Parsing Depression From Fatigue in Patients with Cancer Using the Fatigue Symptom Inventory

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1 52 Journal of Pain and Symptom Management Vol. 42 No. 1 July 2011 Original Article Parsing Depression From Fatigue in Patients with Cancer Using the Fatigue Symptom Inventory Lara Traeger, PhD, Ilana M. Braun, MD, Joseph A. Greer, PhD, Jennifer S. Temel, MD, Barbara Cashavelly, MSN, RN, AOCN, and William F. Pirl, MD, MPH The Center for Psychiatric Oncology and Behavioral Sciences (L.T., J.A.G., J.S.T., B.C., W.F.P.), Massachusetts General Hospital; and Psychosocial Oncology and Psychiatric Services (I.M.B.), Dana-Farber Cancer Institute, Boston, Massachusetts, USA Abstract Context. A central aim in the management of cancer-related fatigue (CRF) is to identify treatable causes, such as depression. However, CRF and depression symptoms overlap and frequently co-occur, complicating diagnostic assessment. Objectives. As cancer-related symptoms have been associated with more functional impairment among patients who are depressed, this study tested the ratio of fatigue interference to fatigue severity as a method for identifying depression cases. Patients who reported that interference was greater than severity were expected to show higher rates of depression as measured by self-report instrument or structured interview. Methods. A secondary analysis was conducted using data from patients who were attending a hospital thoracic oncology clinic and who completed the Fatigue Symptom Inventory (FSI) and Hospital Anxiety and Depression Scale (Sample 1, n ¼ 86). Analyses were then replicated in a sample of diverse cancer patients who completed the FSI and a structured clinical interview for depression on presentation to a CRF clinic at the same hospital (Sample 2, n ¼ 39). Results. Receiver operating curve analyses supported use of the FSI interference/severity ratio in distinguishing depression cases and noncases (area under the curve: Sample 1 ¼ 0.84, 95% confidence interval [CI] 0.74e0.94; Sample 2 ¼ 0.87, 95% CI 0.76e0.99). With sensitivity and specificity weighted equally, the optimal cutoff was $1.0 in Sample 1 (sensitivity ¼ 62.5%, specificity ¼ 91.4%) and Sample 2 (sensitivity ¼ 90.9%, specificity ¼ 85.7%). Conclusion. A fatigue score pattern in which interference was greater than or equal to severity predicted depression in two patient samples. This ratio may be useful for brief initial screening of depression in the context of fatigue. J Pain Symptom Manage 2011;42:52e59. Ó 2011 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved. Address correspondence to: Lara Traeger, PhD, Massachusetts General Hospital, Behavioral Medicine Service, One Bowdoin Square, 7th Floor, Boston, MA 02114, USA. ltraeger@partners.org Ó 2011 U.S. Cancer Pain Relief Committee Published by Elsevier Inc. All rights reserved. Accepted for publication: October 17, /$ - see front matter doi: /j.jpainsymman

2 Vol. 42 No. 1 July 2011 Cancer-Related Fatigue and Depression 53 Key Words Cancer, depression, fatigue, Fatigue Symptom Inventory, quality of life Introduction Cancer-related fatigue (CRF) is estimated to occur in 14%e91% of individuals with cancer, with prevalence rates depending on assessment method and sample characteristics. 1 CRF is a complex syndrome that includes medical and psychological determinants and is more disabling and less likely to be reduced by rest relative to fatigue in healthy individuals. 2e4 Although CRF has been shown to vary by tumor factors and, to some extent, by type of anticancer treatment, it also correlates strongly with psychological distress. 5,6 Patients consider fatigue to be one of the most distressing somatic symptoms, beyond pain or nausea. 7 A central goal in the management of CRF is to identify underlying treatable causes, such as major depressive disorder (MDD). 8 Like CRF, MDD occurs commonly in patients with cancer. 9e12 Although evidence supports that these two conditions can present as distinct entities in cancer patients, 13,14 symptom overlap (e.g., anergia, sleep disturbance, poor concentration, and low mood) complicates diagnosis and treatment planning. 8,15,16 Distinguishing MDD in cancer patients who present with fatigue is critical because both conditions may differ substantially with respect to treatment. Currently, CRF management includes treatments for modifiable medical factors, such as erythropoietin for anemia or physical activity for deconditioning, 3 and there is some evidence supporting the use of cognitivebehavioral therapy. 17 MDD treatment strategies in cancer typically include the use of antidepressants and/or psychotherapy. However, although antidepressant medications may help alleviate symptoms of depression in cancer, they have not been shown to provide benefit in relieving comorbid fatigue. 