PREDICTION OF LONG-TERM COGNITIVE DECLINE FOLLOWING

Transcription

1 PREDICTION OF LONG-TERM COGNITIVE DECLINE FOLLOWING POSTOPERATIVE DELIRIUM IN OLDER ADULTS Elizabeth E. Devore 1, Tamara G. Fong 2,3, Edward R. Marcantonio 2,4, Eva M. Schmitt 2, Thomas G. Travison 2,4, Richard N. Jones 2,5 *, Sharon K. Inouye 2,4 * *Co-senior authors 1 Channing Division of Network Medicine, Brigham and Women s Hospital and Harvard Medical School, Boston, Massachusetts 2 Aging Brain Center, Institute of Aging Research, Hebrew SeniorLife, Boston, Massachusetts 3 Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts 4 Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts 5 Departments of Psychiatry and Human Behavior and Neurology, Brown University Warren Alpert Medical School, Providence, Rhode Island Corresponding author: Elizabeth E. Devore, 181 Longwood Avenue, Room 448, Boston, Massachusetts 02115; phone: ; fax: ; nheed@channing.harvard.edu Word count: Abstract (248), Text (4,630) This is the author s manuscript which was subsequently submitted to and accepted for publication. J Gerontol A Biol Sci Med Sci. 2017; 72(12):

2 ABSTRACT Background: Increasing evidence suggests that postoperative delirium may result in long-term cognitive decline among older adults. Risk factors for such cognitive decline are unknown. Methods: We studied 126 older participants without delirium or dementia upon entering the Successful AGing After Elective Surgery (SAGES) study, who developed postoperative delirium and completed repeated cognitive assessments (up to 36 months of follow up). Pre-surgical factors were assessed preoperatively and divided into nine groupings of related factors ( domains ). Delirium was evaluated at baseline and daily during hospitalization using the Confusion Assessment Method diagnostic algorithm, and cognitive function was assessed using a neuropsychological battery and the Informant Questionnaire for Cognitive Decline in the Elderly (IQCODE) at baseline and six-month intervals over three years. Linear regression was used to examine associations between potential risk factors and rate of long-term cognitive decline over time. A domain-specific and then overall selection method based on adjusted R 2 values was used to identify explanatory factors for the outcome. Results: The General Cognitive Performance (GCP) score (combining all neuropsychological test scores), IQCODE score, and living alone were significantly associated with long-term cognitive decline. GCP score explained the most variation in rate of cognitive decline (13%), and six additional factors IQCODE score, cognitive independent activities of daily living impairment, living alone, cerebrovascular disease, Charlson comorbidity index score, and exhaustion level in combination explained 32% of variation in this outcome. Conclusions: Global cognitive performance was most strongly associated with long-term cognitive decline following delirium. Pre-surgical factors may substantially predict this outcome.

3 Keywords: Risk factors, cognition, elective surgery

4 INTRODUCTION Delirium, an acute confusional state, is a common condition affecting up to 50% of older adults in the hospital, with serious potential consequences including cognitive and functional impairment, institutionalization, and mortality(1). Previous studies have indicated that individuals with delirium following surgery often experience cognitive impairment, although few studies have assessed cognitive function beyond six to twelve months postoperatively(2); still, emerging evidence suggests that some individuals never return to their previous cognitive level(2), and instead exhibit accelerated cognitive decline over the longer term(3). In the SAGES study, we previously reported that individuals had linear trajectories of cognitive decline over postoperative months two to 36, and these trajectories were significantly steeper among participants who developed delirium compared to those who did not(3). The etiology of delirium associated with long-term cognitive sequelae is unclear. Multiple risk factors have been identified for delirium(4), and validated prediction models have been developed to target high-risk individuals for intervention(5-8). Moreover, intervention programs such as the Hospital Elder Life Program and proactive geriatric consultation have successfully targeted modifiable risk factors (e.g., sleep and mobilization), with 30-40% reductions in delirium among older hospitalized patients(9, 10). In the absence of delirium, there are several established risk factors for cognitive decline in older adults (e.g., age and education), although most of these factors are not modifiable(11). However, risk factors specific for delirium associated with cognitive decline have not yet been identified. Exploring such factors is a necessary first step toward understanding the etiology of delirium associated with long-term cognitive decline, and identifying potential therapeutic targets for intervention. This paper specifically examines whether pre-surgical factors predict the rate of long-term cognitive decline

