mortality and morbidity Type D personality Type D personality refers to the joint tendency to experience negative emotions (negative affectivity) and to inhibit these emotions at the same time by avoiding negative reactions from others (social inhibition). Type D personality has been associated with a variety of adverse health outcomes in cardiac patients. These adversities include poor prognosis, heightened emotional distress, poor selfmanagement, pro-inflammatory cytokine activation, and disturbances in cortisol secretion in patients with CHD, heart failure, and heart transplantation (Denollet & Brutsaert, 1998; Whitehead et al., 2007). Type D has also been associated with a wide range of emotional distress, including anxiety, depression, and post-traumatic stress (Denollet et al., 2000; Pedersen & Denollet, 2003; Pedersen & Denollet, 2004; Pedersen & Denollet, 2006). Type D personality may be a pre-existing vulnerability factor for development of posttraumatic stress in response to ACS. Chapters 4, 6 and 7 in this thesis address the role of type D personality in the prediction of post ACS posttraumatic reactions. Denollet et al (1995) showed in a prospective study of 268 men and 38 women with established CHD, that Type D personality was associated with a six-fold increased risk of death from cardiac events two to five years post MI (in men). In a sample of patients undergoing cardiac rehabilitation, Type D personality emerged as an independent risk factor associated with a four fold increased risk of death from 33

34 cardiac causes (Denollet et al., 1996). A more recent study (Denollet et al., 2006b) followed up 337 MI patient after 5 years. Multivariate analyses showed that left ventricular ejection fraction <40%, not having coronary artery bypass surgery, and Type D personality were independent predictors of major adverse events (OR= 2.90, 95% CI: ), whereas psychological stress as measured by the General Health Questionnaire was marginally significant (OR= 2.01, 95% CI: ). These authors concluded that Type D reflects more than temporary changes in stress levels as it predicted cardiac events after controlling for concurrent symptoms of stress. The very core of Type D research is the notion that the general tendency to experience emotional and interpersonal difficulties may exacerbate progression of CHD, irrespective of depression and anxiety. Although some overlap may exist between depression and Type D personality in terms of negative affect, they clearly differ in the inclusion of social inhibition and their conceptualization as either a disorder (depression) or personality trait (Type D). Research has shown that the presence of only one of the tendencies (negative affectivity and social inhibition) has no effect in terms of cardiac prognosis, in fact it seems it is the interaction of the two that predict a significantly increased risk of adverse clinical events in patients with existing CHD (Denollet et al., 2006a). Recent evidence supports the predictive value of Type D personality after adjustment for depressive symptoms. Whitehead et al (2007) showed that Type D predicted cortisol dysregulation after controlling for depression. Denollet and Pedersen (2008) found Type D to be an independent [of depression] risk factor for major clinical events in cardiac patients. Findings from the Myocardial Infarction and Depression Intervention Trial (MIND-IT), demonstrated that depression was confounded by disease severity, whereas Type D personality was not (de Jonge et al., 2007). A number of pathways have been proposed to explain the association of Type D with adverse outcomes among those with established CHD. Whilst numerous studies have suggested a potential link, there is a lack of evidence of specific biological 34

35 pathways. And taken together, results from these studies suggest that Type D may be explained by different underlying biological pathways compared with more established psychological risk factors such as depression. An alternative explanation may be that Type D personality is associated with negative health behaviours. The tendency of social inhibition will lead to less social contact and availability of support, which may have a detrimental effect on health. There is some evidence that social inhibition and negative affectivity are in part due to genetic factors (Bouchard, Jr., 1994), and Type D personality may therefore be the behavioural manifestation of underlying biology, which would predispose an individual to cardiac outcomes associated with such underlying genetic causes. 1.4 Pathways between negative affect and CHD Anger, anxiety and depression are thought to play direct or indirect roles in the disease process. One line of argument for the apparently worse prognosis among cardiac patients that are depressed has been that these patients may just have experienced a more severe clinical event. However, numerous studies show that there is no reliable relationship between depression and any physiological index of disease severity (Carney, 2002). There are several plausible mechanisms that may explain the relationship between depression and prognosis in CHD patients. Some of these suggest an indirect link where negative emotion is predictive of, but not causally related to, morbidity and mortality in coronary heart disease (CHD). Other explanations imply a direct influence of negative emotions on course and outcomes of CHD. One general class of factors relates to adverse health behaviours. This is one proposed pathway for the increased morbidity and mortality after acute cardiac events. Individuals who are anxious, depressed or angry may engage in more adverse health behaviours, and these behaviours may contribute and exacerbate underlying cardiac disease. For example, smoking tends to be more common among angry, anxious or 35

36 depressed persons (Black et al., 1999). Depressed cardiac patients are also less adherent to cardiac medication regimens (DiMatteo et al., 2000), lifestyle risk factor interventions, cardiac rehabilitation programs (Ziegelstein et al., 2000) and are more sedentary (Kritz-Silverstein et al., 2001). Cardiac medications significantly reduce mortality in CHD patients and therefore non-adherence to prescribed medication can have greatly detrimental effects on the course and outcomes of CHD. There is also evidence that hostility and depression are associated with less social support and greater social isolation, as a result these individuals may lack an important stress buffer. Some negative affects are also associated with elevated status on traditional risk factors that result from adverse health behaviours and/or reflect environmental factors or genetic predispositions. For example, some studies show that depression, anxiety or anger increase risk of developing hypertension (Davidson et al., 2000; Jonas & Lando, 2000). Further, diabetes, which confers a three to four-fold increase in CHD risk is twice as prevalent in depressed individuals (Anderson et al., 2001) and more common in hostile persons (Niaura et al., 2002). In addition, individuals who score highly on cynical hostility also tend to be obese and have elevated low-density cholesterol (Weidner et al., 1987). Though many of the studies linking depression with increased CHD risk have included these many of these standard risk factors as covariates. However, a direct effect is unlikely, and multifactorial influences, mediating pathways and unexplained variance in the relationship between depression and CHD must be considered. A more direct pathway between depression and poor cardiac prognosis suggests that neurohormonal dysregulation is involved. Evidence of dysregulation of the autonomic nervous system (ANS) and of the HPA axis in medically well patients with major depressive disorder includes elevated plasma and urinary catecholamines and cortisol. Elevated cortisol levels can promote the development of atherosclerosis and accelerate injury of vascular endothelial cells. HPA dysregulation in depression 36

37 has been well documented (Broadley et al., 2006; Plotsky et al., 1998). Decreased parasympathetic and increased sympathetic nervous system activity predisposes CHD patients to myocardial ischemia, ventricular tachycardia, ventricular fibrillation, and sudden cardiac death. The ANS abnormalities associated with depression could therefore accelerate the progression of CHD and precipitate cardiac events by altering cardiac autonomic tone and promoting procoagulant and proinflammatory processes. Low heart rate variability (HRV) indicates excessive sympathetic and/or inadequate parasympathetic tone, and it is a powerful, independent predictor of mortality in patients with a recent MI or with stable CHD. There is growing evidence that HRV is reduced in depressed compared with medically comparable non-depressed patients following MI (Rottenberg, 2007). Lower HRV is a risk factor for cardiac arrhythmias and cardiac arrest (Curtis & O'Keefe, 2002). Some of the strongest epidemiologic results (Kawachi et al., 1994a) for anxiety are specifically with sudden cardiac death, which tends to be associated with cardiac arrhythmias. Depression is one way by which inflammatory processes can be affected and may also be partly responsible for maintaining inflammatory responses once they have been initiated. Depression may be involved in the maintenance of inflammatory responses by inhibiting the immune system s sensitivity to glucocorticoid hormones, which are responsible for terminating the inflammatory response (Carney, 2002). Numerous studies report higher circulating levels of inflammatory risk markers (e.g. IL- 6, C-reactive protein [CRP], TNR-α) of cardiac morbidity and mortality amongst medically health adults (Carney, 2002). A recent review by Howren and colleagues (2009) assessed the magnitude and direction of associations of depression with CRP, IL-1 and IL-6 in clinical and community samples. Articles published between 1967 and 2008 were systematically reviewed and results showed positive associations of CRP, IL-1, IL-6 and depression. This pattern was observed in both clinical and community settings, as well among studies using clinical interview or self-report measures. There was evidence of a dose-response relationship of these inflammatory markers and 37

38 depression, supporting the notion that cardiac risk conferred by depression is not limited to patient populations. However, the magnitude of effect was substantially greater in studies of clinical patients utilising clinical interview to evaluate depression. 1.5 Overlapping affective dispositions Anxiety, depression and anger correlate highly with each other in patients with established CHD (Denollet & Brutsaert, 1998). This potential construct and measurement overlap create not only ambiguity for theory, but also for interpretation of evidence (Suls & Bunde, 2005). Another difficulty arises from an all too common singlefactor approach. The similarities of results from reports discussed in this chapter suggests that negative emotions in general are related to CHD, however, there remains a tendency to focus on only one of these emotions at a time in this context. Findings from factor analytic studies suggest that anxiety, depression and anger are lower level constructs that all load with a broad dimension of negative affectivity (NA) (rs =.71 85; Costa & McCrae, 1992). NA is a broadband personality dimension that is conceptualized as a higher order construct that subsumes all of the negative emotions (Watson & Clarke, 1984). To date, few studies have used broad measures of NA to predict CHD, however, many studies report positive associations among anxiety, depression, anger and CHD. However, it is important to note that evidence of overlap does not imply that these emotions lack distinctive qualities. The issue of conceptual distinctiveness of psychological constructs was addressed in a sample of 822 healthy working men. Kudielka et al (2004) demonstrated conceptual distinctiveness of depression, vital exhaustion, and negative affectivity, with the factor structure of the original questionnaires maintained. A sample of 565 cardiac patients (ischemic heart disease and chronic heart failure) completed the Type D scale (DS14), HADS, BDI, and State Trait Anxiety Inventory (STAI). Pelle and colleagues (2009) identified two higher-order constructs; negative affect and social 38

39 inhibition. Factor analysis was performed of all measures on item level, and demonstrated distinctiveness of the constructs measured by the various questionnaires. However, only the original structure of the DS14 was confirmed, items on the HADS and BDI loaded more diffusely and did not tend to cluster together as originally proposed. In this study, the higher order construct negative affect comprised both state and trait facets, thereby highlighting the importance of assessing both chronic (i.e. lasting >2 years) and episodic components (i.e. lasting several months and up to 2 years), which might improve understanding of disease progression compared when focusing on these components separately. Taken together, these preliminary finding suggest that there is overlap between some, but not all psychological constructs. 1.6 Summary Overall there seems to be a growing body of research demonstrating that depression, anxiety, anger/hostility and Type D adversely affect either onset or course of CHD or both. Whereas depression seems to involve longer-term risk of adverse outcomes (such as acute MI or death), anxiety seems particularly associated with sudden cardiac death (and possibly acute MI). As discussed in the previous sections, numerous studies have concluded that depression and anxiety predict CHD morbidity and mortality, even after traditional CHD risk factors are controlled for. Studies of anger and hostility have yielded more mixed results, but prospective associations with CHD have been found in some. Posttraumatic stress disorder (PTSD) is linked closely with both anxiety and depression and has been hypothesized to be associated both with the development of CHD and as a consequence of acute cardiac events. Interestingly, findings in select samples are beginning to emerge that are consistent with studies on anxiety or depression and CHD (Kubzansky, 2009). This literature is reviewed in Chapter 2. 39

40 Research has identified multiple behavioural and biological pathways by which these psychological factors may contribute to the development and progression of CHD. However, this area of inquiry has elicited controversy. Disagreement stems partly from the inconsistencies in the literature, noted above. Findings from the ENRICHD trial have intensified controversy. This trial, involving cognitive-behavioural treatment of depression in post-mi patients, reduced depression but did not confer direct cardiac benefits in patients with existing disease (ENRICHD, 2003), suggesting that a fresh approach to understanding the pathways underlying associations with CHD is required. Overall the evidence for a connection between depression and subsequent CHD appear strong. Though the majority of studies support an association between depression and CHD progression or mortality, there are also several negative findings. One possible explanation for trends suggesting a declining effect of depression is that publication biases initially favour positive studies. Later, when a research area begins to mature,,failures to replicate earlier positive findings begin to appear. An alternative explanation is that treatment of CHD has developed greatly in recent decades, and post MI mortality has steadily declined. It is plausible that physiological or behavioural pathways may be weakened by advances in treatment, which were not available in the early days of this research. This explanation is strengthened by the findings that the effect of depression has apparently declined but not disappeared. Further, criticism has been directed at the inadequate adjustment for confounding risk factors in many studies; the diagnosis of depression could be confounded by the patient s medical condition; also, subclinical disease may have been present in some portion of nominally healthy population. As is the case for depression, the evidence supporting a role for anxiety in cardiac disease risk is more consistent in initially healthy samples than in patient populations. This might indicate that negative emotions play less of a role in CHD progression than in the development of CHD. However, a failure to differentiate between state or trait anxiety may be responsible for some of the weak and inconsistent effects observed in the literature. Findings from studies of anger and 40

41 hostility are mixed, and positive results have generally been found in samples of initially healthy participants. The results from patient samples are weaker and more inconsistent. Negative emotions are prevalent among patients who have experienced cardiac events. It is not only of importance to assess disease progression or fatal CHD as outcomes in this population. With an increasing number of ACS survivors, assessment of negative affect is of significant interest due to their association with poor adjustment and quality of life. This thesis attempts to address these issues by assessing recovery and emotional adjustment following ACS. 41

42 CHAPTER 2. Literature Review: Posttraumatic Stress Disorder 2.1 Introduction to Posttraumatic Stress Disorder Although the concept of posttraumatic stress disorder (PTSD) dates back more than a hundred years (recognised under terms such as shell shock, traumatic neurosis, rape-trauma syndrome etc) it was first introduced, and formally recognised, in the third edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III) in PTSD is an anxiety disorder that can develop following exposure to a traumatic event. For current DSM-IV (APA, 1994) classification, trauma must involve actual or threatened death or serious injury, or a threat to the physical integrity of self or others (criterion A1). In addition, trauma must elicit intense fear, helplessness or horror in the exposed individual during the event (criterion A2). There are three sets of symptom clusters related to PTSD. The first symptom cluster, re-experiencing the event (criterion B), may occur in several ways. The individual may have recurring nightmares about the event, may experience intrusive and recurring thoughts about the event, may feel as though the event is recurring ( flashbacks ), and/or may experience psychological and/or physical reactions to stimuli associated with the event. The second symptom cluster, avoidance of stimuli associated with the event and numbing of general responsiveness (criterion C), occurs when the individual avoids thoughts and feelings associated with the event as well as avoids other stimuli such as people, places, and activities associated with the event. The avoidance symptom cluster is also characterized by difficulty remembering important aspects of the event, diminished enjoyment in pleasurable activities, feelings of detachment, a restricted range of affect, and a sense of a foreshortened future. The last symptom cluster, increased arousal (criterion D), is indicated by difficulty staying or falling asleep, anger and irritability, hypervigilance, difficulty concentrating, and an exaggerated startle response. To qualify for a diagnosis of PTSD, in addition to fulfilling criterion A, individuals must also report 42

43 at least one of the re-experiencing symptoms (criterion B), at least three of the avoidance symptoms (criterion C) and at least two arousal symptoms (criterion D). Symptoms should remain for at least one month (criterion E), and must result in clinically significant distress or impairments in social, occupational or other important areas of functioning (criterion F). When a person experiences a traumatic event, it is common to experience some of these symptoms in the immediate aftermath as part of a normal reaction to severe stress. When these symptoms persist after two weeks, a diagnosis of Acute Stress Disorder (ASD) may be appropriate; however, when the duration of the symptoms persists for more than one month, a diagnosis of PTSD may be warranted (APA, 1994). The diagnostic criteria for PTSD, as defined by the DSM-IV, are provided in Table 2.1. However, the introduction of PTSD into the DSM has not gone unchallenged and many of the issues concerning the concept are widely debated. One of the major issues with the concept of PTSD concerns the expansion of the stressor criterion (A). Since its introduction into the DSM the definitions for the crucial factor, the traumatic event, have undergone significant change. In the early days the traumatic stressor was considered an extreme event outside normal human experience. In the latest version of the DSM the concept of the traumatic stressor has broadened to such an extent that the vast majority of [American] adults have been exposed to PTSD-qualifying events (McNally, 2004). Fulfilment of part one of criterion A is sufficient if individuals have been confronted with a traumatic event rather than directly experiencing it. This term is vague and merely hearing about something horrible could cause PTSD, bearing with it the potential for over-inflating the rate of PTSD and abuse of the syndrome for monetary reasons (malingering). McNally (McNally, 2003) refers to this as conceptual bracket creep. The second part of criterion A, which requires a persons response to include intense fear, helplessness, or horror, is flawed by the retrospective nature of such reports and the potential for current symptoms to influence memories of trauma reactions (Frueh et al., 2002). 43

44 A number of issues are also associated with the symptom criteria (B-D). In particular, the limited support for the three-factor model of the condition. A number of studies using factor analysis report best fit models ranging from 2 to 5 factors (Cordova et al., 2000; Simms et al., 2002). Yet another issue is that many of the defining symptoms of PTSD overlap with other well know disorders, in particular depression, specific phobia and other anxiety disorders (Spitzer et al., 2007). It is important to bear in mind that some of the symptoms of PTSD are also part of a non-pathological response to intense stressful experiences. Some research suggests that PTSD may not actually be a qualitatively distinct response to extreme stress but that it in fact reflects the upper end of a stress-response continuum (Ruscio et al., 2002). The implication of this argument for the purpose of my thesis is that it is important to investigate levels of posttraumatic symptoms in cardiac patients as a continuum, rather than simply classifying patients into those with and without PTSD. 2.2 Current understanding of PTSD Much of the work on PTSD to date has focused on the role of memory in the development and maintenance of posttraumatic stress symptoms during and after a traumatic event. A number of changes in memory functioning have been identified (Brewin & Holmes, 2003). PTSD is characterized both by enhanced recall of trauma related materials as well as amnesia for details of the traumatic event (Buckley et al., 2000). One distinctive memory feature of PTSD is flashbacks or the reliving of past trauma as if it were happening in the present. These intrusive memories are not 44

45 TABLE 2.1 DSM-IV DIAGNOSTIC CRITERIA FOR POSTTRAUMATIC STRESS DISORDER Criterion A (Stressor): The person has been exposed to a traumatic event in which both of the following have been present: (i) the person experienced, witnessed, or was confronted with an event or events that involved actual or threatened death or serious injury, or a threat to the physical integrity of self or others (ii) the person's response involved intense fear, helplessness, or horror. Note: In children, this may be expressed instead by disorganized or agitated behaviour Criterion B (Re-experiencing): The traumatic event is persistently re-experienced in one (or more) of the following ways: (i) recurrent and intrusive distressing recollections of the event, including images, thoughts, or perceptions. Note: In young children, repetitive play may occur in which themes or aspects of the trauma are expressed (ii) recurrent distressing dreams of the event. Note: In children, there may be frightening dreams without recognizable content (iii) acting or feeling as if the traumatic event were recurring (includes a sense of reliving the experience, illusions, hallucinations, and dissociative flashback episodes, including those that occur upon awakening or when intoxicated). Note: In young children, trauma-specific re-enactment may occur (iv) intense psychological distress at exposure to internal or external cues that symbolize or resemble an aspect of the traumatic event (v) physiological reactivity on exposure to internal or external cues that symbolize or resemble an aspect of the traumatic event Criterion C (Avoidance): Persistent avoidance of stimuli associated with the trauma and numbing of general responsiveness (not present before the trauma), as indicated by three (or more) of the following: (i) efforts to avoid thoughts, feelings, or conversations associated with the trauma (ii) efforts to avoid activities, places, or people that arouse recollections of the trauma (iii) inability to recall an important aspect of the trauma (iv) markedly diminished interest or participation in significant activities (v) feeling of detachment or estrangement from others (vi) restricted range of affect (e.g., unable to have loving feelings) (vii) sense of a foreshortened future (e.g., does not expect to have a career, marriage, children, or a normal life span) Criterion D (Arousal): Persistent symptoms of increased arousal (not present before the trauma), as indicated by two (or more) of the following: (i) difficulty falling or staying asleep (ii) irritability or outbursts of anger (iii) difficulty concentrating (iv) hypervigilance (v) exaggerated startle response Criterion E (Duration): Duration of the disturbance (symptoms in Criteria B, C, and D) is more than one month. Criterion F (Distress or Impairment): The disturbance causes clinically significant distress or impairment in social, occupational, or other important areas of functioning. 45

