Abstract

Introduction: Although routine screening for post-stroke depression is recommended by the AHA/ASA, the optimal screening method remains unclear. A high rate of false-positives is a potential issue of existing screening tools, as indicated in a recent meta-analysis. This study aims to examine patterns of depressive symptoms that may provide information to improve screening accuracy. Methods: The study sample consists of 613 first-ever stroke patients from the Brain Attack Surveillance in Corpus Christi project (2011-2015), a population-based stroke surveillance study. Depressive symptoms at 90 days after stroke were assessed by the 8-item Patient Health Questionnaire (PHQ-8). Symptom patterns were identified using latent class analysis. Depression status (PHQ-8≥10) by class was examined. Results: The sample was mainly composed of non-Hispanic Whites (38.3%) and Mexican Americans (57.3%), and equally distributed by sex. Mean age was 65.7 (SD=11.0). Prevalence of depression was 26.6% at 90 days after stroke. The best-fitting model yielded 4 classes (Figure). Notably, one class was characterized by symptoms that often overlap between depression and stroke, as opposed to psychological symptoms (termed overlapping symptom class). Percentages of participants classified as having depression were 84.1% in the psychological symptom class, 100.0% in the high-risk class, and 51.7% in the overlapping symptom class. The overlapping symptom class accounted for 28.8% of participants classified as having depression. Conclusion: Existing depression screening tools developed in non-stroke populations may have poor test characteristics in individuals with stroke. Additional research is needed to develop highly sensitive and specific screening tools to identify individuals with post-stroke depression in need of further evaluation and treatment. Understanding symptom patterns may increase our ability to personalize treatment for individual stroke survivors.

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