5 among older persons without dementia who developed postoperative delirium. Identifying such factors could be useful in selecting high-risk individuals for targeted prevention or randomized trials aimed at stalling cognitive decline following delirium. METHODS Study sample The SAGES study is an ongoing prospective cohort study of older adults undergoing elective major non-cardiac surgery. The study design and methods have been described previously(12). Briefly, eligible participants were age 70 years and older, English speaking, scheduled to undergo elective surgery at one of two Harvard-affiliated academic medical centers and with an anticipated length of stay of at least three days. Eligible surgical procedures were: total hip or knee replacement, lumbar, cervical, or sacral laminectomy, lower extremity arterial bypass surgery, open abdominal aortic aneurysm repair, and colectomy. Exclusion criteria included evidence of dementia, delirium, hospitalization within three months, terminal condition, legal blindness, severe deafness, history of schizophrenia or psychosis, and history of alcohol abuse. A total of 566 patients were enrolled between June 18, 2010 and August 8, 2013, and six individuals with possible dementia were excluded after a detailed adjudication process described previously(12), leaving 560 cohort participants. Written informed consent was obtained from all participants according to procedures approved by the institutional review boards of Beth Israel Deaconess Medical Center and Brigham and Women s Hospital, the two study hospitals, and Hebrew SeniorLife, the study coordinating center, all located in Boston, Massachusetts. In this paper, we focused on 134 participants who developed postoperative delirium and had cognitive data during follow up.

6 Assessment of pre-surgical factors An initial home interview was conducted to gather detailed information on health and functioning an average of two weeks prior to hospitalization for scheduled surgery. Basic demographic, medical, and lifestyle information was obtained, and well-validated assessments of cognitive, physical, and mental function were administered (see Supplement A for details). The five components of the Fried frailty index (unintentional weight loss, exhaustion, low physical activity, low grip strength, and slow timed walk) were assessed(13), and a blood sample was collected from which apolipoprotein (APOE) 4 genotype and C-reactive protein (CRP) levels were analyzed. A trained physician conducted medical record reviews after participants were discharged from the hospital, which provided additional information on pre-surgical factors. Assessment of delirium Beginning with the first postoperative day, delirium symptoms were assessed daily during hospitalization using brief cognitive tests(12, 14), the Delirium Symptom Interview(15), and acute changes in mental status reported by family and nurses. Delirium diagnosis was based on the Confusion Assessment Method (CAM) diagnostic algorithm(16), a widely used and wellvalidated assessment tool with very high sensitivity, specificity, and inter-rater reliability(17, 18). Delirium symptoms were also obtained from medical records using a validated delirium assessment(19, 20) with adjudication of cases by an expert delirium panel. For this study, a combined approach based on either CAM or medical record assessment of delirium (and used in prior studies) was utilized to identify incident delirium(20).

7 Assessment of cognitive function Cognitive function was assessed at the home interview, and repeated at one, two, and six months after hospital discharge, and at six-month intervals thereafter up to 36 months following discharge. The neuropsychological battery evaluated cognitive domains putatively most affected by delirium(12, 21) (see Supplement B for details). We combined all test scores into a weighted composite summary measure, the General Cognitive Performance (GCP) score, using previously published methods(22); this score was calibrated to a nationally representative sample of older adults(23), where the mean for persons aged 70 years is expected to be 50, with a standard deviation of 10(21). The GCP score is sensitive to change with minimal floor and ceiling effects(21, 22), and has been applied in previous studies(3, 24-26). For longitudinal GCP values, we applied a previously described method of correction to these scores to account for learning effects over time(27-29). As previously reported in this cohort, the pattern of GCP scores over time indicated cognitive decline at postoperative month one, recovery of cognitive function above baseline at month two, and gradual cognitive decline beginning at month two and dropping below baseline over months twelve to 36; this pattern was more pronounced among participants who developed postoperative delirium compared to those who did not(3). Loss to follow up was minimal due to death (7%) and withdrawal from the study (5%), and non-differential with respect to delirium status; therefore, 89% of eligible participants had complete data on the GCP score over time(3). Statistical analysis We divided pre-surgical factors into nine related groupings or domains: demographics (age, sex, race, education, and living arrangement), lifestyle factors (smoking status, alcohol