46 triggered by a deliberate search but involuntarily by specific reminders that in some way relate to the traumatic event (Brewin & Holmes, 2003). Another memory process relevant to the disorder is working memory capacity. Individuals with greater working memory capacity are better at suppressing unwanted thoughts (Brewin & Beaton, 2002; Brewin & Smart, 2005), and may be part of the explanation that low intelligence, which is strongly associated with working memory, is a risk factor for PTSD (Brewin et al., 2000). There has been some research undertaken aiming to establish the role of attention for risk of PTSD. Evidence shows an attentional bias early in processing of traumatic material (Bryant & Harvey, 1997). However, studies are scarce and results fairly inconsistent and do not show that the effects are unique to PTSD. Another psychological process linked with the increased risk of PTSD following trauma is dissociation. Dissociation has been defined as a kind of temporary interruption of, in our otherwise continuous, interrelated processes of perceiving the world around us, remembering the past, and our ability to link the past with our future (Spiegel & Cardena, 1991). Dissociation most commonly encountered in response to traumatic experience (peri-traumatic dissociation) includes states such as emotional numbing, depersonalization and out-of-body experiences. These reactions appear to be related to the severity of trauma, fear of death and feelings of helplessness (Holman & Silver, 1998; Reynolds & Brewin, 1998). A number of studies show dissociative responses to trauma to be strong predictors of later PTSD (Ehlers et al., 1998; Engelhard et al., 2003; Ursano et al., 1999). However, it is important to note that a number of studies show this is only the case for peri-traumatic dissociation, not for dissociative experiences that occur post trauma (Brewin et al., 1999; Harvey & Bryant, 1998; 1999). Emotions experienced at the time of trauma or as a post trauma response, such as intense fear, helplessness, horror, anger and mental defeat (defined as the perceived loss of all autonomy, a state of giving up ), are strongly associated with increased risk of PTSD, slowed recovery, and more persistent symptomatology (Andrews et al., 2000; 46

47 Ehlers et al., 2000). A central idea of PTSD is that traumatic events shatter people s basic beliefs and assumptions (Bolton & Hill, 1996; Horowitz, 1986). Post trauma, increased negative beliefs (about self, others, the world) are common among those who later go on to develop PTSD (Dunmore et al., 1999; Foa et al., 1999). Coping strategies adopted by trauma victims can influence recovery. Studies show that avoidance and thought suppression are related to slower recovery and greater symptom levels (Dunmore et al., 1999; Steil & Ehlers, 2000) and that attempts to suppress unwanted thoughts may in fact lead to them returning even more strongly (Wenzlaff & Wegner, 2000). A meta-analysis conducted by Brewin and colleagues (2000) found social support, alongside gender and trauma severity, to be the strongest risk factor for PTSD. Several studies show that a lack of positive social support is associated with PTSD, but the existence of a negative social environment appears to be an even greater indicator of PTSD symptomatology (Ullman & Filipas, 2001). It is clear that PTSD is associated with disturbances in a wide range of psychological processes (memory, beliefs, and emotions). The following section will outline the frameworks that drive current thinking about PTSD. The three main theoretical models of PTSD are described. 2.3 Models of PTSD PTSD risk factor research indicates that individuals experiences during traumatic events play a significant role in differentiating between those who subsequently go on to develop PTSD and those who do not (Brewin et al., 2000). The way in which an individual processes these experiences is a part of this subjective experience. Below, three current theoretical approaches to explaining PTSD are described. 47

48 2.3.1 Emotional processing theory One of the basic features of emotional processing theory (Foa et al., 1989; Foa & Kozak, 1986) is that PTSD is a form of pathological fear. Specifically, PTSD symptoms arise when information in the fear network is incompatible with pre-existing memory structures. This model posits that pathological fear responses occur and are maintained when there is an attempt to avoid or suppress the activation of the fear network. Further, depending on the severity of the stressor, the cognitive processes of attention and memory at the time of trauma may be disrupted, which may lead to formation of a disjointed or fragmented fear network that is difficult to integrate with existing memory structures. This model holds that resolution of the trauma occurs with activation of the fear network, which will enable modification of the memory structure by integrating information that is incompatible with the trauma so that new memories can be formed. More recent developments of the theory (Foa & Riggs, 1993; Foa & Rothbaum, 1998; Tolin & Foa, 2002) include elaboration on the relationship between PTSD and knowledge available prior, during and after the trauma. It is hypothesized that individuals who hold more rigid pre-trauma views will be more susceptible to PTSD. These views may be positive or negative, for example, views about the self as being extremely competent, and the world extremely safe, or the opposite, views about the self as being extremely incompetent, and the world extremely dangerous. These recent adaptations also place greater emphasis on negative appraisals of responses and behaviours which can lead to exacerbations of negative self views. It is suggested that these beliefs (present prior, during and post trauma) could interact and reinforce negative views about the self and contribute to chronicity of PTSD. This model fits well with the evidence showing successful reduction of PTSD following exposure therapy (Foa et al., 1999; Foa et al., 1991). Repeated exposure should lead to habituation of fear, allowing integration of new (safety) information 48

49 regarding the trauma into the trauma memory, and re-evaluation of negative beliefs or appraisals. As proposed by this model, trauma can cause disruption of cognitive processes such as memory and attention, leading to disjointed or fragmented fear structures. By reliving the trauma through exposure therapy it is possible to generate a more organized memory record that is easier to integrate into the rest of the memory system. Although this model has great explanatory power in terms of the effects of exposure treatment, and the strongly supported inclusion of importance of appraisal processes prior, during and post trauma, there are a number of issues with this approach. Firstly, there is no consistent evidence that repeated exposure will lead to changes in the structure of trauma memories, to the initial activation of fear, or habituation. Secondly, the model is based on the assumption of a fear network, thus it could be argued that reminders of the trauma would activate the entire fear memory (stimulus information, response information, and meaning). This, however, does not fit with the literature showing that many individuals with PTSD have memories of the traumatic event that fragmented, where certain parts are clear and others less so (Mechanic et al., 1998) Dual representation model In contrast to the emotional processing theory described above, dual representation theory suggests that trauma memories are represented in fundamentally distinctive ways from other types of memories. Brewin et al (1996) argue that there are two separate memory systems, operating in parallel, where one may take precedence over the other at different times. One of these systems, the verbally accessible memory (VAM) system, records only information which has been consciously attended to either prior to, during, or after the traumatic experience. This information receives enough conscious processing to be integrated with other autobiographical memories, 49

50 and can thereby be retrieved deliberately. Emotions accompanying these memories include both those that happened at the time of trauma (primary emotions) as well as those generated by retrospective appraisals of the event (secondary emotions). However, the information stored in this system is limited, as diversion of attention to the immediate source of threat, and the accompanying high levels of arousal during trauma greatly affect the volume of information that can be consciously registered. A second system is proposed. The situationally accessible memory (SAM) system contains information that has been obtained from lower level processing of the traumatic event, such as sights, sounds, bodily responses etc. Memories (i.e. flashbacks) stored in this system can be triggered by situational reminders of the trauma, these triggers can be either external or internal. The emotions that accompany SAM memories are restricted to primary emotions that were experienced during the trauma, and therefore flashbacks tend to be more detailed and emotion-laden than ordinary memories. This model of PTSD mainly focuses on the role of memory, emotions and appraisals, however there is little discussion of other important features of PTSD such as dissociation or emotional numbing. Further research is needed for the basic tenets of this theory to be fully supported Ehlers and Clarke s cognitive model Two mechanisms for pathological responses to trauma are identified in Ehlers and Clarke s cognitive model of PTSD (2000). These are the negative appraisals of the trauma or its consequences and the trauma memory itself. Patients with PTSD feel anxious about the future, even though the source of threat (the trauma) lies in the past. This apparent paradox is addressed in this model of PTSD. These authors argue that factors that increase the likelihood of negative appraisals are thought processes during the trauma and prior beliefs and experiences. There is a particular focus on one state of mind identified as mental defeat. This reaction to trauma, emphasizing the inability 50

51 of the person to influence their fate, is a risk factor for such appraisals as being weak, ineffective or unable to protect oneself. This model also attempts to account for traumatic memories by suggesting a number of peri-traumatic influences that affect the nature of trauma memories. Ehlers and Clarke s approach to explaining research findings of traumatic memory was to suggest that the memory of the traumatic event is poorly elaborated, not given a complete context in the first place, and inadequately integrated into autobiographical memory. Firstly, they distinguish between conceptual and data-driven processing of information encountered during trauma. Conceptual processing (focused on the meaning of the situation, organizing the information, and placing it in context), facilitates integration of the trauma memory into general autobiographical memory, whereas data-driven processing (focused on sensory impressions) leads to strong perceptual priming (a reduced perceptual threshold for trauma-related stimuli), accounting for re-experiencing in the present (absence of temporal context), and a memory that is difficult to intentionally retrieve (indicating absence of clearly specified retrieval routes). Secondly, inability to establish a self-referential perspective while experiencing the trauma, dissociation, emotional numbing, and a lack of cognitive capacity to evaluate aspects of the event clearly, are among the other peri-traumatic influences proposed. A number of maladaptive behavioural strategies post trauma are considered risk factors for persisting PTSD, such as active attempts at thought suppression, distraction, avoidance of trauma reminders, use of alcohol or medication to control anxiety, abandonment of normal activities, and adoption of safety behaviours to prevent or minimize trauma-related negative outcomes. Although this model of PTSD is considered the most detailed account of PTSD (Brewin & Holmes, 2003), there is a lack of consistent evidence in supporting the datadriven versus conceptual processing argument. However, the importance of mental 51

52 defeat, negative appraisals of initial PTSD symptoms and safety behaviours and avoidance have been strongly supported (Dunmore et al., 2001) Summary of current models of PTSD There is a great deal of overlap between these three theoretical accounts of PTSD incorporating factors affecting encoding of trauma information, alterations in memory functioning, appraisals of the trauma, coping strategies and cognitive styles etc. The main difference between these models lies in their accounts of how trauma impacts memory, the process whereby changes are brought about in memory, and how changes are related to recovery. One major difference between the emotional processing theory and the other two models are that in the latter two PTSD is accounted for by two distinct memory systems whereas the emotional processing theory relies on the idea of a single associative network in memory. Though, further work is needed, as effectiveness of treatments for PTSD is likely to increase from improved theoretical understanding of trauma, memory and PTSD. 2.4 PTSD following non-medical trauma The majority of people will experience at least one traumatic event in their lifetime. Data from large, representative community samples in the US, estimate lifetime prevalence of PTSD in the general population between approximately 7% (Kessler et al., 2005) and 15% (Breslau et al., 2005). Certain types of traumatic events are more likely to lead to PTSD. For example intentional acts of interpersonal violence, in particular sexual assault and combat, are more likely to result in posttraumatic stress than are accidents or natural disasters (Creamer et al., 2001; Kessler et al., 1995; Stein et al., 1997). Further, men are more likely to experience more traumatic events than women, but women tend to experience higher impact events and are also more likely to 52

53 develop PTSD in response to trauma (Stein et al., 1997). However, this gender difference in risk is not explained by differences in the type of traumatic event (Kessler et al., 1995). Although many people experience posttraumatic symptoms in the immediate aftermath of a traumatic event, prospective research suggests that rates decline rapidly over time. One study reported that following the terrorist attacks on the 11 th of September 2001 in New York, PTSD rates reduced from 7.5% one month post the event to 1.6% at four months and 0.6% at six months (Galea et al., 2003). For some, however, the disorder persists for a significant period of time (Kessler et al., 1995). It is important to note that not everyone that encounters a traumatic experience will go on to develop PTSD. Hence, exposure itself is not sufficient to explain the phenomenon and it is being increasingly recognised that some pre or post trauma individual variability factors have a role to play in understanding this condition. A growing literature on PTSD shows that demographic characteristics such as age, gender, ethnicity and socioeconomic status (SES) are associated with different rates of PTSD, with younger persons, females, Latinos (in the US), and low SES individuals being more likely to develop PTSD following a traumatic event (Norris et al., 2002). Interpersonal and psychological factors, such as social support and negative affect, have also been implicated in the onset and course of PTSD (Adams & Boscarino, 2005; Breslau et al., 2004). A meta-analysis by Brewin et al (2000) identified 14 risk factors for post-traumatic stress disorder including, trauma severity, lack of education, younger age, female sex, minority status, psychiatric history, low SES, other adverse childhood factors, other previous trauma, family psychiatric history, lack of social support, childhood abuse, life stress and low intelligence. However, closer inspection of the effect sizes reported in this study, suggest that the intensity of trauma and factors that follow the traumatic event (such as social support and further stressors) are the strongest predictors of PTSD, whereas pre-traumatic stressors have smaller individual effects (Shalev, 2001). Thus, once a traumatic event has occurred, 53

54 the major risk factors for PTSD appears to lie ahead, suggesting ample opportunity for secondary prevention of this disorder. However, it should be noted that there are considerable differences observed in effect sizes of risk factors between studies. A recent review by Wittchen and colleagues, highlight the considerable degree of variability, and report that risk factors, their interaction, and effect sizes vary by type of sample, cohort, and a number of other methodological factors (Wittchen et al., 2009). The current conceptualization of PTSD implicitly assumes a dose-response relationship (APA, 1994). That is, the more severe the event, or the greater the proximity, the more intense the posttraumatic stress. Some studies support this assumption. For example, studies of PTSD in combat exposed individuals (Goldberg et al., 1990; Vuksic-Mihaljevic et al., 2000) as well as studies of civilians living under combat conditions (Afana et al., 2002) support a dose-response theory of PTSD. However, there is great variability in the results from such studies and many find no evidence of greater PTSD with increased severity of trauma. For example a welldesigned Swiss study of severely injured motor vehicle accident victims found a onemonth PTSD rate of 4.7%, dropping to 1.9% at one year (Schnyder et al., 2001). One study of Turkish torture victims found that the majority did not develop PTSD (Basoglu & Paker, 1995). A number of studies show that previous experience of traumatic events is one of the strongest risk factors for PTSD in the community (Breslau et al., 1999), in war veterans (Bremner et al., 1993), in female rape victims (Yehuda et al., 1998) and individuals with PTSD following the events of September 11, 2001, in New York (Galea et al., 2003). Although the multiple-trauma data suggest that some individuals show sensitization, some studies also indicate that some people show adaptive responses to events (Corneil et al., 1999; Falsetti & Resick, 1995) with some data suggesting that a majority of trauma exposed adults respond to repeat trauma with greater confidence and enhanced coping skills (Aldwin et al., 1996). 54

55 There are relatively few studies investigating the long-term natural course of PTSD in the community. However, most of these suggest that in the majority of cases, PTSD typically runs a chronic and persistent course (Perkonigg et al., 2005; Owashi & Perkonigg, 2008). This is not to say that individuals continuously meet diagnostic criteria for PTSD, as variations in severity around the diagnostic threshold appear quite frequent. Community findings of predictors of chronicity suggest that occurrence of new traumatic events, higher rates of avoidant symptoms at onset, and higher rates of other anxiety or somatoform disorders are associated with persistence (Perkonigg et al., 2005). Using 10-year longitudinal data, Perkonigg et al (2005) found few spontaneous remissions, even after many years post trauma. Contrary to some other disaster studies, where post-disaster PTSD typically decreases over time, a recent study of survivors of the hurricane Katrina in the US (Kessler et al., 2008) observed increased rates of PTSD from baseline (5 8 months after the traumatic event) to 1 year later (14.9% to 20.9%). More recently studies have begun investigating PTSD as a consequence of medical illness. In the DSM-IV, being diagnosed with a life threatening illness was added as an example of traumatic stress (APA, 1994). Although PTSD has been recorded most commonly as a consequence of trauma in an external environment, some physical illnesses and particularly those of an acute nature (such as acute coronary syndrome) can occur just as suddenly and unexpectedly as other traumatic stressors. This literature is reviewed in the following sections. 2.5 Medical events as traumatic stressors Although most commonly diagnosed in victims of war, natural disasters or assault, there is increasing evidence of PTSD in individuals after onset, diagnosis, or treatment for physical illness. After the inclusion of being diagnosed with a lifethreatening illness in DSM-IV the question has arisen whether the experience of 55

56 severe physical illness, essentially an event internal to the person, satisfies the traumatic stress criterion (A) for PTSD. It seems, however, that medical stressors are not dissimilar from other traumatic stressors, in that they are likely also to convey lifethreat. Medical diagnoses, like other traumatic events, are also precipitants of extreme fear, helplessness or horror. PTSD may develop as a result of direct threat, such as diagnosis of, or treatment for serious illness, or more indirectly as a function of witnessing or care giving for individuals with serious illness (Tedstone & Tarrier, 2003). So what distinguishes medical stressors from other more traditionally viewed traumatic stressors? One of the major differences is the relative prevalence of PTSD. In general, the likelihood of PTSD is lower among medical patients, than other trauma victims. For example, the rate of PTSD as a consequence of myocardial infarction ranges between 0% and 32% (see table 2.2), PTSD as a result of cancer ranges between 0% and 32% (Kangas et al., 2002), compared with 35 47% in studies of rape or battery (Rothbaum et al., 1992; Riggs et al., 1992; Resnick et al., 1992). These lower observed rates have a number of plausible explanations. Lower rates of PTSD among medically ill patients reflect general findings that development of psychopathology in medical populations is also relatively low (Mundy & Baum, 2004). Differences in methodology may also account for these lower rates. Limits on the severity of disease and stages of disease, or at which time point assessments are made, may have an impact on rates. For example, many studies of PTSD following cancer have used early stage breast cancer patients, who have a relatively good prognosis, which may in fact artificially limit the likelihood of experiencing psychological trauma. There are also distinct differences in symptomatology. Patterns of falling and rising posttraumatic symptom experience are not uncommon following medical trauma, whereas in studies of non-medical stressors, distress tends to be greatest in the immediate aftermath of the event and decrease (often rapidly) over time (Rothbaum et al., 1992; Kessler et al., 1995). 56

57 Possibly the most significant difference between many medical stressors and what is considered more traditional traumas is the focus of threat in time. Whereas traditional trauma can be considered a discrete or acute event, occurring in the past, medical stressors not only share this characteristic but there is an added dimension containing future-oriented aspects of the threat, such as fears about treatment, survival, recurrence and persistence of life-threat (Compas et al., 2002; Kangas et al., 2002). One implication of this is the effect it may have on PTSD symptomatology, in particular regarding intrusive thoughts. For medical patients, intrusions may not only consist of past events, but may also include future-oriented aspects of the trauma. This possibility must be taken into account when assessing intrusions following medical trauma. For this study population, it may be more appropriate to ask about the specific nature or content of intrusions, rather than simply assessing existence of intrusive thoughts. The appropriateness of adopting PTSD as a model explaining distress related to medical trauma must therefore be questioned. 2.6 PTSD following medical trauma Tedstone and Tarrier (2003) reviewed the prevalence and predictors associated with posttraumatic symptoms in a number of medical conditions (myocardial infarction, cardiac surgery, haemorrhage and stroke, childbirth, miscarriage, abortion and gynaecological procedures, intensive care treatment and human immunodeficiency virus (HIV) infection) and found that the highest prevalence rates were identified in patients treated, in intensive care units, for life-threatening events (14 59%) and those with HIV infection (30 35%). The lowest rates where found in studies of PTSD post-childbirth ( %). A number of factors were identified across studies which may predispose individuals to the development of PTSD. Patient characteristics, including personality factors, previous life adversity, and previous mental health problems were associated with increased risk of PTSD. Age as a risk factor was 57

58 supported in some studies but not in others. Aspects of the trauma itself, such as poor partner/confidant support during or after the traumatic event and negative perceptions of medical staff were also implicated as risk factors for later PTSD. Interestingly, the majority of studies report no relationship between objective severity of illness and later PTSD. However, one study found more severe pre-operative cardiac conditions to be a risk factor (Stoll et al., 2000). Also, some studies have found that the severity of medical intervention is associated with increased risk of PTSD (Creedy et al., 2000; Menage, 1993). The studies included in Tedstone and Tarrier s review (2003), which measured PTSD at two time points, all found a reduction in the number of cases with PTSD over time. This has also been found in epidemiological studies (Kessler et al., 1995), where PTSD cases have significantly been reduced over time, in particular by 12 months post trauma. Thus the timing of symptom assessment is of crucial importance to understand the nature of PTSD. There has also been a greater focus in recent years on the issue of PTSD following cancer. Kangas et al (2002) reviewed the literature on PTSD as a consequence of cancer and found prevalence rates (13 studies, 10 of which studied breast cancer, all but one were cross-sectional) between 0% and 32%. A further 21 studies were included in this review, these focused only on the prevalence of intrusion and avoidance symptoms. Across studies patients reported high rates of both intrusion (16-43%) and avoidance (15-80%) symptoms at least within the first month following cancer diagnosis. Numerous studies have investigated the predictors of PTSD following cancer. Many predictors of PTSD from the general trauma literature have also been found to predict PTSD in cancer patients, including female gender (Hampton & Frombach, 2000; Nordin & Glimelius, 1998), younger age at diagnosis (Andrykowski et al., 2000; Cordova et al., 2000; Green et al., 2000), lower SES (Cordova et al., 1995), and lower education (Cordova et al., 1995; Jacobsen et al., 1998). Other risk factors associated with PTSD responses following cancer diagnosis are prior negative life stressors (Andrykowski & Cordova, 1998; Mundy et al., 2000), a history of 58

