Trajectory and predictors of post-stroke depression among patients with newly diagnosed stroke: A prospective longitudinal study

Abstract

Background

Post-stroke depression (PSD) is the most prevalent neuropsychological disorder among stroke patients, affecting approximately one-third of stroke survivors at any one time after a stroke. We identified between-person associations between post-stroke depression trajectories across 3 timepoints and predictors affecting trajectory classification among stroke patients.

Methods

This is a prospective longitudinal study using a convenience sample of 119 participants from 2 tertiary hospitals from March 2022 to September 2022. Clinical assessments and data collection were performed at diagnosis (T1), 3 months (T2), and 6 months (T3) after diagnosis. The instruments were Demographic and Disease Information Sheet and PROMIS-Depression 8a. Data were analyzed using SPSS 27.0 for descriptive statistics, logistic regression, and the Mplus program for growth mixture model analysis.

Results

Two stroke survivors depression trajectory classes (Class 1, moderate level decreasing- [37.8 %], and Class 2, high level increasing- [62.2%]) were delineated. Class 1 experienced moderate depression post-stroke, with a smooth diminishing pattern at T2 and T3, while Class 2 had a higher baseline depressive score and a significant increase at T2 and T3. The best growth mixture model was Class 2 model (LMR, p=0.010, BLRT, p≤0.01, AIC=2611.934, BIC=2650.842, aBIC=2606.583, Entropy= 0.944). The logistic regression results revealed that Class 2 of depression trajectory had a significant association with a lower score on cognitive function (B=-5.29, 95%CI: -8.80, -1.78, p <0.05) compared with Class 1. The stroke type, marital status, and monthly income were predictors of the Class 2 depression trajectory group among stroke patients. Precisely, ischemic stroke is associated with lower risk of class 2 trajectory.

Conclusion

The trajectory of post-stroke depression changes over time. This research has the potential to serve as a foundation for the assessment of high-risk stroke patients, the development of precise management programs, the implementation of risk stratification, and the enhancement of prognosis.