8 intake, and socioeconomic status), cognitive function (baseline GCP score and IQCODE score), physical function (ADL and IADL impairments), mental health/quality of life (GDS scale and SF-12 composite and subscale scores), sensory function (hearing and vision impairment), frailty (Fried frailty index components), medical factors (surgery type, CCI score, cardiovascular disease, peripheral vascular disease, diabetes, and cerebrovascular disease), and biomarkers (APOE 4 genotype and CRP level). Our outcome of interest was the rate of cognitive decline over three years of follow up, which was estimated previously for each participant based on mixed effects regression models with random effects for baseline and change over time; this slope was estimated beginning at postoperative month two because, on average, participants started a linear trajectory of gradual cognitive decline at that time(3). We used simple linear regression to estimate mean differences in rates of cognitive decline (and 95% confidence intervals [CIs]) for each unit increase in continuous predictors and for each level of a categorical predictor compared to a chosen reference level. We divided mean differences associated with continuous predictors by twice the standard deviation of the predictor to make the scaling similar to that of binary predictors(30). Next, for each domain of pre-surgical factors, we used an adjusted R 2 -based selection method to determine an order of variable selection into successive linear regression models (with rate of cognitive decline as the outcome). We selected variables into the model by maximizing the adjusted R 2 value (i.e., total proportion of variation explained in the outcome corrected for the number of variables in the model), and we obtained total R 2 values (i.e., total proportion of outcome variation explained by all variables in the model) for each successive model as well. For each domain, we retained variables if they increased the adjusted R 2 value of the model, or if they entered the model first when no factors met this threshold, and these variables were considered for our final model. Finally, we evaluated

9 all variables retained from domain-specific models (with age and sex forced into the model) using the selection procedure described above and obtained adjusted and total R 2 values. The subset of variables that increased the adjusted R 2 value of this model constituted our final model. We evaluated the normality of studentized residuals in our final linear regression model using the Shapiro-Wilk test)(31), and identified potential outliers based on graphical methods and the absolute values of these residuals (threshold of two)(ref); we also detected influential observations based on the Cook s Distance (threshold of four divided by the square root of the sample size)(ref). The entire selection process was repeated if outliers or influential points were identified and these participants were excluded. For each model, participants with complete information on contributing variables were included; as previously reported, there is little missing data in this sample(32). All statistical analyses were conducted in SAS version 9.3. RESULTS Of the 134 participants with postoperative delirium, we initially identified five individuals who were potential outliers and three individuals with influential data in our final model, and subsequently excluded these participants from further analyses. In Table 1, selected pre-surgical characteristics are described among 552 participants in the overall SAGES cohort and separately for 126 participants who developed postoperative delirium. Overall, the cohort had an average age of 76.8 years at baseline; it was 60% female and 8% non-white. Participants with postoperative delirium were slightly older, had lower GCP scores, and more often had multiple comorbidities (as indicated by the Charlson comorbidity index score) compared to the

10 overall cohort. Other differences were relatively small comparing participants with delirium to the whole cohort. In univariate models, worse cognitive function prior to surgery (Domain 3) was significantly associated with faster cognitive decline over follow up; specifically, participants with lower GCP scores and higher IQCODE scores at baseline had greater slopes of cognitive decline (mean differences: 0.47 points/year [95% CI: 0.24, 0.71] per half-standard deviation higher GCP score at pre-operative assessment, and points/year [95% CI: -0.61, -0.08] per half-standard deviation higher IQCODE score) (Supplemental Table 1). Living alone (a component of Domain 1) was related to significantly faster cognitive decline (mean difference: points/year, 95% CI: -0.59, -0.05), and other variables (i.e., any cognitive independent activities of daily living [IADL], hearing impairment, vision impairment, weight loss, and cerebrovascular disease) appeared to be related to faster cognitive decline, but these associations did not reach statistical significance. When we examined the domain-specific contribution of pre-surgical factors to rates of cognitive decline (Supplemental Table 2), baseline cognitive performance (Domain 3) explained the most variation in rates of cognitive decline: GCP scores accounted for 11.1% and IQCODE scores contributed another 3.7%, and both variables improved the explanatory power of the model based on increased adjusted R 2 values upon entry of these variables to the model. Medical factors (Domain 8) explained 7.8% of variation in cognitive decline, with the Charlson comorbidity index score, diabetes, cerebrovascular disease, and cardiovascular disease explaining 3.1%, 1.8%, 1.3%, and 0.9% of variation in cognitive decline, respectively; these variables improved the model based on their adjusted R 2 values. Demographic variables (Domain 1) explained 5.8% of variation, with the majority of this variation explained by living