59 psychological disturbance (Green et al., 2000; Mundy et al., 2000), elevated psychological distress subsequent to the diagnosis (Epping-Jordan et al., 1999; Jacobsen et al., 1998; Smith et al., 1999), avoidant coping style (Nordin & Glimelius, 1998), poor social support (Andrykowski & Cordova, 1998; Butler et al., 1999; Green et al., 2000), poor social functioning (Kelly et al., 1995; Smith et al., 1999), and reduced physical functioning (Jacobsen et al., 1998; Smith et al., 1999). There is mixed evidence on the predictive value of medical variables such as type, severity, stage and prognosis of cancer. Whereas some studies have found no relationship between such variables (Alter et al., 1996; Cordova et al., 1995; Green et al., 1998), other have found more advanced stages (Andrykowski & Cordova, 1998; Epping-Jordan et al., 1999; Jacobsen et al., 1998), the recency of treatment for cancer at PTSD assessment (Andrykowski & Cordova, 1998; Kornblith et al., 1992), and patients experiencing at least one cancer recurrence (Butler et al., 1999; Cella et al., 1990) to be associated with increased severity of PTSD symptoms post cancer diagnosis. There are however a number of major methodological issues that must be taken into account when interpreting findings from these studies. Firstly, the majority of studies have been cross-sectional in nature. Retrospective accounts of symptoms may be strongly influenced by current symptomatology, and cross-sectional designs does not allow researchers to delineate the relative contribution of diagnosis, treatment, side effects or changing prognosis of cancer on PTSD symptomatology. Another major issue is the reliance on self-report measures, and the wide variety of measures used across studies makes results difficult to generalise. There is also a marked variability in the samples used across studies in terms of type and stage of cancer. The overemphasis on using female patients is also problematic considering the strong evidence that females are more likely to develop PTSD then males. 59

60 2.7 PTSD as a consequence of Acute Coronary Syndrome ACS, like other non-medical trauma, is life-threatening, sudden and often unexpected. Many patients report an intense fear of dying (Whitehead et al., 2005), and emotional distress such as anxiety and depression during the acute phase and in the immediate aftermath is common. Although most patients will fully recover from this emotional distress, many patients do not recover and distress can persist for a significant period of time. The persistent and severe psychological distress experienced by some patients may actually satisfy criteria for a diagnosis of PTSD Prevalence of PTSD following ACS Table 2.2 summarizes the prevalence levels of PTSD that have been observed in patients following ACS (mostly acute Myocardial Infarction - MI). As can be seen from the table, the prevalence of PTSD ranges from 0 and 32% in these studies. In their review, Spindler and Pedersen (2005) put the average rate at 15%. There may be several reasons for this variability, one is the measurement tool. As can be seen in Table 2.2, a wide variety of questionnaires have been used, including the Impact of Events scale (IES), the Posttraumatic Diagnostic Scale (PDS) and the Posttraumatic Stress Disorder Symptom Scale (PSS). The criteria derived from these different instruments vary, so prevalence may not be uniform. The IES in particular is likely to elevate estimated prevalence rates as it only assesses avoidance and intrusion symptoms. Second, some studies have used a prospective design while others have been cross-sectional. Subject selection and loss to follow up is a major cause of potential bias in prospective research. Cross-sectional studies have the disadvantage of selection processes operating, and it may also be that patients retrospectively attribute posttraumatic symptoms to the cardiac event that have other origins. Although cross-sectional methods are the most cost-effective and efficient way of assessing 60

61 prevalence, in such selected samples, no one individual may actually have the disorder [under investigation], thus results may not reflect true prevalence rates. Third, the interval between ACS and measurement of PTSD has varied widely, this impairs direct comparison of results. Most prospective studies have ranged from 3 to 9 months, with only one (van Driel & Op den Velde, 1995) lasting more than 12 months. There is no clear relationship between the duration of studies and the prevalence of PTSD. On the other hand, studies that have taken measurements at more than one time point have typically shown some diminution in the prevalence of PTSD (Pedersen et al., 2003; Pedersen et al., 2004; Sheldrick et al., 2006). It is therefore not very clear whether PTSD persists in the long run, or diminishes over time. It is also very striking from table 2.2 that sample sizes have been relatively small. The mean sample size is 79, and only 7 studies have involved more than 100 patients. These limitations in the literature stimulated the work undertaken in this thesis, and data on the longer-term prevalence in presented in chapter 4. 61

62 TABLE 2.2 PREVALENCE OF PTSD FOLLOWING ACS Reference Sample size Design Kutz et al. (1994) 100 (88 male, 12 female) Retrospective Doerfler et al. (1994) 27 male Cross-sectional PTSD measure* PTSD Inventory IES and others Time since ACS 6 to 18 months Prevalence 16% chronic, 9% acute 6 to 18 months 8% Van Driel et al. (1995) 23 (14 male, 9 female) Prospective SCID 22 to 26 months 0% Bennett et al. (1999) Bennett et al. (2001) Shemesh et al. (2001) Ginzburg et al. (2002) Pedersen et al. (2003; 2004) O Reilly et al. (2004) 44 (30 male, 14 female) MI patients 70 (52 male, 18 female) MI patients 102 (81 male, 21 female) MI patients 116 (94 male, 22 female) MI patients; 72 (51 male, 21 female) healthy matched controls 112 (79 male, 33 female) MI patients; 115 (72 male, 43 female) matched controls; 9 months 102 MI patients 27 (24 male, 3 female); 27 Sudden cardiac arrest patients Cross-sectional PDS 6 to 12 months 10.75% Prospective PDS 3 months 3% Prospective IES 6 months 9.8% Prospective Prospective Case-control PTSD Inventory PDS PDS, SCID, IES <1 week; 7 months 4 to 6 weeks; 9 months 16% at 7 months T 1 24 %; T2 14% 3 to 18 months 7% (SCID) Shemesh et al. (2004) 65 MI patients Prospective IES 6 months 20% Doerfler et al. (2005) Sheldrick et al. (2006) Whitehead et al. (2006) 52 (36 male, 16 female) MI patients 17 MI patients; 27 Subarachnoid Haemorrhage patients Cross-sectional PSS 3 to 6 months 7.7% Prospective DTS <2 weeks; 5 to 7 weeks; 11 to 14 weeks T1 13.3%; T2 23.8%; T3 11.8% 135 (99 male, 36 female) Prospective PSS 3 months 14.8% Jones et al. (2007) Chung et al (2008) 111 ( 81 male, 23 female) MI patients 120 MI patients (94 male, 26 female) Cross-sectional Cross-sectional PDS PDS <5 years to >10 years Average time since first MI: 9.92 years Rocha et al. (2008) 31 MI patients Prospective IES, SCID 1 to 2 months Wiedemar et al. (2008) 190 MI patients Prospective CAPS Guler et al. (2009) 394 MI patients Cross-sectional CAPS Average 95 days post MI Screening Q sent on average 98 days post MI 32% 31% 4% (SCID), 16% above threshold on IES 9.4% 10.2% *CAPS Clinician Administered PTSD scale; IES Impact of Events Scale; SCID The Structural Clinical Interview; PDS Posttraumatic Stress Diagnostic Scale; DTS The Davidson Trauma Scale; PSS Posttraumatic Stress Disorder Symptom Scale. 62

63 Studies investigating symptom levels within the subscales of PTSD have found more prevalent or higher scores for avoidance symptoms than for intrusions (Bennett et al., 2001; Bennett & Brooke, 1999; Shemesh et al., 2001). One explanation for higher avoidance symptom scores may be that post-acs patients avoid activities such as physical exertion or avoid exposure to stress for fear of provoking recurrent cardiac symptoms. Bennett et al (2001) also found that women were more likely than men to avoid reminders of their MI. Prevalence rates of PTSD following cardiac surgery (Coronary Artery Bypass Grafting CABG) are similar to those observed following MI, and range between 10.8% and 18% (Tedstone & Tarrier, 2003), however, investigations of the PTSD subscales in particular show that intrusive symptoms are more prevalent than are avoidance symptoms (e.g. Doerfler et al., 1994). This is an interesting finding, considering that the research with MI patients suggests that avoidance symptoms may be more common in cardiac population Predictors of posttraumatic stress symptoms following ACS As discussed in previous sections, several factors predictive of PTSD have been identified from studies of natural disasters, victims of war and interpersonal violence. There are an increasing number of studies aiming to identify the factors that are predictive of posttraumatic stress symptoms following an acute cardiac event. Although many risk factors have been associated with the development of PTSD following trauma, several of these appear not to be associated with the disorder in cardiac populations, suggesting that PTSD may have distinct correlates in this group of patients. The findings from these studies are discussed below and a summary of studies is provided in table 2.3. In contrast to findings suggesting a dose-response relationship between trauma and PTSD, emotional distress following MI appears not to be related to the severity of the infarction. However, there are some data suggesting that patients who experience 63

64 multiple cardiac related events, such as multiple hospitalizations, cardiac arrest, or repeat MI, are at increased risk of developing posttraumatic stress (Doerfler et al., 2005). Accumulating research now suggests that the subjective perception of the MI and its perceived impact on future activities and longevity are important factors determining distress (van Driel & Op den Velde, 1995). In addition, emotional reactions to acute cardiac events appear to be important in the development of posttraumatic stress symptoms. Whitehead et al (2006) found that the most robust predictor of posttraumatic symptoms 3 months post ACS was emotional state at the time of admission. In this study, 135 ACS patients completed psychological measures 7-10 days post admission and again at 3 months. 14.8% of patients could be identified with PTSD at 3 months. No demographic variables were predictive of PTSD, and whereas the subjective rating of severity as indexed by degree of chest pain was predictive of posttraumatic symptoms severity, objective clinical severity was unrelated to posttraumatic symptoms. Early (in-hospital) emotional responses were strongly predictive of posttraumatic stress symptoms. Acute stress symptoms, depressed mood, negative affect and hostility were independent predictors of symptom severity at 3 months. A recent pilot study by Rocha and colleagues (Rocha et al., 2008) showed that higher scores of posttraumatic symptoms, 2 months following MI, were associated with depressive symptoms and self-reported anxiety at baseline. In this study, prevalence of PTSD was low, 4%. However, when including subsyndromal cases this figure rose to 16%. Further support for the importance of subjective perceptions of MI in the prediction of later PTSD, was demonstrated recently by Wiedemar et al (2008). In this study, 190 MI patients were assessed for PTSD approximately 3 months following the index event. There was no association between demographic variables or left ventricular ejection fraction (LVEF a proxy measure of objective MI severity) and later posttraumatic stress. Helplessness and greater subjective pain intensity emerged as independent predictors of posttraumatic stress symptoms in multivariate analyses. 64

65 However, this study did not control for depression. This study also assessed the value of fulfilling the A2 criterion for a DSM-IV diagnosis of PTSD. Most research to date has not assessed subsyndromal symptoms of PTSD. This is now recognised as a specific nosologic subcategory of PTSD (Mylle & Maes, 2004). Including only patients who reach a certain threshold of symptoms for a diagnosis of PTSD can be misleading, as studies show that subsyndromal PTSD also endorse marked levels of psychological distress and low functional status (Grubaugh et al., 2005). This study found no difference between those who fulfilled the A2 criterion (i.e. negative emotional response to MI) and those who did not, on the prevalence of full PTSD. There was, however, a small decrease in prevalence of subsyndromal symptoms when criterion A2 was applied. These results suggest that those who did not respond to the MI with intense fear, helplessness or horror, were less likely to develop PTSD (subsyndromal level). These findings are in line with previous results from a large community-based study showing that traumatic events not involving symptoms of the A2 criterion rarely result in PTSD (Breslau & Kessler, 2001). Guler et al (2009) extended the study reported by Wiedermar et al (2008). PTSD was assessed in 394 patients, the prevalence remained largely the same, 10%, using a structured clinical interview. Patients with clinical PTSD were younger and reported greater fear of dying and more intense helplessness during the MI, supporting the notion that subjective experience of MI is a stronger predictor of PTSD than is objective measures of MI severity. These authors suggest that there is little evidence for the assumption that certain risk factors identified in the literature (demographic, clinical, psychological) can differentiate clinical PTSD status from elevated PTSD symptom level (i.e. subsyndromal levels). However, this might be expected since posttraumatic stress occurs on a continuum of severity (Whitehead et al., 2006). 65

66 TABLE 2.3 RISK FACTORS FOR PTSD FOLLOWING ACS Risk factor Positive Association No Association Negative Association MI severity (objective) Whitehead et al (2006); Ginzburg et al (2002) Ginzburg (2006a); Pedersen et al (2003); Kutz et al (1994); Doerfler et al (2005) MI severity (i.e. subjective pain)/ Subjective perception of threat/ Importance attached to the event Duration of MI/ Pain duration Prior MI/cardiac hospitalization Times re-hospitalized (any reason) Awareness of event as MI Dissociation during event/acute stress disorder Whitehead et al (2006); Ginzburg et al(2002); Bennett et al (2001); Wiedemar et al (2008); Guler et al (2009) fear of dying/helplessness Doerfler et al (2005) Kutz et al (1994) Doerfler et al (2005) Van Driel & Op den Welde (1995); Bennett & Brooke (1999) Bennett et al (2002); Ginzburg (2006a); Whitehead et al (2006) Doerfler et al (2005), Guler et al (2009) subjective pain Fear at time of MI Bennett & Brooke (1999); Bennett et al (2001); van Driel & Op den Welde (1995) Doerfler et al (2005) Subjective expectation of incapacitation Previous PTSD Pre-MI stressful life events Post-MI stressful life events Kutz et al (1994) Kutz et al (1994); van Driel & Op den Welde (1995) Ginzburg (2006a) Ginzburg (2006a) Age Bennett et al (2001); Whitehead et al (2006) Ethnic origin Kutz et al (1994) Asian or Mediterranean; Rocha et al (2008) African- American Bennett & Brooke (1999); Rocha et al (2008) History of depression Whitehead et al (2006) Negative affect in hospital/depressed mood Bennett et al (2001); Pedersen et al (2003); Whitehead et al (2006); Rocha et al (2008) Bennett et al (1999) Hostility Whitehead et al (2006) Optimism Whitehead et al (2006) 66

67 Risk factor Positive Association No Association Negative Association Neuroticism Pedersen et al (2003) Anxiety Pedersen et al (2003); Rocha et al (2008) Whitehead et al (2006) Alexithymia Bennett & Brooke (1999) Bennett et al (2002) Type D personality/neuroticism Social support/ social network Bennett & Brooke (1999); Bennett et al (2001); Whitehead et al (2006); Pedersen & Denollet (2004) Whitehead et al (2006) Bennett et al (2002); Pedersen et al (2004) Perceived control Doerfler et al (2005) Non-repressive coping Ginzburg et al (2002) There are some inconsistencies in the literature on risk factors for PTSD in MI patients, in particular regarding the relation between subjective perceptions and PTSD. For example, Doerfler and colleagues (2005) studied the psychological variables related to PTSD in MI patients 3 to 6 months post the event. The sample included 52 MI patients (36 male, 16 female). PTSD was measured with the Impact of Events Scale and the PTSD symptom scale. Measures of quality of life, perceived control, stressful events at the time of the MI, cardiac history, subjective pain, degree of danger, fear of dying and the degree of predictability of the MI were obtained 3 to 6 months post admission. In this study between 4% and 8% of patients qualified for a diagnosis of PTSD depending on cut-off. Elevated scores on the PTSD measures were associated with poorer quality of life. Factors associated with PTSD at 3 to 6 months post the event included readmissions to hospital, longer lasting MI and ratings of pain duration. Patients reporting lower ability to control their emotions during the acute event and any future MI, as well as perceived lower controllability over aversive events more generally were associated with higher PTSD scores. However, cross-sectional studies such as this have limited ability to determine the direction of the relationships. It is unclear whether perceptions of control lead to the development of PTSD or whether these 67

68 perceptions are produced by psychological distress. In contrast to other research, this study found that subjective pain was not related to PTSD nor were ratings of perceived danger, predictability and fear of dying. A recent study by Guler et al (2009) also report no association between subjective pain scores and later posttraumatic stress symptoms. A study by Bennett and Brooke (1999) found that awareness of having an MI during the acute phase was strongly predictive of posttraumatic stress 6 12 months post admission and in particular the level of intrusive symptoms. In a second, longitudinal study, Bennett et al (2001) found that low mood while in hospital and the fright experienced at the time of the MI were predictive of the frequency of the symptoms of PTSD three months following the event. Some research suggests that personality variables may be important in the development of posttraumatic stress following MI. There is relatively little research in this area and so far the main focus has been on two factors in the context of MI patients. The first, alexithymia, is a stable personality trait implicated as a risk factor for the development of PTSD in general (Krystal et al., 1986) and PTSD in MI patients in particular (Bennett & Brooke, 1999). Alexithymia is characterized by an inability to process emotions, and it has been postulated that this may result in failure to integrate memories and their emotional associations into more general memory systems (Brewin et al., 1996). Although some cross-sectional data suggest an association between alexithymia and PTSD (Bennett & Brooke, 1999), longitudinal studies have not supported this relationship (Bennett et al., 2002). The second personality factor associated with PTSD post MI is negative affectivity which reflects an individuals propensity to experience negative moods and interpret events in a negative way, even without the presence of an obvious stressor (Watson & Clarke, 1984). A number of studies support the role of negative affect in the development of PTSD. Low positive and high negative affect is predictive of PTSD (Bennett & Brooke, 1999; Bennett et al., 2001). Type D personality trait (i.e. negative affectivity and social inhibition) is another factor which has been found to relate to posttraumatic symptoms. 68

69 The negative affectivity component of Type D, conceptualized as neuroticism, has been shown to be associated with post-mi PTSD (Pedersen & Denollet, 2004; Whitehead et al., 2006). A recent study showed higher neuroticism among patients with full-ptsd compared with patients with no or partial PTSD (Chung et al., 2007). These findings support the proposed link between Type D and post-mi PTSD. This is because the negative affectivity dimension of Type D constitutes partly the characteristics of neuroticism. Some argue that the trait of high negative emotionality or neuroticism is the primary personality risk factor for developing full PTSD (Miller, 2003). One line of argument suggests that a particular type of coping style may have a protective function on PTSD development following MI due to its effect on patients appraisals of the stressful event, their own self-esteem and belief in ability to cope. Ginzburg et al (2002) investigated the relationship between repressive coping and PTSD. This study showed that only 7.1% of repressors 7 months post MI were identified as having PTSD compared with between 17.2% and 20% of non-repressors. These authors argue that repressive coping may serve as a stress buffer. Repressive coping is defined as the cognitive and emotional effort to ignore or divert attention from threatening stimuli, whether internal or external. There has been little research on the role of repression on PTSD in MI patients. One study of the related concept of avoidance shows that such strategies are effective in the short term following an MI (Esteve et al., 1992). The efficacy of avoidance strategies in the longer run is less clear. Some studies show that avoidance is related to increased posttraumatic stress symptoms after various traumatic events (Amir, 1997; Reynolds & Brewin, 1998). However, due to the cross-sectional nature of these studies it is not clear whether avoidant coping is a risk factor for PTSD or a manifestation of the disorder. It is clear from the literature reviewed above that there is great variability in studies of risk factors for PTSD following MI and that the risk factors identified do not seem to overlap greatly with those identified in the general population. Although age, gender, and socioeconomic status have generally been identified as risk factors for 69