18 Moreover, some antidepressants carry side effects that include sleep disturbance, as well as gastrointestinal distress, sexual dysfunction, and lowering of the seizure threshold. In the absence of depression, these effects may unnecessarily exacerbate fatigue and negatively impact quality of life. Therefore, detecting the presence or the absence of MDD in the context of fatigue is needed to determine an optimal course of treatment for patients. Cognitive symptoms such as hopelessness and feelings of worthlessness/guilt may help clinicians to distinguish depression in the context of fatigue. However, a recent survey of U.S. oncologists identified several factors that impede disclosure and comprehensive assessment of depression symptoms, including limited time and staff resources, competing health priorities, mental health stigma, and provider uncertainty. 19 Consequently, few providers are using recommended screening instruments for psychological distress. 19 More work is needed to identify effective and efficient ways to increase initial screening for depression in the context of fatigue that can be incorporated into current medical practices, to help inform accurate diagnosis and treatment planning. To help screen for depression in the context of fatigue, the present study explored whether fatigue patterns may differ between oncology patients who meet or do not meet criteria for depression. MDD is associated with marked impairment in daily functioning across a variety of medical conditions. 20e23 Specifically in cancer, depression has been associated with greater physical symptom bother, functional impairment, and disability, 24e28 suggesting that patients with depression may have more difficulty managing cancer-related symptoms. Based on this prior work, we hypothesized that oncology patients with depression would report higher levels of fatigue interference than those with the same level of severity but no depression. This pattern of fatigue could be operationalized within individuals as a ratio of interference to severity. We used the Fatigue Symptom Inventory (FSI), 29 a self-report questionnaire that measures both severity and interference on daily functioning to assess fatigue patterns. The FSI is a brief measure that can be administered and scored quickly and easily in clinical settings. We investigated whether the FSI interference/severity ratio may be useful as a brief screen for MDD in

3 54 Traeger et al. Vol. 42 No. 1 July 2011 the context of fatigue, to increase awareness of the need for further psychiatric evaluation. Methods The present study analyzed two separate data sets. We first tested our hypothesis using data from a larger study of fatigue measurement among patients presenting to a thoracic oncology clinic in an academic cancer center (Sample 1). In this sample, depression was assessed using a brief validated self-report instrument. We then aimed to replicate findings in a sample of patients presenting specifically for evaluation of fatigue at a CRF clinic in the same center (Sample 2). In this sample, depression was assessed using a structured clinical interview. This two-sample approach allowed us to first explore the usefulness of the FSI interference/severity ratio in predicting probable depression cases using a common brief depression instrument in a general oncology clinic. Significant findings in support of the ratio were then used to justify conducting a comprehensive chart review to replicate our analyses in a specialized CRF clinic setting, using a gold standard measure of MDD. Participants Sample 1 was drawn from a larger study of fatigue assessment among ambulatory thoracic oncology patients (n ¼ 100). Primary study details and clinic patient characteristics have been previously published. 30 Between October and December 2004, the study recruited patients attending the thoracic oncology clinic at the Massachusetts General Hospital. All patients regardless of thoracic malignancy type, disease stage, or illness duration were eligible. Patients also were required to read and comprehend psychosocial measures in English. Internal review board approval was obtained before study initiation, and all participants signed informed consent. Participants were then invited to complete a battery of psychosocial questionnaires at a single time point. Among participants (n ¼ 100), 86 completed the FSI 24 and the Hospital Anxiety and Depression Scale (HADS) 31 and were, therefore, included in the current analyses. Sample 2 was drawn from patients presenting to the Massachusetts General Hospital CRF Clinic, a specialized clinic for evaluation and management of fatigue (n ¼ 44). As part of initial clinic consultation, psychiatrists administer both the FSI and the MDD module of the Structured Clinical Interview for DSM- IV Axis I Disorders, Clinician Version (SCID- CV) 32 at a single time point. New patients who are able to read and comprehend the measures in English complete the assessment, with the exception of those experiencing severe somnolence, marked cognitive impairment, or agitation. Because the