11 alone (4.4%); this variable was the only one to increase the adjusted R 2 value of this domainspecific model. Frailty variables (Domain 7) explained 4.5% of variation in cognitive decline, with slow timed walk explaining 1.5% of variation and less exhaustion explaining 1.8%; both of these variables improved the model based on their adjusted R 2 values. Other domains contributed less to explaining variation in the outcome, and only had one variable that improved the explanatory power of the respective models. In our final step, baseline GCP score was found to explain the most variation in rate of cognitive decline (13.0%) when factors retained from each of the domain-specific models were considered for inclusion (in addition to age and sex, which were forced into the model and contributed 3.0% to explained variation) (Table 2). Living alone, less exhaustion, Charlson comorbidity index score, IQCODE score, cerebrovascular disease, and any cognitive IADL impairment contributed another 6.4%, 3.2%, 2.1%, 2.0%, 1.3%, and 1.0% to explaining variation in the outcome, respectively. Beyond age and sex, these seven factors GCP score, living alone, exhaustion level, Charlson comorbidity index score, IQCODE score, cerebrovascular disease, and cognitive IADL impairment improved the amount of explained variation in the outcome based on an increase in the adjusted R 2 value with variable entry into the model. The total amount of variation in rates of cognitive decline explained by these variables (i.e., total model R 2 value) was 31.8%. The residuals from this final model did not violate the assumption of normality for linear regression models (Shapiro-Wilk statistic, W=0.99, p=0.5). DISCUSSION We found that worse cognitive performance (GCP and IQCODE scores) and living alone prior to surgery were significantly associated with long-term cognitive decline in older

12 participants with postoperative delirium in SAGES. Baseline GCP score contributed most substantially to explained variation in rates of cognitive decline, and a total of seven factors GCP score, IQCODE score, cognitive IADL impairment, living alone, cerebrovascular disease, Charlson comorbidity index, and exhaustion level accounted for 32% of the variation in this outcome. These results suggest that pre-surgical factors may have important influences on longterm cognitive decline following postoperative delirium in older adults. Our findings serve to confirm and extend prior work. Baseline cognitive function has been previously demonstrated to be a strong risk factor for cognitive decline in older adults(11), and was also shown to be the dominant predictor of cognitive decline over time in a small study of community-dwelling older individuals (R 2 =37%)(33). This result is consistent with our finding that baseline GCP score is the main factor predicting long-term cognitive decline among older individuals with postoperative delirium, and additional baseline cognitive measures (i.e., impaired IQCODE score and cognitive IADL impairment) contribute to prediction as well. In addition, previous work in SAGES found that lower GCP score at baseline was linearly associated with greater risk of delirium (relative risk=2.0 for each half standard deviation decrease in GCP score)(34), and furthermore delirium was associated with steeper rates of cognitive decline over the three-year follow up(3). The present study provides additional insight by suggesting that lower baseline GCP scores predict greater rates of long-term cognitive decline among those who develop postoperative delirium, in a graded fashion that holds across the full range of baseline GCP scores in this sample. Prior epidemiologic studies have also demonstrated that cerebrovascular disease(35, 36) and living alone(37, 38) are important risk factors for cognitive decline in older individuals, and we identified these variables as significant predictors of cognitive decline in our sample of older

13 participants with delirium. Cerebrovascular disease has been recognized as increasing the risk of delirium as well(1), and brain injury from both types of events might cause worse deterioration in cognitive function over time. In contrast, living alone may result in less physical, social, and cognitive stimulation at home prior to surgery, leading to reduced cognitive reserve and greater vulnerability to decline following postoperative delirium. Other predictors that were identified in our analyses included the Charlson comorbidity index and exhaustion, although these variables appeared to be associated with cognitive decline in a counterintuitive direction (i.e., multiple comorbidities and more exhaustion were associated with less decline) in our sample of participants with delirium. These results are likely due to inherent limitations of our multivariable modeling approach in disentangling interrelated chronic disease factors when entered into our models simultaneously. Although this approach can generally identify true predictors, it can incorporate extraneous predictors into a final model(39); therefore, our findings should be interpreted with caution and need replication in future studies. Without replication, our individual predictors cannot be considered suitable for clinical applications. Clearly, additional studies are needed to evaluate pre-surgical factors that contribute to long-term cognitive decline following postoperative delirium, as well as the potential interplay of perioperative and postoperative factors in the prediction of this outcome. In recent studies, common neuropathologic factors associated with Alzheimer s disease, cerebrovascular disease, and Lewy-body disease predicted 41% of variation in rates of cognitive decline among older adults(40), and postmortem factors (e.g., indicators of neuronal density and neural tissue integrity) explained additional variation in this outcome(41, 42). In our study, the total percentage of explained variation was 32% based on a variety of clinical characteristics, but similar neuropathologic factors could potentially account for remaining variation in cognitive