70 PTSD following trauma, the majority of the post-mi PTSD literature reports no such associations. However, lack of social support and negative affect in the wake of trauma do seem to be important factors for developing posttraumatic stress overall. One of the core assumptions of the PTSD construct is the idea of a dose-response relationship between trauma and subsequent symptoms. Although there has been some support for this argument in the literature on PTSD from various causes, there is an overwhelming lack of support for such a relationship among post-mi PTSD patients. Due to the lack of agreement of risk factors between studies of PTSD in MI patients and of individuals exposed to other types of trauma, it is difficult to make inferences from the broad PTSD literature, thus, it is important to further explore the potential risk factors for PTSD in cardiac populations. Data on predictors of posttraumatic stress symptoms are presented in chapters 4, 6 and 7 in this thesis. The salience of emotional and psychological predictor variables for posttraumatic stress in response to acute cardiac events is highlighted Distinctive features of ACS-related PTSD The core constructs of PTSD include the traumatic stressor, re-experiencing symptoms, avoidance symptoms and arousal symptoms. These may be potentially distinctive among ACS patients. Firstly, the experience of an acute coronary syndrome represents a distinctive stressor within the PTSD framework because it involves a potentially chronic and debilitating illness that may be accompanied by a range of aversive associated events (e.g. angina). The stressor causing PTSD is distinctive also because it is triggered by an internally induced event rather than an external source of threat. The threat associated with the stressor is not only immediate but the outcome is also future oriented. This is problematic in terms of the DSM-IV s description of reexperiencing symptoms, which implies that the intrusive thoughts and associated affect pertain to an event that has occurred in the past (APA, 1994). Many intrusive thoughts 70

71 reported by ACS patients appear to be future oriented fears about one s health. A number of the avoidance symptoms in the DSM-IV definition of PTSD may also pose a problem. Avoiding reminders of the stressor may be difficult because many of the cues are either internally (e.g. symptoms such as shortened breath, fatigue, and angina) or externally imposed (e.g. medical visits, rehabilitation attendance, and taking prescribed medication). Arousal symptoms may also be problematic because they can index somatic responses that can overlap with effects of ACS and its treatment. It is erroneous to attribute somatic problems to PTSD if they are in fact secondary to the effects of ACS and its treatment. These issues highlight the difficulties associated with applying the current PTSD framework to distress reported in response to medical illness, and how cautiously results must be interpreted Consequences of PTSD following ACS Posttraumatic stress is associated with a number of adverse consequences following ACS. PTSD commonly co-occurs with depression in cardiac patients and depressive symptoms are themselves associated with a 2-fold increased risk of mortality (van Melle et al., 2004). Posttraumatic stress symptoms are associated with outcomes such as impaired quality of life (Doerfler et al., 2005), inadequate coping (Alonzo, 1999), increased smoking and alcohol intake (Op den Velde et al., 2002), which are, in themselves, independent risk factors for cardiovascular complications after MI (Shemesh et al., 2003), and overall worse general health (Jones et al., 2007). Stukas et al (1999) report that PTSD is also a strong predictor of mortality after a heart transplant. In this study, those patients who survived beyond a year post transplant and who met criteria for PTSD during that year had over a 13 times greater risk of mortality by 3 years post-transplant. This effect was independent of other known transplant related predictors of mortality. Alonzo (1999) highlighted the variation in patients reactions and suggested that as distress increases, their ability to seek help when 71

72 experiencing early signs of a further MI is reduced. The impact of posttraumatic stress symptoms on patients health behaviours and adjustment is presented in chapter 7 in this thesis, further demonstrating the significant negative influence of posttraumatic stress symptoms on patients post ACS recovery. Levels of social support may also be affected in MI patients who develop PTSD. Lack of social support is a known risk factor for coronary heart disease (CHD) and has also been related to adverse prognosis (Everson-Rose & Lewis, 2005). Kutz et al (1994) reported that patients who developed PTSD following MI were more likely not to return to work, to decrease their social activity level, and to avoid social situations, all of which result in less social contacts and less potential support. Since lack of support is an independent risk factor for CHD it is possible that MI patients who suffer posttraumatic stress symptoms have a higher risk of developing recurrent cardiac events. PTSD is also associated with nonadherence to cardiac medications in survivors of MI, which in turn is related to poor medical outcome (Shemesh et al., 2001). It is speculated that taking prescribed medication may serve as a reminder of the traumatic event, something which those with PTSD would rather avoid. A study by Shemesh and colleagues (2004) found that PTSD was associated with almost a 3-fold increased risk of readmission for cardiovascular events 1.5 years post admission for MI. It can be speculated that these findings relate to the increased nonadherence observed in the sample, or they may be related to biological correlates of PTSD (see section 2.9), which may put an additional strain on an already ailing heart The relationship between PTSD and depression The comorbidity between PTSD and anxiety, depression, substance abuse, somatoform disorders, and personality disorders is well documented (McFarlane & Papay, 1992) In a large scale epidemiological study in the general American population [National Comorbidity Study], lifetime prevalence of PTSD was estimated at 7.8%, 72

73 based on interviews with over 5000 people (Kessler et al., 1995). Approximately 48% (47.9% of males and 48.5% of females) of those who met criteria for lifetime PTSD also met criteria for major depression, secondary to the trauma. These comorbidity patterns can also arise in the context of acute coronary syndromes. Rates of depression among cardiac patients are relatively high, with approximately 15% of patients developing major depression and a further 20% minor depression (Davidson et al., 2004; Lett et al., 2004; Rozanski et al., 1999). Previous research show that a substantial proportion of PTSD sufferers also meet diagnostic criteria for depression, with prevalence ranging from about 21% to 94% (e.g. Mollica et al., 1999; Salcioglu et al., 2003). As discussed in the previous section, PTSD is associated with a number of adverse outcomes following ACS. Similarly, high levels of depression are associated with psychosocial maladjustment (Drory et al., 1999), increased hospitalization and mortality among cardiac patients (Burg et al., 2003). The clinical implications of comorbidity of PTSD and depression are unclear, though a number of studies suggest that it may complicate adjustment. For example, studies have found more severe PTSD, greater depressive symptoms and greater difficulties in psychosocial adjustment among those with comorbid PTSD and depression compared with those with PTSD alone (e.g. Kozaric- Kovacic & Kocijan-Hercigonja, 2001; Maes et al., 2000; Momartin et al., 2004). These findings are replicated in those with comorbid PTSD and depression compared with those who suffer from depression without PTSD (e.g Constans et al., 1997; Frayne et al., 2004; Holtzheimer, III et al., 2005). There, is however, a lack of empirical research on the comorbidity of PTSD and depression following MI. One study (Ginzburg, 2006a) assessed the issue of comorbid PTSD and depression in a prospective study of 116 MI patients at one week and seven months post the index event. 73

74 At one week following the acute MI, 6% of patients fulfilled criteria for ASD 1, 13% were identified as suffering from depression, and 12% had comorbid ASD and depression. At follow up 8% met diagnostic criteria for full PTSD, 14% had depression alone, and 8% were classified as having comorbid PTSD and depression. Predictors of comorbidity were investigated and no association was found with baseline objective MI severity, or with subjective appraisals (threat of death, perceived severity). However, immediate levels of dissociation, intrusion, arousal and symptoms of depression were significantly associated with comorbidity at time 2. Dissociative responses to the MI and arousal symptoms were higher in the depression and comorbidity groups. The depression group reported the highest levels of intrusion, and the comorbid group reported the highest levels of initial depression. Comorbidity was significantly associated with PTSD symptomatology, depression, psychosocial functioning and somatic complaints. However, the PTSD group reported the highest levels of somatic complaints, whereas the comorbid group reported the lowest level of psychosocial functioning. It is clear from these findings that comorbidity is an important issue following acute cardiac events. Causal pathways for explaining the association between PTSD and major depression post trauma include; (1) Pre-existing depression may render individuals more vulnerable to PTSD in the aftermath of trauma; (2) The presence of PTSD may increase the risk of first onset of depression. These pathways suggest the possibility of a shared vulnerability for both disorders. However, not all support the view that comorbidity is genuine clinical phenomenon, some argue that what is observed is an artifact of symptom overlap. In fact, when examining the DSM-IV criteria for these disorders, one finds that three of the 17 symptoms of PTSD (sleep disturbance/insomnia, difficulty with or impaired concentration, and loss of interest in 1 ASD Occurs within 1 month following trauma. Similar etiology, symptoms and course as PTSD, but of limited duration - 2 days to 4 weeks. If the symptoms and behavioral disturbances of the acute stress disorder persist for more than a month, and if these features are associated with functional impairment or significant distress to the sufferer, the diagnosis is changed to posttraumatic stress disorder (APA, 1994). The usefulness of ASD in the prediction of later PTSD has been questioned (Marshall et al., 1999). 74

75 previously enjoyed activities anhedonia) are also three of the nine symptoms needed for a diagnosis of major depression. Two important research questions must be addressed; (1) Are depression and PTSD independent consequences of trauma, each having its own course and prognosis? (2) Which symptoms are shared, and which separate the two disorders? Constans et al (1997) investigated the phenomenological features of depression occurring in PTSD patients, and the relationship between depressive features and PTSD symptoms in a sample of 217 veterans of war. 84% of the sample met criteria for PTSD. These were subsequently re-categorised into three groups: comorbid depression/melancholic features; comorbid depression/non-melancholic features; no comorbid depression. Results showed that those with melancholic and non-melancholic features of comorbid depression did not differ on measures of depression, PTSD, and anxiety. However, the melancholic sub-group reported excessive guilt compared with the other groups. Further, the presence of melancholic features was related to severity of emotional-numbing experienced by the PTSD sufferers. These authors concluded that a subset of PTSD patients experience a depression subtype of PTSD, distinguished by higher frequency and severity of emotional-numbing symptoms. Franklin and Zimmerman (2001) investigated the role of overlapping symptoms in diagnostic comorbidity. These authors argued that if contaminated symptoms (i.e. the three symptoms common to both PTSD and major depression) are responsible for the observed comorbidity, they would be more frequently endorsed among PTSD patients with major depression compared with those with PTSD only, and that these symptoms would show less specificity, that is, they would correlate less highly with the total PTSD symptom score than would the other 14 PTSD unique symptoms. These authors found no evidence to support the notion that overlapping symptoms contribute to the comorbidity observed between PTSD and depression. Results showed that the contaminated symptoms did not correlate less 75

76 strongly with the total PTSD scores, nor were they more frequently endorsed by the comorbidity group. A prospective study of posttraumatic stress disorder and depression in 211 trauma survivors (a variety of traumatic stressors) showed that the intensity of depressive symptoms in PTSD resembles that of major depression. Results showed that the comorbidity group had higher scores on a range of symptoms typically thought to reflect depression than did the depression only group (i.e. diminished interest, detachment/estrangement, restricted range of affect) (Shalev et al., 1998a). However, 29% of the trauma survivors with major depression did not have comorbid PTSD. These authors concluded that depression and PTSD may be independent sequelae of trauma. Contrasting findings indicate that risk of depression only increases among trauma victims who develop PTSD, suggesting that PTSD and major depression are not influenced by separate vulnerabilities (Breslau et al., 1997; Breslau et al., 2000). A more recent study by O Donnell et al (2004) assessed predictors of PTSD, depression and comorbid PTSD/depression in a sample of 363 injury survivors at hospital discharge, and at 3 and 12 months. They argued that if indeed PTSD and depression are separate constructs, then symptom severity and diagnostic group should be a function of differential groups of predictors. Full diagnostic criteria were met in almost equal numbers for each of the three groups at 3 months (4% depressed, 6% PTSD and 5% comorbid) and at 12 months (4%, 4% and 6% respectively). A surprising degree of movement between diagnostic categories was observed, with approximately half of those with a diagnosis at both time points changing diagnostic category by 12 months follow up. The pattern of change was similar for the PTSD and comorbid groups with comparable proportions recovering, maintaining their diagnosis, or changing their diagnostic group. The depression group in contrast showed a strikingly different pattern. The majority of those with depression at 3 month had recovered by 12 months. Further, results indicated that the PTSD and comorbid PTSD/depression were predicted by the same range of variables at both time points. 76

77 Depression at 3 months was predicted by different variables. However, at 12 months, depression was no longer found to be an independent construct suggesting that in the immediate aftermath of a trauma, depression may exist as a separate and independent construct, with its own unique set of predictors. However, by 12 months, as the psychopathology becomes more chronic, this construct may become less well differentiated and no longer possible to identify. At this point, it may be most appropriate to consider the psychopathology observed as a more general traumatic stress factor that is characterized by a combination of PTSD and depressive symptoms. These results suggest that PTSD alone and comorbid PTSD/depression may be the one and same construct. The findings presented in this section highlight the complex interaction and overlap between these diagnoses. 2.8 The role of PTSD in the development of coronary heart disease Not only is PTSD an outcome of cardiac disease, in fact a number of studies have found a relationship between a diagnosis of PTSD and increased risk of developing CHD in initially disease free individuals. Boscarino (1997) found lifetime PTSD to be associated with a higher prevalence of circulatory disorders (OR= 1.62), independent of demographic factors, smoking and substance abuse. A later study found an association between PTSD and increased risk of MI (OR= 4.44) (Boscarino & Chang, 1999). This effect was independent of a number of traditional risk factors such as smoking, body mass index and alcohol use. Consistent with work suggesting that PTSD rather than trauma exposure alone may mediate between trauma and risk of adverse health outcomes, a study by Dong and colleagues (Dong et al., 2004) found that men and women who had experienced numerous adverse childhood events were at increased risk of CHD (OR= 3.6, C.I: ), this effect was explained more completely by psychological distress than by traditional risk factors. 77

78 The association between PTSD and increased risk of CHD may be explained by pathophysiological changes that occur during a stress response. A persistent state of arousal may contribute to the progression of CHD mediated through changes in haemostatic parameters (von Känel et al., 2001). A recent review suggests that PTSD confers a heightened pro-inflammatory state (Gander & von Känel., 2006). Although there are some inconsistencies in the evidence for a pro-inflammatory state in PTSD, most consistency emerged in studies showing higher Interleukin (IL) -1β in individuals with PTSD compared with controls (Sondergaard et al., 2004; Tucker et al., 2004). It has also been found that patients with PTSD have lower peripheral cortisol levels both at rest and in response to trauma related stimuli (McFall et al., 1990; Yehuda et al., 1992) as well as reduced heart rate variability (Cohen et al., 1997). The pathophysiology of PTSD will be discussed in the following section. If PTSD indeed has pathophysiological effects, it seems they would be likely to be most evident when symptoms of PTSD follow a pattern of persisting or recurring over time. PTSD may also motivate health-related behaviours that can influence risk of developing CHD. For example, PTSD has been associated with greater likelihood of smoking and excess alcohol consumption, behaviours that increase risk of CHD (Breslau et al., 2003). A large scale prospective study of PTSD and CHD (data from the Normative Ageing Study) assessed the relationship directly and independently of depression (Kubzansky et al., 2007). It is important to establish a link between PTSD and CHD that is independent of depression due to concerns that self-report measures of PTSD may over-emphasize the depression component of PTSD and because depression has been identified as a risk factor for CHD (see chapter 1). Some investigators have suggested that any apparent PTSD-CHD association is largely due to depression. Kubzansky et al (2007) report that PTSD symptoms are associated with an increased risk of incident CHD. Although modest, the effects were maintained after controlling for depressive symptoms, and most clearly apparent in relation to the hard outcomes of nonfatal MI and fatal CHD. These findings were also suggestive of a dose-response 78

79 relationship, as for each standard deviation increase in posttraumatic stress symptoms there was a significant increase in the risk of developing CHD (OR= 1.21). A more recent study investigated PTSD and increased risk of CHD in a sample of community dwelling women [non-military], and tested whether the relationship was independent of other forms of distress (Kubzansky et al., 2009). These authors found that women reporting five or more posttraumatic symptoms were at over three times greater risk of developing CHD compared with those with no symptoms (OR= 3.21, C.I: ). Findings were maintained after controlling for standard risk factors as well as depression and trait anxiety. The findings discussed in this section are provocative, and suggest that being exposed to a traumatic event and experiencing a prolonged stress reaction may not only lead to psychological disability but may also have cardiotoxic effects. More research is clearly called for to further the understanding of a possible relationship between posttraumatic stress and cardiovascular endpoints. 2.9 Psychophysiology of PTSD Cortisol Although a great many individuals are exposed to one or more traumatic events in their lifetime (Kessler et al., 1995), and many develop symptoms in the early aftermath of trauma, the intensity of the initial response and the number of individuals who manifest these responses substantially decreases as time goes on. Posttraumatic symptoms become chronic only in a subgroup of trauma survivors. Thus, PTSD can be best considered a possible, not inevitable, outcome following trauma exposure. As discussed in previous sections, there are a number of psychological variables that influence the development of PTSD in the days following the initial trauma. There also 79

80 appear to be some salient predictors of PTSD that manifest within hours after the traumatic event. These are not psychological variables, but rather biological ones. Evidence is emerging of a distinct pathophysiology for posttraumatic stress. The hypothalamic-pituitary-adrenal (HPA) axis is activated in response to stress. HPA axis activity is governed by the secretion of corticotropin-releasing hormone (CRH) from the hypothalamus (figure 2.1). CRH activates the secretion of adrenocorticotropic hormone (ACTH) from the pituitary. ACTH, in turn, stimulates the secretion of glucocorticoids (cortisol in humans) from the adrenal glands. Cortisol interacts with their receptors - the corticosteroid receptors - in almost every tissue in the body, and the best known effect is the regulation of energy metabolism. By binding to corticosteroid receptors in the brain, cortisol also inhibits the further secretion of CRH from the hypothalamus and ACTH from the pituitary (negative feedback). The major function of cortisol is to contain these stress-activated reactions (Munck et al., 1984). FIGURE 2.1 HYPOTHALAMIC-PITUITARY-ADRENAL AXIS 80

81 The neuroendocrine profile observed in those with chronic PTSD is somewhat paradoxical as the alterations in patterns make the patterns almost exactly the opposite of the patterns observed in chronic stress and major depression (Chrousos & Gold, 1992). Like depression, PTSD is associated with increased secretion of corticotropin releasing factor. Unlike depression, however, the increased secretion is associated with hypocortisolaemia (Baker et al., 1999), suggesting a grossly exaggerated negative feedback inhibition of the HPA axis that is possibly secondary to up-regulation of glucocorticoid receptors (Liberzon et al., 1999). That is, chronic PTSD is characterized by a decrease in levels of circulating cortisol and a concomittant increase in responsiveness of glucocorticoid receptors, an increased sensitivity of the HPA negative feedback inhibition, and a progressive sensitization of the entire HPA axis (Yehuda et al., 1998). These findings raise two questions; (1) are these alterations observed in the HPA axis characteristic of chronic PTSD, and; (2) are there fundamental differences in the way the HPA axis functions normally that will influence the way in which an individual will respond to traumatic stress? There are inconsistencies in the literature on the relationship of cortisol and PTSD, with some studies reporting increased levels of cortisol and others reporting decreased cortisol. Whereas a number of studies of women with PTSD have found evidence of HPA hyperactivity, particularly following childhood abuse (Heim et al., 2000; Lemieux & Coe, 1995; Maes et al., 1998; Rasmusson et al., 2001), studies of male combat veterans and elderly Holocaust survivors have found evidence of a low cortisol profile, persisting even in the presence of major depression (e.g. Boscarino, 1996; Yehuda, 2002a; Yehuda et al., 2002). Some prospective research has shown that low cortisol levels at the time of exposure to psychological trauma predict the development of PTSD (e.g. Resnick et al., 1995; Yehuda et al., 1998), suggesting hypocortisolism might be a pre-existing risk factor that is associated with maladaptive stress responses such as PTSD. Consequently, administration of hydrocortisone directly after exposure to psychological trauma has been shown to effectively reduce 81

82 risk of developing PTSD (de Quervain, 2008; Schelling et al., 2004). Resnick et al (1995) found significantly lower cortisol levels in a sample of female rape victims (assessed during emergency room visit within hours of the trauma) with prior history of sexual assault. This group of women was three times more likely to develop PTSD at 4-month follow up than were women without a history of prior sexual assault. These authors argued that prior traumatization may have been the cause of altered HPA axis function in response to subsequent trauma, and that the attenuated cortisol response consequently increased the risk of PTSD from this new trauma. This study was however limited by the fact no other psychiatric or psychological disturbances were assessed or controlled for. In a study of motor vehicle accident victims, McFarlane et al (1997) found lower levels of cortisol in the immediate aftermath of the trauma among those who at 6 months were classified as having PTSD, compared with no diagnosis or depression (higher levels of cortisol observed). These findings were independent of accident severity, time of day and minutes post-trauma. The studies by Resnick et al (1995) and McFarlane et al (1997) suggest a paradoxically lower cortisol response is present in trauma victims who later go on to develop PTSD compared with those who either develop depression or those who subsequently do not develop any psychiatric disorder. However, in both these studies cortisol was assessed only post trauma, there was no examination of cortisol levels prior to trauma, therefore no statement can be made about the cortisol response relative to these individuals baseline levels. Not all studies have demonstrated lower cortisol levels among groups of PTSD patients. Indeed some have reported the opposite pattern. Liberzon et al (1999) found significantly higher baseline cortisol among PTSD diagnosed patients than among non- PTSD controls. Pitman and Orr (1990) reported significantly higher 24-hour cortisol values in those with PTSD compared with controls. A study by Young and colleagues (2004a) found normal levels of cortisol in a low SES sample of women with high exposure rates to trauma, with either current or lifetime PTSD. However, a non- 82