assessment constitutes typical clinic care, this portion of the study was approved by the internal review board as exempt from monitoring. Among participants assessed between September 2005 and April 2006 and between October 2006 and January 2008, five of 44 patients had indeterminate SCID-CV results, and a remaining sample of 39 patients was included in the current analyses. Measures Fatigue. The FSI 29 is a 14-item self-report measure that comprises several subscales, including a four-item severity scale and a seven-item interference-in-functioning scale. Items refer to fatigue experiences in the past week and are measured on 10-point scales ranging from not fatigued at all or no interference to as fatigued as I could be or extreme interference. The interference subscale assesses the impact of fatigue on multiple quality-of-life domains, including general activities and social relationships. The FSI has shown good psychometric properties in diverse cancer patient samples. 29,33,34 The current analyses used mean scores on the interference and severity subscales. For each participant, an interference/severity ratio also was calculated (interference O severity). Depression. The HADS 31 is a self-report questionnaire that assesses depression and anxiety symptom severity during the past seven days, with each item measured on a four-point Likert-type scale. The seven-item depression subscale has been used extensively in clinical oncology research settings. 35e38 This subscale is particularly suited for patients with cancer because it does not include somatic items, such as fatigue, which may be present regardless of depression. 31 Depression scores range from zero to 21. Sample 1 analyses used a score

4 Vol. 42 No. 1 July 2011 Cancer-Related Fatigue and Depression 55 $8 to identify probable cases of MDD, based on studies from diverse cancer groups The SCID-IV was developed to identify psychiatric diagnoses according to DSM-IV criteria. This structured interview is considered the gold standard for assessing psychiatric diagnoses in research studies. 42,43 Sample 2 analyses used data from the MDD module of the SCID-CV 32 to identify the presence or the absence of MDD. To meet criteria for current MDD, a participant must have endorsed at least five depressive symptoms that had persisted for the past two weeks or longer, with at least one symptom being depressed mood or anhedonia. Data Analysis Relationships among fatigue interference, fatigue severity, and depression were first assessed using the FSI and the HADS depression scores in the sample of patients presenting to the ambulatory thoracic oncology clinic (Sample 1). Independent samples t-tests were used to evaluate whether participants who screened positive (HADS score $8) or negative (HADS score <8) for probable MDD differed on mean FSI interference and severity scores. An FSI interference/severity ratio was then calculated for each participant. We hypothesized an optimal FSI ratio value of >1, greater interference than severity, to predict cases of probable MDD. This ratio allowed us to capture all participants who reported a fatigue interference score that was higher than their fatigue severity score. A receiver operating curve (ROC) analysis was conducted to test our assumptions. Specifically, the area under the ROC curve was used to identify the performance of the FSI ratio, where 0.5 ¼ same as chance and 1.0 ¼ ability to distinguish between all MDD cases and noncases. Results also were used to identify the specificity, sensitivity, and positive predictive value of different FSI ratio cutoffs in distinguishing between depression cases and noncases. These three factors help to characterize the accuracy of a classification method. Sensitivity refers to the proportion of depression cases that were accurately identified (i.e., true positive/[true positive þ false negative]). Specificity refers to the proportion of cases without depression that were accurately identified (true negative/[true negative þ false positive]). Positive predictive value reflects the proportion of FSI-identified depression cases that were actual cases (i.e., true positive/total positive). In the present study, sensitivity and specificity were weighted equally ([sensitivity þ specificity]/2) in determining the optimal cutoff value. This approach was selected at the cost of maximal sensitivity (i.e., missing some cases), with the purpose of identifying the best ratio to help practices allocate limited psychiatric consultation resources. The analyses were replicated to extend findings in a sample of oncology patients presenting to the CRF Clinic, using SCID- CV criteria to identify MDD cases (Sample 2). Results of all statistical analyses in the present study were evaluated at a significance level of P < Results Sample 1: Thoracic Oncology Clinic Patients Among 86 thoracic oncology clinic patients who completed the FSI and the HADS depression subscale, 18.6% (16/86) met criteria for