14 decline. More studies are needed to explore the combination of clinical and neuropathologic factors that explain this variation. Major strengths of this study include: rigorous data collection, standardized delirium assessments, careful characterization of baseline cognition, repeated neuropsychological assessments, relatively long follow up, and careful optimization of the GCP score for analyses of cognitive decline. We also had a wide range of data available to explore potential risk factors at baseline of this study. However, our study has several limitations that should be considered. First, we have evaluated a subset of 126 participants who developed postoperative delirium in the SAGES cohort, and this modest sample size limits the power of our analyses. We may have missed important associations of interest, and the certainty of our estimates related to observed associations and model building is decreased; low power also increases the risk of identifying false positive results, which may have occurred in this study(43). Hence, these results will need to be replicated in future, larger studies. Second, we cannot rule out the possibility that some participants had preclinical dementia at baseline, despite our extensive efforts to exclude individuals with dementia from this cohort. For example, if some individuals had mild cognitive impairment at baseline, we may have overestimated the association between lower GCP score and faster cognitive decline in our sample; however, this is less likely to be driving the observed association because we found the association held across the full range of baseline cognitive scores even among those with the highest scores. Finally, our sample included participants who were aged 70 years, highly educated, and mostly white race, and therefore these results may be limited in their generalizability to younger or more diverse samples. In summary, we found pre-surgical factors that were associated with, and contributed to explained variation in, long-term cognitive decline among older participants who experienced

15 postoperative delirium. Baseline cognitive function contributed most substantially to explaining variation in the rate of cognitive decline, and a set of seven identified factors explained over onequarter of the variation in the outcome. Further research should evaluate the relative contribution of pre- and post-surgical factors to long-term cognitive decline following delirium, as well as the extent to which these factors predict such cognitive decline in older adults with and without postoperative delirium. FUNDING This work was supported by the National Institute on Aging (P01AG and K07AG to S.K.I.; R01AG to S.K.I./ R.N.J.; and R01AG030618, K24AG035075, and R01AG to E.R.M.). S.K.I. holds the Milton and Shirley F. Levy Family Chair.

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22 Table 1. Selected Baseline Characteristics of Participants, Including Those With Post-Operative Delirium, in the Successful AGing After Elective Surgery (SAGES) Study a All Participants (n=552) Participants with Delirium (n=126) Continuous variables Mean ± Standard deviation Age, in years 76.8 ± ± 4.8 Education, in years 14.8 ± ± 3.0 Leisure-time activity level, in Metabolic Equivalent Times 742 ± ± 1495 expended per week Global Cognitive Performance score, in standard units 56.9 ± ± 6.4 Informant Questionnaire for Cognitive Decline in the Elderly 3.1 ± ± 0.3 score Geriatric Depression Scale score, in points 2.7 ± ± 2.8 Categorical variables Percentages Female sex Non-white race 8 10 Living alone Current smoking weekly alcohol intake Any activities of daily living impairment 8 9 Any independent activities of daily living impairment Any cognitive independent activities of daily living impairment 6 10 Hearing impairment Surgery type Orthopedic Vascular 7 9 Gastrointestinal Charlson comorbidity index score, in points Cardiovascular disease Diabetes Cerebrovascular disease 6 8 a Data are derived from 552 participants, except there is missing information on alcohol intake (4 missing), Informant Questionnaire for Cognitive Decline in the Elderly score (12 missing), and Geriatric Depression Scale score (2 missing).

23 Table 2. Overall Contribution of Remaining Potential Risk Factors to Explained Variation in Cognitive Decline Among Participants With Post-Operative Delirium in the Successful AGing After Elective Surgery (SAGES) Study a,b Total R 2 value Change in total R 2 value Adjusted R 2 value, adding variables successively c Variables forced into the model 1. Age Sex Variables retained from Supplemental Table 2 3. Global Cognitive Performance score Living alone Frailty component 2: exhaustion Cerebrovascular disease Informant Questionnaire for Cognitive Decline in the Elderly score 8. Charlson comorbidity index score Any cognitive independent activities of daily living impairment Diabetes C-reactive protein level Cardiovascular disease Frailty component 5: slow timed walk Geriatric Depression Scale score Hearing impairment Smoking status a Data are derived from 126 participants, except there is missing information on Informant Questionnaire for Cognitive Decline in the Elderly score (2 missing), frailty component 5 (27 missing), and C-reactive protein level (3 missing). b Variables are numbered according to the order in which they were selected into the model. c Adjusted R 2 values represent the proportion of variation explained as variables are added successively to the final model, with a correction for the number of variables entered in the model. An increase in adjusted R 2 with addition of a variable to the model (shown in bold type) indicates the variable improved the explanatory power of the model, and a decrease in adjusted R 2 indicates the variable does not improve the explanatory power of the model.