83 significant trend of higher cortisol in women with comorbid lifetime PTSD and past-year major depression was observed. Young and Breslau (2004a) assessed 24-hour urinary free cortisol (UFC) at a sleep research centre, in a community sample and demonstrated no effect of exposure to trauma, or lifetime PTSD, on UFC. They did however find a significant increase in UFC among women with comorbid major depression and PTSD. Using a larger subset of this community sample, Young and Breslau (2004b), using morning and early evening salivary cortisol from 516 participants (265, exposed to trauma, 183 not exposed to trauma, 68 current or past PTSD), found significantly higher evening cortisol in participants with PTSD compared with those who had been exposed to trauma but did not develop PTSD. Further, analyses comparing PTSD only with major depression only, comorbid depression/ptsd and no disorder did not show higher levels of cortisol for either the PTSD or depression only groups. However, the comorbid group demonstrated elevated evening levels of cortisol in comparison with the no disorder group. Whereas this effect was observed among women only in the earlier study (Young & Breslau, 2004a), in this study the effect was observed in participants of both genders. Though findings are inconsistent, there is strong evidence for persistent cortisol abnormalities. These abnormalities may be a trait rather than state phenomenon, possibly reflecting pre-existing abnormalities rather than a consequence of prior disorder. The findings of elevated cortisol in those with comorbid psychiatric disorders further highlight the importance of assessing comorbidity among those with PTSD. More research needs to be undertaken to further the understanding of the role of cortisol in PTSD. The cortisol profiles in a sample of ACS patients are investigated in relation to posttraumatic reactions in chapters 6 and 7. These data show some interesting findings in relation to co-morbid depression. 83

84 2.9.2 Heart rate One of the criteria that must be fulfilled for a diagnosis of PTSD is that of psychophysiological arousal such as sleep disturbances, hypervigilance and an exaggerated startle. A number of studies now also suggest an important role of heart rate (in the immediate aftermath of trauma) for subsequent development of PTSD. In a prospective study of 91 injured trauma survivors (not requiring admission), heart rate was assessed at the emergency department, then at 1 week, 1 month and 4 months post trauma (Shalev et al., 1998b). The CAPS was also administered at each assessment point to assess posttraumatic stress. The results showed higher heart rates but not blood pressure, among those who later went on to develop PTSD, at the emergency department and at 1 week post trauma heart rate assessment. At 1 and 4 months follow up, there was no longer a difference in heart rate. These results were independent of age, trauma severity, response intensity and peri-traumatic dissociation. It is important to note, however, that those who did not develop PTSD by 4 month follow up, also had elevated heart rate at the emergency room, which would be expected from a stress response. Consistent with these findings are those of Bryant et al (2000). In this study, discharge heart rate of motor vehicle accident survivors was significantly predictive of 6-month PTSD. Unexpectedly, higher heart rates were demonstrated among those with sub-clinical ASD. This might be due to the lack of dissociation in this group. The presence of dissociative symptoms distinguishes ASD from sub-clinical ASD, and it has been proposed that dissociation may reduce overwhelming distress and arousal in the acute phase thereby providing one explanation for the higher heart rates observed in the sub-clinical ASD group. A more recent study by Zatzick et al (2005) further support these results. Emergency heart rate of 95 beats per minute was a significant independent predictor of posttraumatic stress symptoms in severely injured surgical inpatients, over the course of one year (assessment points in-hospital, 1 month, 4-6 months, 12 months). These authors found 84

85 no significant association of heart rate and subsequent depression, even though major depression has been associated with elevated heart rate in a number of studies (e.g. Carney et al., 1999; Moser et al., 1998). Shalev et al (1998b) report that although emergency department heart rate significantly predicted PTSD at 4 months, it showed no relationship with depression. These findings suggest that autonomic nervous system disruptions may develop as a result of depression, whereas for PTSD these alterations may contribute to the pathogenesis of the disorder. Although these three prospective studies have differed in terms of heart rate measurement, type of trauma and trauma severity, inclusion and exclusion criteria, and the timing and duration of follow up, taken together they provide support for an association between elevated sympathetic arousal and the development of enduring posttraumatic stress symptoms. However, contrasting findings have been demonstrated by Blanchard et al (2002). In this study of 76 motor vehicle accident survivors, heart rate assessed in the emergency department was significantly negatively associated with increasing posttraumatic symptom levels (CAPS interview) at 13 months follow up. The use of heart rate as a predictor of later PTSD among cardiac patients is problematic due to the use of beta-blockers as part of treatment for myocardial infarction. However, I did investigate both heart rate and heart rate variability in relation to PTSD in the second of the studies I conducted (see chapters 6 and 7) Chapter summary Although in recent years more attention has been directed to posttraumatic stress reactions in patients following an ACS, it is becoming clear that the risk factors for posttraumatic stress in cardiac patients may not be the same as the risk factors for PTSD following other trauma. Risk factors for developing posttraumatic symptoms following MI may include pre-event vulnerabilities such as prior trauma or personality, event related variables such as the subjective experience of pain and emotional 85

86 experiences soon after hospitalization, and post-event experiences such as social support and coping. Together these findings clearly show that a significant minority of cardiac patients will go on to develop posttraumatic stress symptoms, and that this may have a significant impact on morbidity and mortality. Considering the well established link between depression and CHD it is becoming clear that PTSD may have similarly detrimental effects on outcomes. Many studies in this area have used relatively small samples and follow up periods have generally been shorter than 9 months. In order to fill this gap, I carried out a study to assess the longer term prevalence and predictors of posttraumatic stress symptom in patients 12 and 36 months post admission. These results are presented in chapter 4. The second study I carried out assessed posttraumatic stress in a sample of ACS patients at 2 weeks, 6 months and 12 months post the acute event investigating the change in symptoms during the first year post trauma. The results of this study are presented in chapters 6 and 7. 86

87 CHAPTER 3. Methodology ACCENT study 3.1 Introduction and hypotheses There is growing evidence that PTSD may develop as a consequence of an acute cardiac event. The average prevalence rate of PTSD across studies of post-mi patients is approximately 15% (Gander & von Känel, 2006). Most prospective research on PTSD has measured posttraumatic stress symptoms 3 9 months following cardiac events, and only one study (van Driel & Op den Velde, 1995) has involved longer periods. Since longer term prevalence has not been studied in detail, it is not known whether symptoms persist or there is recovery over time. Cross-sectional studies suggest that posttraumatic symptoms may last for many years (Jones et al., 2007; Bennett & Brooke, 1999), but dysphoric reporting biases may be present (Chung et al., 2007). The first aim of this study was therefore to establish the intensity of posttraumatic symptoms and incidence of PTSD at 12 and 36 months following admission to hospital with an acute coronary syndrome (ACS) in a prospective design. The second aim of the study was to identify early predictors of later PTSD. For most traumatic events, posttraumatic symptoms increase with the severity of the stressor (Brewin et al., 2000). However, studies of cardiac patients have not typically found a relationship between severity of cardiac damage or occurrence of other cardiac symptoms and the development of posttraumatic stress symptoms (O'Reilly et al., 2004; Pedersen et al., 2003; Ginzburg et al., 2003). But although objective clinical severity appears not to be predictive, several studies have found that subjective intensity of the event (Ginzburg et al., 2002; Ginzburg et al., 2003; Whitehead et al., 2006) and the anticipation of incapacitation (Kutz et al., 1994) are predictive of later PTSD. Acute cardiac events can be very distressing and expectations of death or serious disability can be highly influential. Fear during and immediately after the acute 87

88 event is associated with greater posttraumatic stress symptoms (Bennett & Brooke, 1999; Bennett et al., 2001). It is also possible that recurrence of cardiac symptoms in the period following hospital discharge will increase posttraumatic symptoms by providing vivid reminders of the acute event. A growing number of studies suggest that emotional responses and negative affective states soon after admission are strongly predictive of posttraumatic symptoms following ACS (Pedersen et al., 2003; Bennett et al., 2001; Whitehead et al., 2006). However, the significance of early emotional reactions for persistent long-term posttraumatic symptoms has not been established. In this study, my focus was on how acute emotional reactions to ACS and patients general psychological dispositions would influence the development and course of PTSD 12 and 36 months after the acute event. My predictions were that depressed mood following hospital admission would be predictive of posttraumatic stress symptoms 12 and 36 months later, independently of clinical and sociodemographic factors. In addition I hypothesised that patients psychological disposition, in particular type D personality and hostility, would also be predictive of posttraumatic stress symptoms 12 and 36 months following hospital admission. Previous research suggest that posttraumatic stress symptoms may become chronic, if left untreated, beyond as early as nine months post trauma (Gander & von Känel, 2006; Freedman et al., 1999). Therefore, I hypothesised that patients posttraumatic stress symptoms would show chronicity, remaining stable between 12 and 36 month follow up. The analyses presented in this chapter were undertaken as part of the ACCENT study (see section 3.2). Posttraumatic stress symptoms were previously assessed at 3 months, and results from this subgroup of ACCENT patients have been presented elsewhere (Whitehead et al., 2006). 88

89 3.2 Participants Participants were 284 patients admitted with ACS to one of three London hospitals. ACS was diagnosed based on the presence of chest pain and the following criteria: verification by electrocardiographic changes (ECG; new ST elevation > 0.2mV in two contiguous leads [V1, V2, or V3] and > 0.1mV in two contiguous other leads, ST depression > 0.1mV in two contiguous leads in the absence of QRS confounders, new left branch bundle block, or dynamic T wave inversion in more than one lead), and/or elevated cardiac enzymes (troponin T measurement > 0.01µg/l or a creatine kinase measurement more than twice the upper range of normal for the measuring laboratory). As the original focus of this study was to investigate acute triggering of ACS, patients were eligible to participate if they could recall the specific time of symptom onset. Patients with co-morbid conditions which could influence either symptom presentation or mood state (such as severe psychiatric illness, unexplained anaemia, ongoing infection or inflammatory conditions, neoplasia and renal failure), and conditions that might cause false troponin positivity, were excluded. Eligible patients were aged between 18 and 80 years old and were able to complete the in-hospital interview and questionnaire measures in English. Data for this study were collected between 2001 and potentially eligible patients were admitted on the days recruitment was conducted during this period. Of these, 46 patients (12.8%) had been discharged or transferred on to a different hospital before the researchers could conduct the in-hospital interview. A further 30 patients (8.3%) declined to participate in the study (fig. 3.1). These data were collected as part of a larger study of emotional experiences related to ACS (The ACCENT study), and other results from this study have been published previously, including data about the triggering of cardiac events (Strike et al., 2006), the delays between symptom onset and admission to hospital (Perkins-Porras et al., 2009), the role of ongoing stress and social support in determining adherence to advice following 89

90 discharge (Molloy et al., 2008) and factors predicting return to work (Bhattacharyya et al., 2007). 3.3 Study design and procedure Potential participants were approached as soon as possible after their admission for ACS, at which point the study was explained and informed consent was obtained. The in-hospital interview took place on average 2.56 ± 1.5 days following admission, with the majority (95%) taking place within five days of admission. These interviews primarily focused on the circumstances surrounding symptom onset and hospital admission. After the interview, within 7 to 10 days of admission, participants completed a battery of self-report questionnaires (detailed in the section below). Patients were re-contacted at 3 (reported elsewhere, Whitehead et al., 2006), 12 and 36 months post admission for ACS. Only data collected at the baseline hospital assessment and at 12 and 36 month follow up have been used for the purpose of this thesis. The follow-up interviews included a semi-structured telephone interview assessing recurrence of symptoms, other health problems, adherence to medication, and health behaviours (Appendix I) (details on health behaviours from this data set not shown as I did not incorporate these in any of my analyses or hypotheses). The participants were also mailed a battery of standardized questionnaires, at both follow up points, to complete and return by post My role in study design, data collection and analysis As the ACCENT study was already underway at the time of my joining, my responsibilities at that stage included data collection, data entry and data analysis. I carried out the majority of the 36 month interviews, though a proportion was conducted 90

91 by a colleague, Dr Mimi Bhattacharyya. The statistical analyses included in this thesis were undertaken by myself, with additional guidance from my thesis supervisor. 3.4 Psychosocial measures Socio-demographic information Patients age, marital status and ethnicity were obtained and data on patients socio-economic status (SES) were collected during the in-hospital interview. A social deprivation index was created using an adaptation of the Townsend Material Deprivation Index (1988). This index has been shown to be related to increased cardiovascular risk factors (Sunquist et al., 1999) and is a broad measure of social deprivation and access to resources. Social deprivation was computed based on the following four criteria: renting one s home (as opposed to owning a home), not having access to a car or van, living in a crowded household (defined as more than one person per room) and being in receipt of state benefits. Scores on these items ranged from 0 to four, with four being the highest level of deprivation. Participants were classified as low deprivation (negative on all items), medium deprivation (one positive score) and high deprivation (two to four positive items). In addition, patients level of educational attainment was also assessed. The level of reported education was categorised into seven groups: no educational qualifications, up to school certificate, CSE s, GCSE s, A level, Degree and Other. For the purposes of statistical analyses educational attainment was reclassified into a categorical variable with three levels; none, up to O-level and A-level or above. 91

92 3.4.2 Clinical data Information on clinical factors during admission, management and cardiovascular history was collected from patients hospital admission notes. Admission ECGs and troponin T or creatine kinase data were reviewed by a cardiologist and patients were classified for presentation with ST- elevation myocardial infarction (STEMI), non ST- elevation myocardial infarction (NSTEMI) or unstable angina (UA). For purposes of analyses these were categorised into a binary variable STEMI vs NSTEMI/UA. Composite clinical risk scores were also computed based on the algorithm developed in the Global Registry of Acute Coronary Events (GRACE) study (Eagle et al., 2004). This algorithm uses the following nine criteria to define risk of six month post discharge death applicable to all types of ACS: age, history of congestive heart failure, history of MI, systolic blood pressure and heart rate on admission, ST segment depression, initial serum creatine, raised cardiac enzymes and no in-hospital percutaneous coronary intervention. Patients subjective pain ratings were also recorded. Chest pain during ACS was rated on a 10-point scale from 1= very low to 10= excruciating; however, this measure was not introduced until midway through the study, so was available only for a subset of patients Psychological measures A range of well established standardized questionnaires were used in this study (Appendix II). These were used to assess emotional states and well-being as well as psychological traits. The questionnaires described in this section have been widely used with cardiac populations in previous research. A range of emotional factors was measured, including depression, anxiety and posttraumatic stress symptoms. Other measures included negative affectivity and hostility. The full range of questionnaires 92

93 employed is described in detail below. Details on which measures were obtained at each time point are presented in table Beck Depression Inventory (BDI) Patients level of depression was assessed using the Beck Depression Inventory (BDI), a standard measure of depressive symptoms (Beck et al., 1988). This measure has been widely used in cardiac populations and is considered a valid measure of depression (Buchanan et al., 1993; Crowe et al., 1996; Frasure-Smith et al., 1997). Findings from a meta-analysis of studies of non-psychiatric participants show a mean coefficient alpha of.81 (Beck et al., 1988). The BDI is a 21-item selfreport measure that assesses the severity of depressive symptoms over the past week. Patients rate symptoms from none (0) to severe (3). The scores can range from 0 to 63 with higher scores indicating more severe depressive symptoms. The standard cut-off points are as follows; 0-9 indicates that the person is not depressed, suggests mild-to-moderate depression, indicates moderate to severe symptoms and a score between would suggest presence of severe depression. The Cronbach s alpha for this scale was.88. The BDI suffers the same problems as other self-report measures; for example, the way the measure is administered can influence the responses. For instance, if a patient is asked to fill the form out in front of other people in a clinical environment, social expectations might elicit a different response compared to administration via a postal survey (Bowling, 2005). More closely relevant to this study of ACS patients is the issue of the BDI s inclusion of physical symptoms such as fatigue, which might artificially inflate depression scores in a medically ill population, due to symptoms related to their physical health status rather than depression. 93

94 Posttraumatic Stress Symptoms Self Report Scale (PSS-SR) The presence and severity of posttraumatic stress symptoms was assessed using the PTSD Symptom Scale - Self Report version (PSS-SR) (Foa et al., 1993), the precursor of the Posttraumatic Diagnostic Scale (Foa et al., 1997). The PSS-SR is a 17-item scale with items corresponding to the DSM-IV criteria for diagnosis of PTSD for the three dimensions of intrusions/re-experiencing, avoidance and arousal. Each item is rated on a 4-point scale, ranging from 0 (not at all) to 3 (5 or more times per week) to indicate frequency of patients experiencing symptoms during the past 2 weeks. The PSS-SR was primarily analyzed as a continuous variable to identify predictors of posttraumatic stress symptoms. However, the scale can also be used to identify individuals who meet criteria for a probable diagnosis of PTSD, and has been endorsed by the UK National Institute for Health and Clinical Excellence (National Institute for Health and Clinical Excellence, 2005) as a suitable measure of PTSD. Sensitivity of the scale is 62% and specificity 100% based on DSM criteria (Foa et al., 1993). The presence of PTSD is defined by a score of 1 or more on at least one re-experiencing, three avoidance and two arousal symptoms. For the purpose of analysis in this study, I used an adaptation of the original scoring system, such that the presence of PTSD was defined by a score of 2 (two to four times a week) on at least one intrusion, three avoidance and two arousal items. These scoring criteria are more conservative than the original guidelines but are more closely related to original DSM-IV criteria (APA, 1994). Brewin and colleagues (1999) found that when using original scoring criteria, some patients were identified with PTSD even though no symptom was rated more than 1 and the total symptom scores were as low as 9 (possible range 0-51). Thus in order to eliminate low scores and to more closely match DSM criteria for persistence of symptoms these authors argue that the adapted scoring system will yield a more accurate estimate of posttraumatic symptoms. This modified version of the scale showed good internal reliability with a Cronbach s alpha of

95 Hospital Anxiety Scale (HADS-A) Anxiety was measured using the anxiety sub-scale from the Hospital Anxiety and Depression scale (HADS). This is a well-established measure of psychological distress in medical patients, and was originally developed to assess anxiety and depression in a clinical population of medical outpatients suffering from a wide variety of illnesses (Zigmond & Snaith, 1983). The anxiety sub-scale of the HADS has seven items (five which are reverse scored) which are scored from 0 (not at all anxious) to 3 (very often anxious). Total scores range from 0 to 21, with higher scores reflecting greater anxiety. The recognised cut-off for moderate anxiety is scores 8. Cronbach s alpha for this scale was.85. The HADS has shown good psychometric properties with Cronbach s alpha over.60 in populations of both medical and psychiatric settings, and in the general population (Bjalland et al., 2002) Medical Outcome Short Form 36 (SF36) The SF36 is a general outcome measure which assesses health status and health-related quality of life (Ware & Sherbourne, 1992), and has been frequently used to assess quality of life among cardiac patients (Brown et al., 1999; Fogel et al., 2004; Rumsfeld et al., 1999). Previous research has shown good psychometric properties of this scale, with internal reliability in excess of.70 (Ware & Gandek, 1998). The SF36 uses eight sub-scales to measure three aspects of health functional status, wellbeing, and overall evaluation of health. The Cronbach s alphas ranged between.77 and.94 for these scales. This measure has 36 items, which are grouped into eight multi-item sub-scales representing the three domains described above. These are as follows: (1) functional status physical functioning (limitations in physical activity due to physical problems), social functioning (interference with social activities due to physical and emotional health problems), role limitations due to physical problems (problems 95