probable depression (i.e., HADS score $8). Mean FSI severity and interference scores were 3.25 (standard deviation [SD] 2.14) and 2.23 (SD 2.30), respectively. FSI severity shared a strong linear association with FSI interference (r ¼ 0.76, P < 0.001), which did not vary significantly by depression status. Participants with probable depression reported significantly higher scores than patients without probable depression on both FSI severity (M depressed ¼ 5.17 [SD 1.66]; not depressed ¼ 2.81 [SD 2.00]; t(84) ¼ 4.39, P < 0.001) and FSI interference (M depressed ¼ 5.28 [SD 1.94]; not depressed ¼ 1.54 [SD 1.75]; t(84) ¼ 7.58, P < 0.001). When the FSI ratio was used to identify probable depression cases, the area under the ROC was 0.84 (95% confidence interval [CI] 0.74e0.94), supporting use of the ratio to distinguish between cases and noncases. When sensitivity and specificity were weighted equally, results indicated that the FSI ratio $1.0 was the optimal cutoff value. Classification statistics are summarized in Table 1. Sensitivity and specificity of this cutoff to detect HADS-defined probable depression were 62.5% (10/16) and

5 56 Traeger et al. Vol. 42 No. 1 July 2011 Table 1 Use of the FSI 24 Interference/Severity Ratio to Classify Depression Cases as Measured by Self-Report Screening Instrument or Structured Interview FSI Interference/ Severity Ratio Performance Sample 1 Sample 2 (HADS) a (SCID) b Area under the 0.84 (0.74e0.94) 0.87 (0.76e0.99) ROC (95% CI) Optimal cutoff c $1.0 $1.0 Sensitivity (%) Specificity (%) Positive predictive value (%) a Sample 1: The HADS 26 was used to assess probable depression (score $8) in thoracic oncology clinic patients (n ¼ 86). b Sample 2: The SCID-CV 27 was used to diagnose MDD in patients attending initial evaluation at a CRF clinic (n ¼ 39). c Sensitivity and specificity were weighted equally in determining the optimal cutoff value. 91.4% (64/70), respectively. Positive predictive value was 62.5% (10/16). Sample 2: CRF Clinic Patients To extend findings from Sample 1, analyses were replicated in a subsequent sample of 39 diverse cancer patients who presented for initial evaluation at the CRF clinic and who completed the FSI and the MDD module of the SCID as part of the evaluation. Participants were men (61.5%) and women (38.5%) who were approximately 57.3 years of age (SD 12.5) and who represented diverse cancer diagnoses: gastrointestinal cancer (33.3%), lung cancer (12.8%), hematological malignancy (10.3%), head and neck cancer (7.7%), central nervous system tumor (7.7%), and other cancer types that included only one to two individuals per type (28.2%). Participants included 61.5% with metastatic disease. Among Sample 2 participants, 28.2% (11/39) met SCID criteria for MDD. Mean FSI severity and interference scores were 5.29 (SD 2.25) and 4.84 (SD 2.74), respectively. FSI severity shared a strong linear association with FSI interference (r ¼ 0.77, P < 0.001), which was comparable to the association observed in Sample 1 and which did not vary significantly by depression status. Participants who met MDD criteria reported significantly higher scores than patients who did not meet MDD criteria on both FSI severity (M depressed ¼ 6.49 [SD 1.79]; not depressed ¼ 4.82 [SD 2.27]; t(37) ¼ 2.18, P ¼ 0.036) and FSI interference (M depressed ¼ 7.53 [SD 1.72]; not depressed ¼ 3.78 [SD 2.32]; t(37) ¼ 4.85, P < 0.001). When the FSI ratio was used to identify depression cases, the area under the ROC was 0.87 (95% CI 0.76e0.99), which supported the use of the ratio to distinguish between cases and noncases in this sample. When sensitivity and specificity were weighted equally, results indicated that the FSI ratio $1.0 was the optimal cutoff value. Sensitivity and specificity of this ratio to detect MDD cases were 90.9% (10/11) and 85.7% (24/28), respectively. Positive predictive value was 71.4% (10/14; see Table 1). Discussion Although the diagnosis of MDD presents challenges in cancer patients with fatigue, current findings suggest that the FSI 29 may be a useful tool in alerting clinicians to the need for further psychiatric evaluation. The FSI is a brief self-report instrument that assesses multiple dimensions of fatigue, including severity and interference on daily functioning. Results indicated that a ratio of FSI interference/ severity was useful in detecting depression cases, and that a ratio $1.0 was the optimal cutoff for distinguishing between cases and noncases when sensitivity and specificity were weighted equally. The current findings were identified in a sample of patients who were attending an ambulatory thoracic oncology clinic and who were assessed for probable depression using a brief validated self-report instrument (the HADS). 31 Findings were then extended to a subsequent sample of diverse oncology patients who were specifically attending a CRF clinic for evaluation and who completed the SCID-CV, 32 a gold standard assessment, with a staff psychiatrist. The FSI interference/severity ratio showed higher sensitivity and positive predictive value in predicting depression cases in CRF clinic patients as identified by the SCID (90.9% and 71.4%) relative to those in thoracic clinic patients based on the HADS (62.5% and 62.5%), whereas specificity was similar across the two studies (85.7% vs. 91.4%). As the FSI is brief and easy to administer and score, it may be useful as an instrument to identify possible cases for more in-depth

