(PS6-B30) Cognitive and Functional Impairments Predict Depression Trajectories in Stroke Patients

Stroke is a potentially life-threatening neurological disease responsible for approximately 1 of every 19 deaths in the United States (Virani et al., 2020). The literature has called special attention to the impact of stroke among the geriatric population with a 10% higher chance of stroke than younger adults. About one third of stroke survivors exhibit depression and cognitive impairment (Towfighi et al., 2016). Further, over half stroke patients experience mild to moderate declines in activities of daily living (ADLs). Treatment for depression in patients with experience of stroke needs to take cognitive and functional impairments into consideration.

This study used data from the Health and Retirement Study to examine longitudinal trajectories of depression, and identify important cognitive and functional predictors for these trajectories. A total of 2,344 participants were included in the final analyses (55.97% female, 43.94% male, 0.09% unknown; 18.26% Black Americans, 78.3% White Americans, 0.18% identified as “Others” or unknown; 5.50% identified as Hispanic). The data included measures of depression, working, short-term, and long-term memory, vocabulary, mental status, and functional assessments such as ADLs and muscle movements. For each participant, we included four waves of assessments. Participants who did not survive for at least two years following stroke were not included in analyses.

First, we ran latent growth mixture modeling to identify subgroups of individuals that exhibit different developmental trajectories of depression across four time points. Based on various fit indices, we selected the 4-class solution as our best fitting model. The classes identified were resilience, recovery, chronic, and delay. Second, the least absolute shrinkage and selection operator (LASSO) logistic regression, a form of supervised machine learning suited for high-dimensional data, was built to predict resilience against all other trajectories, and predict chronic against all other trajectories. When predicting resilience against the other trajectories, the model performance was fair (AUC = .76), with important predictors (in order of variable importance) including large muscle activities, gross muscle activities, total asset, gender, years of education, age, ethnicity, vocabulary, working memory, race, and delayed memory recall. When predicting chronic depression against others, the model performance was fair again (AUC = .73), with important predictors (in order of variable importance) including large muscle activities, age, gross muscle activities, years of education, working memory, gender, ethnicity, total asset.

Depression in the aftermath of stroke result from a combination of socioeconomic, cognitive, and functional factors. In specific, socioeconomic disadvantage, pre-existing deficits in working memory, and large and gross muscle activities were most predictive of depression trajectories. These findings highlight the importance of incorporating cognitive remediation and physical rehabilitation when treating depression among patients with history of stroke.