96 with work and daily activities due to physical health), role limitations due to emotional problems (problems with work and daily activities due to emotional problems); (2) wellbeing mental health (anxiety and depression), vitality (energy and fatigue), bodily pain (severity); (3) overall evaluation of health general health perception (evaluation of physical health and likelihood of improvement). Each sub-scale is scored from 0 (worst health status) to 100 (best health status) to indicate level of function. The SF36 can also provide scores on two summary components, by averaging scores on the subscales relevant to these; physical health status (physical functioning, role limitations due to physical problems, bodily pain and general health perception) and mental health status (social functioning, limitations due to emotional problems, vitality and general mental health) Cook and Medley Hostility Scale (Ho) Hostility is a key component of Type A personality (Williams, Jr. et al., 1980) and has been linked to CHD rates. Hostility is also associated with poor health behaviours such as smoking and alcohol use. The link between hostility and CHD may be mediated by poor health behaviours. Hostility was assessed using the Cook-Medley Hostility Scale (Ho) (Cook & Medley, 1954). This 39-item version contains items from the four subscales identified by Barefoot and colleagues; cynicism, hostile attribution, hostile affect and aggressive responding (Barefoot et al., 1989). Responses to these items were rated 0 (false) or 1 (true), with total scores ranging from 0 (lowest hostility) to 39 (highest hostility). The Ho scale has been reported to have good psychometric properties, including adequate internal validity, good test-retest reliability and construct validity (Barefoot & Lipkus, 1994). In this study the Cronbach s alpha was

97 Type D (DS16) Type D personality is associated with worse prognosis following myocardial infarction and is defined as the joint tendency towards negative affectivity and social inhibition. Type D was assessed using the 16-item DS16 (Denollet, 1998). Each item is rated according to a 5-point Likert scale with scores from 0 (false) to 4 (true). Patients who score high on both the social inhibition and negative affectivity scales, as determined by median split, are classified as having Type D. This scale shows satisfactory psychometric qualities and prognostic power, with Cronbach s alpha of.89 and.82 for the negative affectivity and social inhibition scales respectively (Denollet, 1998; Denollet et al., 2000). The Cronbach s alphas for the negative affectivity and social inhibition scales in this study was.85 and.73, respectively Fear, helplessness and horror Acute stress. During the in-hospital interview, patients were asked to rate their experience of acute distress and fear at the time of their ACS. This was measured using three items; I was frightened when the symptoms came on, I thought I might be dying when the symptoms came on, and I found my cardiac event stressful. Each item was rated on a five point scale; not at all true, slightly true, somewhat true, very true and extremely true. A combined score was created by averaging these ratings. Participants were subsequently categorised into one of three groups; no distress and fear (average ratings of not at all true ), moderate distress and fear (average ratings of slightly true and somewhat true ) and high distress and fear (average ratings of very true and extremely true ). This measure was used as a proxy measure for fulfilment of criterion A of the DSM-IV classification for a diagnosis of PTSD. 97

98 Acute stress disorder Patients acute stress reactions (acute stress disorder - ASD) to the ACS was assessed using a composite scale derived from the Peritraumatic Dissociative Experiences Questionnaire (Marmar et al., 1997) and the Acute Stress Disorder Scale (Bryant et al., 2000). This scale consisted of 14-items rated on a five point scale ( not at all true to extremely true ). Higher scores indicate greater dissociation, flashbacks, intrusions and fear duing the event. Cronbach s alpha showed good reliability of the full scale (α =.87). As for the subjective pain measure, the acute stress measure was also not introduced until midway through the study and therefore data were only available for a subset of patients for this scale. 3.5 Data storage All data collected were treated as confidential. Interview data and questionnaires from all data collection points were kept separate from consent forms, and all were kept in locked filing cabinets with restricted access. Data were anonymised and entered into a database which was password protected. 3.6 Statistical analyses All statistical analyses were performed using the statistical programme SPSS 17.0 (SPSS Inc). The significance level was set at p <.05 for all analyses. Specific details on the analyses conducted are presented in the results sections of chapter 4. 98

99 TABLE 3.1 MEASURES OBTAINED AT EACH TIME POINT Time point Measure In-Hospital 12 m follow up 36 m follow up BDI X X X PTSS-SR X X HADS-A X X X SF-36 X X X Ho-Scale X Type D X Acute stress X ASD X X: Measure was administered at this time point. Hospital Recruitment N=360 Discharged or Transferred N=46 Declined to Participate N=30 In-Hospital Interview N=284 Untraceable N= 54 Excluded from analyses at 12m N=13 Deceased N=4 12 month Follow up N=213 Untraceable N=34 36 month Follow up N=179 FIGURE 3.1 FLOWCHART OF PATIENT RECRUITMENT 99

100 CHAPTER 4. Results ACCENT Study 4.1 Results Data analyses 226 of the 284 patients recruited at baseline were assessed at 12 months. Four patients were deceased and 54 could not be traced. 13 of the patients responding at 12 months had to be excluded from the analyses because their questionnaires provided insufficient data. At 36 months 179 patients were re-assessed. The principal analyses were therefore carried out on 213 patients at 12 month follow-up, and 179 at 36 months (for flowchart of recruitment see chapter 3, fig. 3.1). Patients who did not complete the 12 month follow up were similar to those who completed the study on most baseline clinical, demographic and psychological variables. However, non-completers were more likely to be in the high deprivation category (χ² = 19.26, p <.001), scored significantly lower on the GRACE index (t = -2.19, p <.05) and were more likely to be unmarried (χ² = 6.63, p <.05) than completers. At 36 months, a significantly greater proportion of non-completers scored highly on the index of social deprivation (χ² = 6.84, p <.05). There were no differences on any of the clinical, demographic or psychological characteristics collected during admission between those patients who completed 12 month follow-up only, 36 months follow-up only, or those who completed follow-up at both time points. The prevalence of PTSD and severity of posttraumatic symptoms at each time point were examined. Repeated measures analysis of variance was employed to test whether symptom levels changed between 12 and 36 months. Associations between posttraumatic stress symptom severity at 12 and 36 months and demographic, clinical and psychological factors were analyzed using product-moment correlations for continuous variables and analysis of variance for categorical variables. Multiple 100

101 regressions on posttraumatic symptoms at 12 and 36 months were conducted in order to identify independent predictors of symptom levels. I selected variables into these models based on previous literature and the results from the univariate analyses. Two models were tested for each follow up point. Model 1 included demographic and clinical factors. In Model 2, demographic and clinical factors were entered on step 1, and psychological predictors on step 2. In the regression on 36 month posttraumatic symptoms, 12 month symptom levels were included at step 1. Standardized regression coefficients (β) are presented Patient characteristics The characteristics of the patients participating at 12 month follow up are presented in Table 4.1. Patients were aged 61 years on average, and the majority were men of white European descent. They were poorly educated, with only 30% having completed high school or college. The majority had experienced an STEMI rather than an NSTEMI/UA. ACS severity as defined by the GRACE score was moderate, and only 9.9% had experienced a previous MI. Depression scores on the BDI 7-10 days following admission were elevated, with 33.1% scoring

102 TABLE 4.1 PATIENT CHARACTERISTICS 12 MONTH SAMPLE Mean (SD) N (%) Demographic factors Age (11.22) Gender Men 164 (77.0) Women 49 (23.0) Educational attainment None 65 (42.5) Up to O-level 42 (27.5) A-level + 46 (30.1) Ethnicity (white) 182 (85.4) Social deprivation Low 101 (47.4) Medium 57 (26.8) High 55 (25.8) Marital status (married) 140 (65.7) Clinical factors ACS type STEMI 153 (71.8) NSTEMI/UA 60 (28.2) GRACE score (26.63) Previous MI (yes) 21 (9.9) Recurrence of cardiac symptoms (yes) 43 (21.5) Psychosocial factors BDI (following admission) 8.13 (7.51) Anxiety 5.70 (3.92) Acute stress disorder symptoms (n = 154) (9.62) Subjective pain (n = 120) 7.49 (2.22) Hostility (7.96) Type D (positive) 60 (33.1) History of depression (yes) 38 (17.8) 102

103 4.1.3 Prevalence of posttraumatic stress symptoms at 12 and 36 months At 12 months post-acs, 26 patients (12.2%) met the diagnostic criteria for PTSD, and the posttraumatic symptom severity score was (SD 10.40). At 36 months 23 (12.8%) patients were identified as having PTSD, and the severity score for the sample averaged (SD 10.27). Table 4.2 shows the scores on the PSS-SR for the sample at 12 and 36 months on all sub-scales. It can be seen that scores did not differ markedly between 12 and 36 months, indicating no reduction in posttraumatic stress symptoms. This was confirmed by analysis of those patients who provided PSS- SR ratings at both time points (F (1, 162) = 2.27, p =.134, η 2 =.014). TABLE 4.2 PSS-SR SCORES 12 months 36 months N Means (SD) Range N Means (SD) Range PSS-SR Total score (10.40) (10.27) 0-43 PSS-SR Avoidance (5.95) (5.04) 0-21 PSS-SR Arousal (3.83) (3.75) 0-18 PSS-SR Re-experiencing (2.66) (2.48) 0-12 PTSD Diagnosis (positive) modified % % PTSD Diagnosis (positive) original % % As discussed in chapter 3, section , the scoring criteria used for a probable diagnosis of PTSD in this sample is more conservative than the original guidelines for this scale. When adopting the original diagnostic scoring of the PSS-SR, this measure yielded a prevalence of 51.7% of PTSD among patients at 12 months (n=109). At 36 month follow up, 75 patients (41.9%) met the diagnostic criteria for PTSD. These rates appear highly inflated in comparison with previous research on posttraumatic stress symptoms within this population, and the use of the modified criteria for scoring is supported. When the analysis was limited to patients with a 103

104 classic STEMI, the prevalence was 9.8% and mean severity score (SD 10.26) at 12 months, and 12.2% and (SD 9.98) respectively, at 36 months. At 36 months there were 8 new cases of PTSD that had not scored above the diagnostic threshold at 12 months, while 6 cases showed improvements from 12 month follow up and were no longer classified as having PTSD at 36 months. A symptom severity change score was calculated by subtracting the 36 month total symptom score from the 12 month total symptom score. 163 patients had completed the PSS-SR scale at both time points. The mean symptom change score was.77 (SD 6.56), range -18 to 36, with negative scores indicating a worsening of symptoms. None of the psychological, sociodemographic or clinical variables were significantly associated with change in symptom severity, with the exception of a history of previous Myocardial Infarction (r = -.171, p =.029). Patients who had experienced a previous MI had a significantly worse change score (mean -2.61, SD 6.52) than did those who had not had a previous MI (mean 1.14, SD 6.48) (F (1, 161) = 4.837, p =.029, η 2 =.029), suggesting these patients emotional status declined with time. The correlation between posttraumatic symptom severity at 12 and 36 months was.79 (p <.001), suggesting that generally symptom intensity remained stable over time. As seen above, in repeated measures analysis of variance, there was no significant change in posttraumatic symptom intensity between 12 and 36 months (p =.134). Patients with posttraumatic symptoms above threshold at 12 months had markedly impaired physical and mental health status at 12 months as measured by the SF-36, averaging 29.7 (SD 15.1) and 32.3 (SD 15.5) for the two scales, compared with 68.2 (SD 23.9) and 73.1 (SD 20.8) for physical and mental health status respectively in the non-ptsd patients (both p <.001). At 36 months, patients meeting criteria for PTSD continued to have impaired physical and mental health status (means 30.9, SD 11.3 and 35.7, SD 17.7 respectively) compared with the remainder (means 50.9, SD 19.5 and 72.4, SD 21.5, both p <.001), indicating that PTSD was associated with 104

105 impairment in other important areas of functioning, fulfilling criteria F of the DSM-IV classification for a PTSD diagnosis Comparisons of psychological variables between PTSD and non-ptsd groups Patients who went on to develop PTSD at 12 months reported significantly greater depression in hospital, higher hostility scores, and were more likely to have a history of depression (Table 4.3). None of the other psychological variables assessed at baseline differed between the diagnostic groups (significance level adjusted to p <.007, by means of Bonferroni correction). Patients who scored above threshold at 36 months had higher depression, hostility and acute stress disorder symptoms at baseline (all at p <.007) (data not shown). TABLE 4.3 PSYCHOLOGICAL VARIABLES BY PTSD CASENESS 12 months Non-PTSD PTSD p-value Posttraumatic stress symptoms 9.75 (6.90) (6.95) <.001* BDI 7.07 (6.10) (11.57) =.001* Anxiety 5.66 (3.86) 6.06 (4.58) =.691 Acute stress disorder symptoms (8.91) (13.25) =.066 Hostility (7.51) (8.83) <.001* Subjective pain 7.35 (8.71) 2.23 (1.74) =.044 Type D (positive) 61.1% 30.1% =.008 History of depression (yes) 15% 38.5% =.003* * Sig. at p <.007 by means of Bonferroni correction. 105

106 4.1.5 Predictors of posttraumatic stress symptom severity at 12 months Associations between demographic, clinical, and psychological variables and posttraumatic symptom intensity at 12 months are summarized in Table 4.4. Posttraumatic symptom intensity was greater in younger patients (r = -.138, p =.045), those from ethnic minorities (F (1,211) = 4.51, p =.035, η 2 =.021) and more socially deprived patients (F (2,210) = 5.31, p =.006). None of the clinical measures obtained at the time of hospital admission predicted later posttraumatic symptoms, but recurrent cardiac symptoms were strongly associated with 12 month posttraumatic stress symptoms (F (1,198) = 25.7, p <.001, η 2 =.115). All psychological variables (BDI measured in hospital, anxiety, acute stress disorder symptoms, hostility, Type D personality, subjective pain ratings and a history of clinical depression) were associated with higher posttraumatic stress symptom scores at 12 months (all p <.001, pain ratings p <.05). Although subjective pain and acute stress symptoms were predictive of posttraumatic stress symptoms in this sample at 3 months (Whitehead et al., 2006), it was not possible to include patients subjective pain ratings and acute stress disorder symptoms in multivariate analyses in the present study due to the large amount of missing data on these variables. These measures were not introduced until midway through the study and therefore the inclusion of these variables for multivariate analyses would reduce the overall number of cases included in the models, and could thereby cause failure to detect other significant relationships. 106

107 TABLE 4.4 PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS 12 month posttraumatic symptoms 36 month posttraumatic symptoms Means (SD) or Means (SD) or r P r P Demographic factors Age Gender Men (10.59) (10.83).347 Women (9.69) (8.23) Education None (11.00) (10.94).142 Up to O-level (10.89) 9.28 (9.18) A-level (8.91) (9.94) Ethnicity White (9.85) (9.56).003 Other (12.79) (12.33) Social deprivation Low (8.75) (9.04).246 Medium (11.42) (10.64) High (11.29) (11.96) Marital status Married (10.02) (9.57).933 Not married (11.06) (11.53) Clinical factors ACS type STEMI (10.26) (10.87).308 NSTEMI/UN (10.80) (9.98) GRACE score Previous MI Yes (11.65) (11.76).026 No (10.31) (9.94) Recurrence of cardiac symptoms Psychosocial factors BDI (following admission) Yes (12.37) (11.60) <.001 No (8.94) 9.65 (8.89).674 < <.001 Anxiety.294 < Acute stress disorder symptoms (n =154/130) Subjective pain (n =120/101).442 < < <.001 Hostility.354 < Type D Yes (11.56) < (10.58).002 No 9.91 (8.21) 9.72 (9.66) History of depression Yes (12.32) < (11.38).001 No (9.56) (9.62) 107

108 Examination of the intercorrelations (table 4.5) between the psychological variables measured show that symptoms of depression shortly after ACS were significantly related to all other psychological variables. Anxiety in hospital was only associated with depression and acute stress symptoms. The acute stress disorder measure was significantly associated with all other measures with the exception of a history of depression. Hostility showed a relationship with the other trait measure, type D, as well as with depression and acute stress disorder symptoms. Subjective pain was largely unrelated to the other psychological variables, however, an association was found between this variable and the acute stress disorder measure and depression in hospital. Type D personality was associated only with a history of depression and depression in hospital. These correlations suggest that although there are several different psychological predictors of posttraumatic stress, it is important to note that many of these are not independent on one another. TABLE 4.5 CORRELATIONS BETWEEN PSYCHOLOGICAL PREDICTOR VARIABLES Psychological BDI Anxiety Acute Hostility Subjective Type variable Stress Pain D disorder symptoms Anxiety r.352 p.000 Acute stress r disorder symptoms p Hostility r p Subjective Pain r p Type D r p History of r Depression p Correlations between psychological variables measured at baseline among patients who completed 12-month posttraumatic stress symptom follow up. 108

109 The multiple regression on 12 month posttraumatic symptoms indicated that demographic and clinical factors together accounted for 21.5% of the variance, with social deprivation and recurrence of cardiac symptoms being the strongest independent predictors (Table 4.6, Model 1). The psychological factors (Model 2, step 2) accounted for an additional 29.8% of the variance, and the complete model explained 51.3% of the variance in 12 month posttraumatic symptoms. The BDI measured in hospital was the only independent predictor from among the psychological measures. None of the variables included in the final model showed multicollinearity according to variance inflation factor and tolerance values. TABLE 4.6 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT 12 MONTHS Model 1 Model 2 Standardised regression coefficients P Standardised regression coefficients P Age Gender Ethnicity Social deprivation GRACE score Recurrence of cardiac symptoms.269 < R².215 BDI (hospital).471 <.001 Anxiety Hostility Type D History of depression R ².513 Figure 4.1 Illustrates the relationship between depression in hospital and later posttraumatic stress symptom severity, where patients were grouped according to the cut-off scores for none, mild-to-moderate, moderate-to-severe and severe symptoms as described in chapter 3, section Scores are shown for both 109

110 unadjusted and adjusted means (co-variates; age, gender, ethnicity, social deprivation, GRACE score, recurrence of symptoms). A strong linear relationship is apparent. FIGURE 4.1 THE RELATIONSHIP BETWEEN DEPRESSION SCORES AT BASELINE AND 12 MONTH POSTTRAUMATIC STRESS SYMPTOMS Predictors of posttraumatic stress symptoms at 36 months post ACS Posttraumatic symptom intensity at 36 months was greater in patients from ethnic minorities (F (1,177) = 8.87, p =.003, η 2 =.048), patients who had previously had an MI (F (1,177) = 5.04, p =.026, η 2 =.028) and those experiencing recurrent cardiac symptoms (F (1,1556) = 13.98, p <.001, η 2 =.083), as shown in Table 4.4. None of the other clinical measures or demographic characteristics obtained at baseline were predictive of later posttraumatic stress symptoms. All psychological variables (BDI measured in hospital, anxiety, acute stress disorder symptoms, hostility, Type D personality, subjective pain ratings and a history of clinical depression) were 110

111 strongly associated with higher posttraumatic stress symptom scores (all p < 0.05) (Table 4.4). The multiple regression analyses indicated that demographic, clinical factors and posttraumatic symptoms at 12 months together accounted for 61.2% of the variance in 36 month posttraumatic symptom intensity. This was primarily due to the strong influence of 12 month posttraumatic symptoms in the model, this variable being the only independent predictor in this step (Table 4.7, Model 1). Nevertheless, psychological factors measured in hospital (Model 2, step 2) accounted for an additional 2.3% of the variance, and the complete model explained 63.5% of the variance in 36 month posttraumatic symptoms. The BDI measured in hospital was the only independent predictor from among the psychological measures. None of the variables included in the final model showed multicollinearity according to variance inflation factor and tolerance values. Because the inclusion of the 12 month symptom level in the predictive model for 36 month posttraumatic symptom intensity could possibly obscure other interesting associations, I repeated the multiple regression on 36 month posttraumatic symptom intensity omitting the 12 month symptoms. In this model, one additional factor, ethnicity ( =.159, p =.037), made an independent contribution to the final model but the BDI measured in hospital continued to be the only independent predictor among the psychological measures ( =.602, p <.001) (data not shown). 111

112 TABLE 4.7 MULTIVARIATE PREDICTORS OF POSTTRAUMATIC STRESS SYMPTOMS AT 36 MONTHS Model 1 Model 2 Standardised regression coefficients P Standardised regression coefficients P Age Gender Ethnicity GRACE score Previous MI Recurrence of cardiac symptoms Posttraumatic symptoms (12 months).726 < <.001 R ².612 BDI (hospital) Anxiety Hostility Type D History of depression R ² Discussion This study investigated the prevalence and predictors of posttraumatic stress symptoms 12 and 36 months following admission for an acute coronary syndrome. The findings indicate that 12.2% of patients at 12 months and 12.8% of patients at 36 months met criteria for PTSD. Other studies that examined the occurrence of PTSD in cardiac patients over shorter periods reported comparable rates (Spindler & Pedersen, 2005; Gander & von Känel, 2006; Shemesh et al., 2006; Ginzburg et al., 2003) (see Table 2.2, chapter 2). It has been suggested that MI related posttraumatic stress may comprise an acute reaction to a life-threatening event, and therefore may abate with time (Owen et al., 2001). Although one previous study showed that the prevalence of PTSD dropped by 40% between 4-6 weeks and 9 months (Pedersen et al., 2004), a recent review found no convincing evidence of diminishing PTSD 4-6 weeks to