6 Vol. 42 No. 1 July 2011 Cancer-Related Fatigue and Depression 57 MDD structured evaluation. This screening may help to optimize mental health resources in oncology practices for which such resources are already limited. The proposed use of the FSI is novel and is based on findings that cancer patients with depression report elevated functional impairment. 24e28 MDD is a leading cause of disability, and it is not surprising that this disorder may exacerbate decrements in functioning caused by cancer and treatment side effects. The indication that depressed patients may report elevated fatigue interference relative to severity suggests worse functioning for these patients. Alternatively, findings may also reflect the impact of depressed cognitions on patient perceptions of fatigue. In other work, emerging evidence suggests that in some cases, co-occurring depression and fatigue have a shared biological etiology. 44 Further work is needed to examine current findings in the context of shared mechanisms and bidirectional relationships of depression and fatigue in cancer, with a focus on treatment implications. The FSI interference/severity ratio demonstrated adequate specificity and sensitivity in detecting MDD in two cancer samples, suggesting that it may be a useful initial screen for this disorder, but that it is not a replacement for careful clinical interview. Furthermore, findings should be interpreted with consideration for a number of limitations. The first analysis drew from a population of ambulatory patients with thoracic cancer, limiting generalizability to other cancer groups. The second analysis extended results to a heterogeneous cancer group and also used standardized diagnostic interviews to assess MDD. These results appeared to corroborate and strengthen the initial findings. However, in this second analysis, the sample size was relatively small and the degree to which results may generalize to patients who do not present for CRF evaluation is uncertain. It is also possible that for patients with CRF, fatigue symptoms may have impacted depression assessment using either the HADS or the SCID. The absence of somatic items such as fatigue (HADS) and the use of careful clinical interview (SCID) may have helped to minimize this risk. In clinical settings, the FSI interference/ severity ratio shows promise as an initial screen for MDD in the context of fatigue in oncology care settings. This method addresses several barriers to depression screening that have been reported by oncology providers. 19 For one, given limited time and competing health demands during clinic visits, the FSI may expedite initial screening for depression and fatigue simultaneously. Furthermore, this brief tool may help to reduce provider uncertainty about screening for distress. 19 It is also possible that the FSI s focus on physical symptoms and functioning may reduce the perceived threat and stigma of psychiatric evaluation among both patients and providers. Research with larger cancer patient samples is needed to replicate the current findings and to determine their stability across tumor stages and in patients with or without referral for CRF. More work is also needed to determine the feasibility of incorporating the FSI in clinic practice. Guidelines for comprehensive cancer care highlight the importance of monitoring for psychological distress. However, there is evidence that both depression and fatigue remain underdetected and undertreated in practice settings. 19,45e47 Importantly, the benefits of using this brief tool depend in part on time, incentive, and resources to integrate symptom screening into cancer care settings. Disclosures and Acknowledgments No funding was received for this study, and the authors declare no conflicts of interest. References 1. Lawrence DP, Kupelnick B, Miller K, Devine D, Lau J. Evidence report on the occurrence, assessment and treatment of fatigue in cancer patients. J Natl Cancer Inst Monogr 2004;32:40e Jacobsen PB. Assessment of fatigue in cancer patients. J Natl Cancer Inst Monogr 2004;32:93e Mock V. Evidence-based treatment for cancerrelated fatigue. J Natl Cancer Inst Monogr 2004; 32:112e Ng AV, Alt CA, Gore EM. Fatigue. In: Feuerstein M, ed. Handbook of cancer survivorship. New York: Springer, 2007: 133e Hwang SS, Chang VT, Rue M, Kasimis B. Multidimensional independent predictors of cancerrelated fatigue. J Pain Symptom Manage 2003;26: 604e614.

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