113 months following a cardiac event (Gander & von Känel, 2006). I also found that the prevalence of posttraumatic stress symptoms had not diminished by 36 month follow up, but remained stable and comparable to levels recorded at earlier times. This result adds to the findings from studies of posttraumatic stress disorder from other traumatic experiences that symptoms persisting beyond 12 months may become chronic (Freedman et al., 1999). Whether it is appropriate to regard posttraumatic stress symptoms above a certain threshold to constitute PTSD as a diagnostic entity is open to debate. DSM-IV criteria specify not only that the person experiences or witnesses a traumatic event, but that the person s response involves intense fear, helplessness, or horror. In common with much other research on PTSD following MI or ACS (Bennett & Brooke, 1999; Chung et al., 2007; Ginzburg, 2006a; Doerfler et al., 1994), data were not systematically collected on these acute emotional responses in all patients at the time of admission (acute stress disorder measure). In the general PTSD literature, there are doubts about the value of criterion A2 as an indicator, provided A1 is fulfilled (Weathers & Keane, 2007). Additionally, only a minority of studies have included criterion F (the disturbance should cause clinically significant distress or impairment in social, occupational, or other important areas of functioning) as part of the diagnosis (Pedersen et al., 2003). Narrow et al (2002) have emphasised that an accurate understanding of clinical impact is required in order to plan treatment need. In this study, I assessed physical, social and emotional functioning using the SF36 health status measure, and found that ratings were markedly impaired in patients exceeding the threshold on the PSS-SR, suggesting that criterion F was fulfilled. More generally, the posttraumatic nature of some of the symptoms is open to question. As Kangas et al (2002) have pointed out in relation to PTSD following cancer, the stressor for patients following ACS is not in the past, since they are at increased risk for future events. Further, the stressor is triggered by an internal cardiac event rather than an external threat, so cannot be physically avoided in the same way 113

114 as other traumas. There may be ambiguity in the extent to which patients complete assessment instruments with respect to the index ACS, or take into account current health and symptomatology. For these reasons, the primary focus of this study was on severity of posttraumatic symptoms, rather than PTSD as a diagnosis. Consistent with other research (Kutz et al., 1994; Whitehead et al., 2006), the clinical severity of the cardiac event was not related to posttraumatic stress symptoms. In other words, cardiac disease severity and the extent of myocardial damage could not explain why some patients experienced greater posttraumatic symptoms post ACS than others. Recurrence of cardiac symptoms, however, strongly predicted posttraumatic stress symptoms at 12 months. These cardiac symptoms may have served as reminders of the traumatic event, activating re-experiencing symptoms and contributing to the persistence of posttraumatic stress symptoms. Unlike the situation when posttraumatic symptoms arise from external traumas such as natural disasters, war or violence inflicted by others, ACS involves internal trauma, and it may therefore be more difficult to avoid reminders of the ACS when interoceptive stimuli such as chest pain or shortness of breath are present to trigger memories of the event. Posttraumatic symptoms at 12 months were predicted by high social deprivation scores, while ethnic minority status was associated with symptom levels after 36 months (model omitting 12 posttraumatic symptom level). Low socioeconomic status is a consistent predictor of PTSD in trauma-exposed adults (Brewin et al., 2000). Low socioeconomic status is also associated with depression (Lorant et al., 2003), so it is interesting in this study that the association between social deprivation and 12 month posttraumatic symptoms was no longer significant after depressed mood in hospital was included in the model (Table 4.6). This suggests that depressed mood may have mediated the social deprivation effect. Ethnic minority status is a predictor of PTSD following external traumatic events (Brewin et al., 2000), and ethnicity predicted PTSD following acute MI in a study in Israel (Kutz et al., 1994). 114

115 Previous research indicates that posttraumatic stress symptoms in cardiac patients are associated with higher levels of anxiety, depression and anger (Doerfler & Paraskos, 2004). A number of studies indicate that posttraumatic symptoms are influenced by negative affective states during and following the acute event (Bennett & Brooke, 1999; Pedersen et al., 2003; Bennett et al., 2001; Whitehead et al., 2006). In line with my predictions, I found depressed mood in hospital, anxiety, hostility and type D personality, were predictive of posttraumatic symptoms. Depressed mood was the strongest predictor of posttraumatic symptoms at 12 and 36 months following ACS. This finding is not surprising considering the high levels co-morbidity of depression and posttraumatic stress (Ginzburg, 2006a). Type D (distressed) personality has previously been found to be associated with posttraumatic symptoms at 3 months following ACS (Pedersen & Denollet, 2004). The present findings show that although type D was associated with symptom severity at both 12 and 36 months, it did not independently predict posttraumatic stress symptoms at 12 months after depressed mood had been taken into account. Hostility has been found to be an independent predictor of posttraumatic stress 3 months after ACS (Whitehead et al., 2006), as well as other traumatic events (Brewin et al., 2000). I replicated this finding at 12 and 36 months, when hostility was a significant predictor after age, gender, GRACE risk score, ethnicity, social deprivation and recurrence of symptoms had been taken into account (data not shown). However, it did not survive as an independent predictor when depressed mood in hospital was included in the model (tables 4.6 and 4.7). Similarly, I found anxiety to be an independent predictor of posttraumatic stress at 12 months, though not at 36 months, after age, gender, GRACE risk score, ethnicity, social deprivation and recurrence of symptoms had been taken into account. However, once depression was included in the model at 12 months, anxiety no longer made an independent contribution to the prediction of posttraumatic symptoms. 115

116 One explanation may be that hostility and depressed mood were positively correlated (r =.394, p <.001), and this shared variance reduced the independent influence of hostility. This may also be the reason why Type D personality and anxiety did not survive as independent predictors. Posttraumatic stress symptoms were highly stable between 12 and 36 months, with a correlation of.79. Consequently, 12 month symptom level was the major predictor of symptoms at 36 months. Nonetheless, depressed mood following hospitalization remained a significant independent predictor of posttraumatic symptom severity at 36 months, further reinforcing the long-term significance of early emotional responses to acute cardiac events. Despite the stability in average posttraumatic symptom levels, a small proportion of patients who did not fulfill criteria for PTSD at 12 months moved into the positive category at 36 months, and a similar number improved. Unfortunately, the numbers were insufficient to carry out robust analyses of predictors of this pattern. These effects merit a larger investigation, since it may be important from the clinical perspective that some patients deteriorate over the long-term in posttraumatic symptomatology. In particular, previous MI was associated with worsening of symptoms from 12 to 36 months, and some research (non-medical trauma) suggest that exposure to previous trauma is a vulnerability factor for developing PTSD following new traumatic events (Breslau et al., 1999), in particular among those who went on to develop PTSD previously (Breslau et al., 2008) Strengths and limitations The strengths of this study include its prospective design, relatively large sample, and the length of the follow up period. There are however a number of limitations. Posttraumatic symptoms were assessed with self-report measures rather than gold standard clinical interviews. A full diagnostic interview was not possible within the confines of the study, and although a well standardized measure was used, results 116

117 might have been different with diagnosis by interview. However, it is interesting that in a recent study that diagnosed PTSD by clinical interview, the prevalence was 9.4%, similar to the level observed in this study (Wiedemar et al., 2008). Second, the dropout rate raises the concern of selective attrition. Non-completers were of lower socioeconomic background, were more likely to be unmarried but had lower scores on the GRACE index, suggesting that they had experienced less severe ACS. I do not know how many of these individuals experienced posttraumatic symptoms, so whether their inclusion would have increased or reduced the prevalence of severe PTSD remains unclear. It is conceivable that posttraumatic symptom levels would have been lower in mild cases, leading to an overestimation of the incidence of PTSD. Third, I did not measure other factors that are thought to relate to the development of posttraumatic symptoms following ACS, including dissociative responses (Ginzburg, 2006b) and personality traits (Chung et al., 2007). Finally, the sample was predominantly male and of white ethnic origin, so it cannot be assumed that the present findings generalize to the entire population of patients after ACS. Additionally, patients recruited for the study were selected on the basis of being able to identify a distinct time of onset of symptoms, and patients with co-morbidities that potentially influenced cardiac enzyme levels or mood were excluded. In practice, this meant that few patients with co-morbidities participated, so the sample was not representative of ACS patients in general Summary Despite these limitations, the present findings show that posttraumatic symptoms are a problem for some patients who experience an ACS and that we can begin to predict who is at increased risk for this condition. Although the experience of posttraumatic stress in patients after major cardiac events is low in comparison with traumatic events such as war, natural disasters or assault, it is nevertheless associated 117

118 with significant psychological disability and poorer quality of life. Elevated symptom levels, regardless of whether they meet criteria for diagnosis, are related to distress and poor functioning (Doerfler et al., 2005). The prevalence rates of 12.2% and 12.8%, at 12 and 36 months suggest that symptoms may become chronic. Assessment of posttraumatic stress symptoms may be helpful in identifying patients who experience psychological distress. Appropriate treatment for these patients is important as they are more likely to experience other psychosocial impairments as well as increased risk of recurrent cardiac events. 118

119 CHAPTER 5. Methodology TRACE study 5.1 Introduction and hypotheses Acute post-acs emotional responses and their relationship with short (2 week) and long term (six months) posttraumatic stress reactions Emotional reactions identified early after an acute cardiac event can be predictive of long-term psychosocial adaptation. In my first study, described in chapters 3 and 4, the significance of early emotional reactions for persistence of longer term posttraumatic stress symptoms was assessed. Approximately 12% of patients meet diagnostic criteria for PTSD at both 12 and 36 months following admission for ACS, suggesting a chronic course of symptoms. These analyses further supported the importance of early emotional distress, in particular depressive symptoms experienced in the immediate aftermath of the acute event (independent of clinical and demographic variables), in the prediction of posttraumatic symptomatology up to three years later. In addition, those patients who reported experiencing recurrent cardiac symptoms also reported greater posttraumatic stress symptoms, suggesting that internal reminders of patients cardiac condition can contribute to the maintenance of posttraumatic symptoms. Building on the results reported in chapter 4, the first objective of this study was to assess patients acute emotional reactions to ACS and how these predict early (2 week) posttraumatic symptoms. Secondly, I aimed to establish the prevalence and severity of ACS related posttraumatic stress symptoms at six months follow up. Thirdly, I aimed to investigate the predictors of longer-term (six month) posttraumatic stress symptoms. Based on previous work and the results presented in chapter 4 the following hypotheses were addressed; 119

120 i) Negative emotional state during admission, in particular negative mood and acute distress in response to the acute cardiac event, will be associated with greater posttraumatic stress symptomatology shortly after hospital discharge. ii) Negative emotional state, in particular depressed mood, assessed shortly after hospital discharge for ACS will be predictive of posttraumatic stress symptoms at six months, independent of clinical and demographic variables. iii) Patients psychological disposition, in particular type D personality and hostility, will also be predictive of posttraumatic stress independent of clinical and demographic variables Introduction to illness representations Although most patients ultimately have successful adjustment following an acute cardiac event, the literature reviewed previously and the results presented in chapter 4 demonstrate that a significant minority have persistent psychosocial distress that can have negative consequences for physical and psychological well-being, reintegration into usual work and for social, leisure, sexual and domestic activities. There is relatively little known about the mechanisms underlying emotional reactions to acute medical trauma such as MI, with most studies in this area focusing on chronically ill populations. In addition to the importance of early emotional reactions as a consequence of the acute event in predicting later psychosocial adjustment, an individuals perception of their illness or condition could also have an effect. The concept of illness perceptions could provide a useful theoretical framework for exploring this process in ACS patients. 120

121 An individual s beliefs, thoughts, attributions, cognitive schemas and general attitudes all provide meaning to life events and contribute to emotional arousal. Beliefs structure meaning and affect emotion. Four cognitive or belief aspects have been proposed to have a central role in the development of PTSD: (1) the appraisal of the event that it is harmful; (2) general beliefs about personal vulnerability; (3) attempts to assign meaning to the event; and (4) beliefs about the amount of individual control (Parrot & Howes, 1991). One approach which may prove useful in understanding the development and persistence of posttraumatic stress following an ACS focuses on the patients own model of their illness. Leventhal and colleagues (1980) proposed a model of self-regulation in which individuals regulate both their emotional and behavioural reactions to illness based on symptoms attributed to the illness (identity), beliefs about what caused their illness (cause), their belief in the curability or controllability of the illness (cure/control), the perceived consequences (consequences) and the expected duration of the illness (time-line). The five components of the self-regulation model; identity, cause, cure/control, consequences and time-line represent an individuals own understanding or perception of a situation. It is important to take these into account when trying to understand health related outcomes, in particular because these often differ greatly from the cognitive models of patients health care professionals or from medical fact. It has been shown that the illness perceptions held by the patient can account for variations in emotional reactions to symptoms of physical disease Causal attributions and CHD There have been a number of studies investigating causal attributions of CHD. Causal attributions are the beliefs that people hold about the causes of their illness or condition. A wide range of causal attributions have been identified in cardiac patients, including beliefs about psychological causes (stress, overwork, etc), lifestyle (physical inactivity, smoking) and hereditary factors (Perkins-Porras et al., 2006). There are 121

122 strong associations between traditional risk factors, such as smoking, hypertension, obesity, and the causal attributions endorsed by CHD patients (Perkins-Porras et al., 2006). For example, patients who are overweight are more likely to attribute the cause of their cardiac problem to being overweight. Patients attributions have important implications for recovery. When someone experiences an acute cardiac event, identifying causes may give them a greater sense of predictability and control, thus aiding the coping process. Some studies suggest that illness perceptions are predictive of behaviour change following MI. Weinman and colleagues (2000) demonstrated that patients who rated poor health habits as the main cause of their MI were also more likely to make changes to their diet than were those who rated stress or family history as causal factors. In addition, causal attributions of poor health habits, by the spouses of MI patients, were predictive of increase in exercise levels at 6 months in the patients. However, in a more recent reanalysis of the data reported by Weinman and colleagues, French et al (2005) showed that these attributions were no longer associated with behaviour change when controlling for pre-mi behaviour. The reanalysis did nevertheless indicate that spousal attributions may be important in predicting patient behaviour change, in particular reductions in smoking. In the same paper, French et al also describe an additional study in which causal belief measures again did not predict behaviour change 6 months post the event. Some studies highlight gender differences in CHD related causal attributions. Aalto et al (2005) investigated illness perceptions and their correlates in CHD in a sample of 3130 men and women. The results showed that men were more likely to attribute their illness to internal and behavioural factors, whereas women more often saw their illness as a result of stress. Women in this study also reported more perceived CHD-related symptoms and more serious consequences of CHD disease than did men. These gender differences may be related to the general observation that women tend to report more psychological morbidity post MI than men (Brezinka & Kittel, 1996). Although severity of cardiac disease tends not to be related to later 122

123 emotional distress, such disease-related factors appear to exert influence on patients perceptions. For example in the study by Aalto and colleagues, disease severity was related to change in illness perceptions at one year follow up. In addition, stronger psychosocial resources (e.g. perceived competence, social support) were related to weaker illness identity, stronger belief in control/cure, and less severe perceived consequences Illness representations and post-mi recovery Much less research has been conducted focusing on illness perceptions and their role in the successful recovery following MI. There are some data from studies using the theoretical framework of illness perceptions. For example, Petrie and colleagues (1996) reported that illness representations were better predictors of return to work following MI than was severity of illness. In this sample of 143 first time MI patients, return to work within six weeks was predicted by the perception that the illness would last a short time and that it would have less grave consequences. Rehabilitation attendance was strongly predicted by patients stronger beliefs during admission that the illness could be cured or controlled. Patients who anticipated serious consequences [of their MI] were slower to regain social and domestic duties. These authors argued that the early emergence of such coherent illness representations suggests they may be largely formed by pre-mi information. In a sample of post-mi women, MacInnes (2005) found that at 3 months post the acute event, cardiac rehabilitation attendance was influenced by beliefs of a known cause and a higher level of perceived control over the illness. In this study, women most commonly endorsed stress as the causal attribution, and a belief in the illness being inevitable was more common among older women. These perceptions may have contributed to the lack of lifestyle changes reported in this sample. Cooper and colleagues (2007) assessed patient s beliefs about cardiac rehabilitation in particular 123

124 and demonstrated that those who attended were more likely to believe that cardiac rehabilitation was necessary and to understand its role compared with non-attenders. There was a non-significant trend suggesting that patients who expressed concerns about exercise or who reported practical barriers to attendance were less likely to attend. Earlier work has shown non-attenders to be more likely to hold misconceptions regarding rest and avoiding exerting themselves (Wyer et al., 2001). These studies demonstrating strong associations of illness representations and cardiac rehabilitation uptake suggest that screening patients perceptions, and intervening to modify beliefs, at an early stage may be valuable in increasing participation in such programs. Petrie at al (2002) demonstrated that a brief hospital intervention was successful in changing post-mi patients negative illness perceptions about their MI and resulted in improved recovery and reduced disability at 3 months follow up. 65 patients were randomized either to the intervention group or usual care (i.e. cardiac rehabilitation nurse in-hospital visits and standard MI educational material). The intervention consisted of three minute sessions conducted by a psychologist in hospital. Patients understanding of the physiology of MI, their causal beliefs, timeline and consequences were addressed during these sessions, as well as providing explanations for symptoms that can be attributable to MI. These authors found that before leaving hospital the intervention group had significantly modified their perceptions about the duration of their illness, the personal consequences on their life, and were more optimistic their illness could be cured or controlled than the control group. These findings were maintained at the 3 month follow up assessment. Patients in the intervention group also returned to work sooner than did the controls. There was a non-significant trend of increased rehabilitation attendance for intervention compared with control patients. These results support the usefulness of adopting the illness representation model as a theoretical framework within which in-patient rehabilitation efforts can be directed and evaluated. A recent study by Broadbent et al (2009a) further supports these findings. In a sample of 103 MI patients, the group that received 124

125 the three half-hour sessions and one half-hour session including the patient s spouse, had a significantly faster rate of return to work at six month follow up than did those in the control group (standard post-mi care). This study replicated the findings of Petrie et al (2002) increasing the generalizability of the intervention to the current broader definition of MI and to patients who also have experienced a previous MI. Others have found that illness perceptions are predictive of complications shortly after the acute cardiac event. Cherrington et al (2004) found that as illness representations became more negative, the odds of experiencing a cardiac complication in hospital increased by In other words, for each unit increase in the illness perception measure, the odds of a complication increased by 5%. In this study, neither anxiety nor depression added significantly to the prediction of recurrent complications whilst in hospital. However, the authors offer no explanation of the possible physiological mechanisms underlying this relationship The relationship between illness representations and post-mi depression and quality of life Among CAD patients, illness beliefs have predominantly been investigated in relation to their impact on health behaviours. The association with depression has received little attention. Although numerous associations with disease related factors and socio-demographic variables and depression have been established, the inability to modify these variables to improve outcome is problematic. Therefore, the identification of modifiable determinants of depression in CAD patients is important. Patients illness perceptions are also strongly related to global health status and quality of life. In particular, patients who associate a greater number of symptoms with their cardiac disease, perceive more severe consequences, and regard their illness as uncontrollable report poorer health status and quality of life (Aalto et al., 2006). 125

126 A recent study by Stafford and colleagues (2009) found an association between illness representations and depressive symptomatology in CAD patients at three and nine months following hospital admission. Stafford et al (2009) reported that negative illness beliefs, in particular more severe perceived consequences of the condition were predictive of higher levels of depression at three and nine months follow up. However, the relationship between illness representations and depression at nine months was no longer significant after the three month symptoms were controlled for, suggesting that the association observed at nine months might entirely reflect a crosssectional relationship between illness representations and depression at three months. In this study, positive illness beliefs were related to better quality of life at both time points, as well predicting the change in mental quality of life between the two assessment points. Another study found negative illness beliefs following MI was associated with new onset depression six or 12 months post the acute event (Dickens et al., 2008). In particular, beliefs that their illness would last a long time were associated with a 2.7 fold increased risk of developing new-onset depression. Anticipation that the heart condition could be controlled or cured was associated with half the incidence of depression. The cognitive model of depression proposes that negative thoughts and beliefs can predispose an individual to the development of depression (Kovacs & Beck, 1978). If in fact negative health beliefs do contribute to the development of depression after MI, they may be amenable to psychological interventions, which in turn could reduce depression incidence, and therefore, hold potential to improve medical outcomes. Women tend to experience greater depressive symptomatology following ACS than do men (Grace et al., 2005; Frasure-Smith et al., 1999). One way to address this gender difference in psychosocial adjustment may be through the examination of illness belief held and their relationship with depression. Grace et al (2005) found that women, compared with men, were more likely to attribute their cardiac condition to 126

127 causes beyond their control and to perceive CVD as a chronic, untreatable condition. Depressive symptomatology was associated with younger age, lower activity status and perceiving a chronic time course among women. For men, being non-white, reporting lower activity status, less exercise behaviour, perceiving a chronic course, greater consequences and lower treatability was associated with greater depressive symptomatology. In this study there was no significant difference in depressive symptoms between men and women. The explanation for this finding is unclear. Men were more likely to view their illness as within their personal control, and to view treatment as effective in controlling or curing their condition. This may have important ramifications for depressive symptomatology as previous research suggests that helplessness and external locus of control are associated with depression (Moser & Dracup, 1995). Murphy et al (1999) found that depression in the chronic condition rheumatoid arthritis was most strongly correlated with the negative illness perception components consequences, suggesting patients view their condition as serious, and control/cure, which suggest a belief in limited control over their illness. These effects are independent of disease severity. The findings that patients control beliefs are associated with depressed mood are in line with other studies measuring health locus of control, suggesting that perceived control may be conducive to psychological well being. Regaining a sense of control has been postulated to be a core process in the adjustment following an MI. Moser and Dracup (1995) found that when controlling for sociodemographic and clinical variables, only perceived control contributed significantly to the prediction of differences in psychosocial recovery. Patients with perceptions of high control at baseline had significantly lower anxiety, depression, hostility and psychosocial adjustment (total score) to illness at 6 month follow up, compared with those with low control. It is important to note that it is the perception of control that has been associated with positive outcomes, control need not be exercised nor real to be 127

128 effective (Litt, 1988). The findings presented in this section further support the designing and testing of brief interventions to optimize psychological well-being following acute cardiac events Posttraumatic stress and illness representations Illness perceptions are not rigid and may very well change over time as patients knowledge and experience of their illness changes. As patients recover, changes occur in physical ability, affect, social environment and comprehension of their illness, therefore it can be expected that illness perceptions will change accordingly. One recent study investigated the association between patients illness perceptions and posttraumatic stress symptoms following MI (Sheldrick et al., 2006). These authors found that PTSD levels varied over time. Posttraumatic symptoms were assessed in MI patients within 2 weeks of admission, then between 5 7 weeks, and finally weeks post admission. There was a non-significant increase in symptoms between baseline and the second assessment. However, symptoms significantly decreased between the second and third follow up. Increased emotional representations, decreased illness coherence, increased consequences and decreased treatment control beliefs measured at 5 to 7 weeks were predictive of posttraumatic stress at 11 to 14 weeks. The multiple regression model explained 62% of variance. The component of emotional representations was the strongest predictor of posttraumatic symptoms, accounting for approximately 47% of the total variance. These results suggest that the experience of PTSD following MI may be mediated by patients emotional responses to the trauma, their confidence in their understanding of the illness, their perception of its impact upon their lives and their confidence in subsequent medical treatment. These authors argue that through appropriate information provision it may be possible to reduce some of the distress experienced by MI patients. Through appropriate information particular illness beliefs could be targeted, such as 128

129 encouraging and increasing patients confidence in their knowledge and understanding of their condition, generating realistic expectations of the consequences and reassuring patients of the efficacy of treatment. I aimed to assess patients illness beliefs regarding their cardiac condition and how these relate to levels of posttraumatic stress at six months post ACS. The following hypothesis was tested; iv) Patients who hold more negative illness representations at time 2 will also report greater posttraumatic stress symptoms at six month follow up. In particular, beliefs that the condition will have more serious consequences, a lack of understanding of the condition, experiencing more negative emotional representations and lower control/cure beliefs at 2 weeks post admission for ACS will predict posttraumatic symptom severity at follow up Biological determinants of early emotional responses to ACS Cortisol Cortisol is a glucocorticoid hormone produced in the adrenal cortex both spontaneously and in response to stressors. Cortisol can be measured in blood, urine and saliva. Measurement of cortisol is an important tool in assessing HPA axis function. Cortisol shows a natural diurnal pattern in healthy adults, which peaks at approximately minutes after waking, and subsequently decreases through the day, reaching the lowest levels at night and in the early hours of the morning before rising again. The sharp increase in cortisol levels post awakening is referred to as the cortisol awakening response (CAR). Exaggerated increases in cortisol after waking has been associated with chronic stress (Chida & Steptoe, 2009). Further, studies have 129

130 found a greater CAR to be related to depressive symptoms and reduced positive affect (Pruessner et al., 2003; Steptoe et al., 2007). The decline in cortisol levels following the CAR is referred to as the slope of decline, and is calculated as the difference between awakening levels and evening [bedtime] values. Total cortisol output over the day is measured as the area under the curve (AUC). A growing body of evidence suggest an association between HPA axis activity and coronary atherosclerosis. A prospective association between cortisol and future CHD has been documented in middle aged men (Smith et al., 2005), while acute cortisol elevation after ACS predicts adverse cardiac outcomes (Bain et al., 1989; Tenerz et al., 2003). Positive correlations between morning plasma cortisol levels and the degree of coronary artery disease (CAD) have been demonstrated (Troxler et al., 1977; Koertge et al., 2002; (Alevizaki et al., 2007). Though, others have failed to find a relationship between morning levels and number of diseased coronary vessels (e.g. Whitehead et al., 2007). Otte et al (2004) reported significantly increased total output levels of cortisol in patients with stable CAD compared with age and gender matched controls. One limitation of these studies is that neither single measures of morning cortisol, nor collection of 24-hour urinary cortisol, provide information on cortisol reactivity or daily profiles. In fact, these measures are unable to assess the diurnal slope. This is important as dysfunction of the HPA axis can take the form of a smaller decline in levels across the day, that is, a flatter slope. Although it is still unclear what the determinants or consequences of having a flatter cortisol profile are, it is generally considered a result of long-term HPA overstimulation (Nijm & Jonasson, 2009). Findings from a population based cross-sectional study, in which six salivary cortisol samples were collected over the course of one day, from awakening to bedtime, demonstrated an increased likelihood of any coronary calcification the flatter the slope throughout the day (Rosmond et al., 2003). Some reports also demonstrate associations between HPA dysfunction and clusters of recognised CAD risk factors. For example, a study by Rosmond and 130

131 Bjorntorp (2000) showed strong associations between low diurnal cortisol variability and a poor lunch-induced cortisol response and a cluster of established risk factors. Other studies suggest a relationship between subtle alterations in cortisol secretion and separate CAD risk factors such as smoking, abdominal adiposity, and hypertension (Gluck et al., 2004; Duclos et al., 2005; Rohleder & Kirschbaum, 2006; Wirtz et al., 2007). Overall, these studies suggest a role of HPA axis dysfunction in inflammatory disease activity. A flatter diurnal cortisol profile has been found in both preclinical and clinical CAD samples and the dysregulated cortisol secretion in CAD patients appear to be associated with a systemic inflammatory activity. HPA axis dysfunction may, thereby, have implications for CAD progress. A flatter slope across the day has also been found to be associated with depression (Weber et al., 2000). Depressed mood is associated with a blunting of the normal reduction in cortisol levels over the course of the evening (Kirschbaum & Hellhamner, 1989a). Whereas some findings suggest a relationship between depression and elevated values in the morning (e.g. Yehuda et al., 1996), others report strong associations of evening levels with depression (Gold et al., 1988). There is a lack of evidence directly linking depression and cortisol in patients with CAD. HPA axis dysfunction, particularly hypercortisolaemia, may play a role in the pathogenesis and progression of CAD through their association with established physiological risk factors such as hypertension, hyperlipidaemia, and insuline resistance (Girod & Brotman, 2004). A study by von Kanel and colleagues (2007) showed independent associations of elevated morning cortisol levels and prothrombotic activity in a sample of women, however no relationship with mood was observed. A relationship between psychological factors and cortisol has been demonstrated by Whitehead and colleagues (2007) in sample of 72 ACS patients. Salivary cortisol was assessed over a 24 hour period within five days of admission, while patients still remained in the cardiac ward. Cortisol was not associated with severity of ACS or underlying CAD, nor with depression scores (BDI). However, the 131

132 CAR was positively associated with type-d personality independently of age, and BMI. Molloy et al (2008) assessed the association of cortisol and type-d personality in same sample of patients 4 months post ACS. These authors observed a typical diurnal pattern of cortisol, with low levels in the evening and high levels early in the day. However, type-d was not associated with CAR, but a higher total cortisol output was higher in type-d compared with non type-d ACS patients. These results contrast those reported from earlier analyses of these data (Whithead et al., 2007). However, in the earlier report, cortisol was assessed in-hospital, where numerous factors may have influenced the results. For example, patients were in an unfamiliar environment, where sleep was likely to be disrupted, and wake up times tend to be early. There is evidence that basal cortisol levels are increased in hospital settings (Scheer et al., 2002), which might partly explain the differing pattern of results in the study by Molloy and colleagues (2008). This study also showed that concurrent depressed mood as measured by the BDI was not associated with cortisol. This finding is in contrast to previous work, which has demonstrated links between depression and 24-hour urinary cortisol in patients with stable CAD (Otte et al., 2004). Bhattacharyya et al (2008) observed a relationship between cortisol levels and depression in CHD patients. These authors found a flatter cortisol slope over the day in more depressed patients with CHD, but no relationship was observed between cortisol slope and depression in patients without CHD. There is accumulating evidence linking cortisol to the pathophysiological processes contributing the cardiovascular disease (Girod & Brotman, 2004; Brotman et al., 2007). Heightened cortisol output is partly responsible for vascular endothelial dysfunction in depressed individuals (Broadley et al., 2005), and this effect is reversed by inhibition of cortisol (Broadley et al., 2006). These results suggest that endothelial dysfunction may play a role in the increased CHD risk associated with depression. There is emerging evidence for a distinct pathophysiology of PTSD. The notion that negative emotional states such as depression and PTSD may confer an 132

133 atherogenic risk suggests involvement of behavioural factors (e.g. smoking, nonadherence to medications) as well as biological mechanisms (e.g. inflammation, sympathetic overactivity, and endocrine dysfunction) in underpinning pathways (see chapter 2, section 2.9). However, teasing apart the unique contributions of these intertwined variables remains a challenge. Whereas the literature on depression and cortisol tends to suggest hypercortisolaemia, with a blunted diurnal profile, results from studies of PTSD and cortisol are more varied. Clearly, more work needs to be undertaken to address these difference in results, and to further our understanding of the role of cortisol in the development of posttraumatic stress. Cortisol in relation to posttraumatic stress in a sample of MI patients was investigated in a recent study by von Känel and colleagues (in press). In bi-variate correlation analyses, no significant associations were observed between PTSD or posttraumatic symptoms and cortisol levels. However, patients with PTSD had significantly lower mean cortisol levels than patients without PTSD when controlling for depressive symptoms. These results suggest that depression may disguise associations between posttraumatic symptoms and cortisol profiles. For the TRACE study, cortisol was assessed in saliva. Salivary cortisol has proven to be an accurate reflection of plasma cortisol, and this type of salivary sampling is a non-invasive and convenient method for assessing diurnal patterns in a naturalistic ambulatory setting (Kirschbaum & Hellhamner, 1989b). However it is important to note that responses can be affected by e.g. gender, smoking, use of oral contraceptive and a range of other factors. For example, some studies show trait negative affect is associated with a higher overall total cortisol concentration and a greater morning rise in men, even after controlling for wakening levels (Polk et al., 2005). This collection method relies on participants themselves taking the samples, and reporting the time of assessment, resulting in a significant loss of control available in the laboratory setting in order to gain ecological validity. 133

134 Mood state in the immediate aftermath of admission for ACS is an important indicator of future quality of life, morbidity and mortality. One aim of the TRACE study was to assess the relationship between psychological status and HPA-axis function by assessing salivary cortisol over the course of one single day in ACS patients shortly after discharge and again at 12 months. There are relatively few studies investigating the cortisol profiles of patients in the immediate aftermath of ACS. The analyses presented in chapter 6 were undertaken to investigate the biological underpinnings of early distress and mood in patients two weeks following admission for ACS. Based on previous literature I hypothesized; v) Patients who report greater posttraumatic stress symptoms will show evidence of cortisol dysregulation following hospital discharge Heart rate variability Heart rate variability (HRV) refers to the beat-to-beat (or R-R) alterations in heart rate. HRV is a useful non-invasive tool for the assessment of cardiac autonomic control. Resting heart rate (HR) is one index of autonomic imbalance (Levy et al., 1990) and a large positive dose response relationship between resting HR and all cause mortality has been observed previously (Habib, 1997). In particular, elevated HR has been shown to predict future CHD, independent of other established CHD risk factors (Kannel et al., 1987). One particularly salient reason for the increasing interest in the measurement of HRV stems from its ability to predict survival after heart attacks. Numerous prospective studies have demonstrated that reduced HRV predicts sudden death in patients with MI, independent of other prognostic indicators such as left ventricular ejection fraction 134

135 (LVEF 1 ) (e.g. Cripps et al., 1991; Bigger et al., 1993; Quintana et al., 1997). Moreover, findings from population studies suggest that decreased HRV has predictive value for mortality also among healthy adults, and is a well established risk factor for arrhythmic events, cardiovascular disease, and sudden cardiac death (Simpson & Wicks, 1988). Several studies also suggest a link between negative emotions (such as anxiety, depression and hostility) and reduced HRV. Kawachi et al (1995) reported a cross-sectional association between anxiety and reduced HRV in 581 men. Offerhaus (1980) observed lower HRV in individuals who were highly anxious. Yeragani et al. (e.g., 1991; 1993) have published a series of reports indicating reduced HRV (using both time domain and spectral measures) among DSM-III diagnosed panic disorder and major depression patients. In turn, at least three prospective epidemiologic studies (Haines et al, 1987; Kawachi et al., 1994a; Kawachi et al., 1994b), and one casecrossover study (Mittleman et al., 1995) have suggested a relationship between high levels of anxiety and risk of CHD. Sloan et al (1994) reported reduced high-frequency power among 33 healthy volunteers who scored high on the Cooke-Medley Hostility scale. Carney et al (2001) demonstrated that depressed mood is associated with reduced HRV in patients following MI. The association between negative affect and reduced HRV may thus provide a potential mechanism linking chronic stress to disease outcomes (e.g., risk of CHD). There are three distinct rhythms identified within the beat-to-beat modulation of the heart; a high, low, and a very low frequency component (Task force, 1996). * High Frequency band (HF): range between 0.15 and 0.4 Hz. HF is driven by respiration and appears to derive mainly from vagal (parasympathetic) tone. 1 Ejection fraction refers to the fraction of blood pumped out of the ventricle with each heart beat. The ejection fraction is one of the most important predictors of prognosis; those with significantly reduced ejection fractions typically have poorer prognoses. Ejection fractions typically range between 50% and 65% in healthy individuals, lower levels indicate ventricular dysfunction. 135

136 * Low Frequency band (LF): between 0.04 and 0.15 Hz. LF is mediated by both parasympathetic and sympathetic activity. * Very Low Frequency band (VLF): band between and 0.04 Hz. The origin of VLF is not well known, but it had been attributed to thermal regulation of the body's internal systems. The ratio between HF and LF can be measured, and the higher the ratio, the greater the vagal tone. LF and HF are strongly related and it is postulated that LF power reflects substantial parasympathetic influence (Wang et al., 2005). Originally, HRV was assessed manually from calculation of the mean R-R interval and its standard deviation measured on short-term (e.g., 5 minute) electrocardiograms. The smaller the standard deviation in R-R intervals, the lower is the HRV. There are many different types of arithmetic manipulations of R-R intervals, and time domain measures include standard deviation of normal mean R-R interval obtained over 24 hour holter recordings (called SDNN index) and the root-mean square of the difference of successive R-R intervals (the RMSSD index). The various methods of expressing HRV are potentially equivalent, with no evidence that one method is superior to another, providing measurement windows are 5 minutes or longer (Bigger et al., 1992a, 1992b). The measurement of HRV has been standardised (Task Force, 1996). A number of studies have found post trauma assessed heart rate to predict subsequent development of PTSD (see chapter 2, section 2.9.2). One explanation suggests that the development of PTSD may be facilitated by an atypical biological response (such as heart rate) in the immediate aftermath of a traumatic event, which in turn leads to a maladaptive psychological state. In this study, patients heart rate and HRV were assessed at the time 2 home interview. Based on previous literature I hypothesized that; 136

137 vi) HRV at 2 weeks post ACS will be reduced in those patients exhibiting greater posttraumatic stress responses to the acute cardiac event The relationship between posttraumatic stress responses and post ACS adaptation Chronic conditions, such as CHD, often involve complicated treatment regimens or medications and lifestyle adjustments. One of the challenges these patients may encounter is finding a balance between the demands and restrictions posed by their cardiac condition on the one hand and the various challenges and demands of everyday life on the other. As survival rates following ACS have increased, more and more patients live with the consequences of such conditions. Post-ACS patients are encouraged to facilitate their recovery by engaging in diverse lifestyle modifications such as smoking cessation, dietary change, weight loss and increased exercise. Improvements of health behaviours are associated with improved prospects for recovery following ACS (Daubenmier et al., 2007; Pischke et al., 2008). One aspect of recovery involves cardiac rehabilitation. Participation in comprehensive cardiac rehabilitation programmes reduces cardiac mortality by 26% (Jolliffe et al., 2004), all cause mortality by 13% and non-fatal MI by 38% (Clark et al., 2005). As reviewed in chapter 2, past research has demonstrated that ACS related posttraumatic stress is associated with a range of adverse consequences such as increased smoking and alcohol intake, poor adherence to medication regimens and reduced participation in rehabilitation programmes, lower social support and reduced quality of life. In addition, many of these outcomes have also been identified as independent risk factors for cardiac prognosis. The aim of this section is to establish post ACS adjustment and the influence of posttraumatic stress symptomatology on recovery behaviours. Based on past research I hypothesized that; 137

138 vii) Higher levels of posttraumatic symptoms reported 2 weeks following discharge from hospital will have a negative influence on health behaviours and quality of life at six months Influence of partner distress on patient posttraumatic stress reactions Many of the traumatic aspects of an ACS are not experienced by the patient alone. Family members and friends may feel great concern and even intense anxiety at the possibility of losing a significant other. For those who are responsible for the care and wellbeing of the patient (often a spouse), the anxiety experienced might even reach the traumatic levels associated with PTSD. Previous research has found that partners of ACS patients experience substantial distress, and examination of affect in cardiac couples have revealed that partners tend to be more anxious, distressed and depressed than patients (Bennett & Connell, 1999; Conway et al., 2008; Mayou et al., 1978; Moore, 1994; Moser & Dracup., 2004; Rose et al., 1996). This difference has been found to persist beyond hospitalisation and may also impact upon the patient s level of depression and experience suggesting emotional contagion effects (Conway et al., 2008, Moser & Dracup, 2004). High anxiety and depression in partners may increase anxiety and depression experienced by the patient, and may negatively influence provision of social support from the partner to the patient, thereby imposing greater emotional demands upon the patient. Most research of couple distress following cardiac events has focused on a female partner, and findings suggest the increased distress observed among partners may be an artefact of gender. Though, there is some limited evidence suggesting that these effects may be independent of gender (Moser & Dracup, 2004). Based on this literature I hypothesized that; 138

139 viii) Partners of the ACS patients will show high levels of distress, in particular anxiety, depression and posttraumatic stress symptoms and this will in turn negatively influence patients emotional recovery. 5.2 Study design The TRACE study is a prospective longitudinal study incorporating four assessment time points conducted over the course of one year following admission for ACS (fig. 5.1). Time 1 assessment was conducted in-hospital within two days of admission, Time 2 interviews were conducted approximately 10 to 12 days post discharge, Time 3 follow-up assessment was conducted at six months post admission and Time 4 follow-up was completed 12 months following the initial hospital admission. For the purpose of this thesis, only data from time 1, 2 and 3 are included. FIGURE 5.1 TRACE STUDY DESIGN 139