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Predictive factors for remission in post-stroke depression: A Markov model cohort study
Wenwen Liang1, Yifan Fang1, Tianyi Li1
1Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
Iscience
|June 29, 2026
Summary
Post-stroke depression (PSD) has fluctuating courses. Higher education, functional independence, and social support may predict recovery from mild or severe post-stroke depression.
Area of Science:
- Neurology
- Psychiatry
- Biostatistics
Background:
- Post-stroke depression (PSD) is a common complication following ischemic stroke.
- The dynamic remission-relapse course of PSD and its transition predictors are not well understood.
- Understanding these transitions is crucial for effective PSD management.
Purpose of the Study:
- To investigate the transition dynamics between different states of post-stroke depression (non-PSD, mild-PSD, severe-PSD).
- To identify baseline predictors associated with deterioration and recovery in PSD patients.
- To inform early risk stratification and targeted interventions for PSD.
Main Methods:
- A continuous-time multi-state Markov model was applied.
- Longitudinal data from 536 ischemic stroke patients with 1,441 assessments were analyzed.
- Transition intensities between PSD states were quantified, and predictors were identified.
Main Results:
- The transition intensity from mild to non-PSD was significantly higher than progression to severe-PSD.
- Higher education (≥10 years) was associated with reduced risk of deterioration (HR = 0.63).
- Functional independence (Barthel index >60) and social support predicted remission from PSD.
Conclusions:
- PSD exhibits complex transition dynamics between different severity states.
- Baseline factors like education, functional status, and social support play a role in PSD course.
- These findings support early risk stratification and personalized interventions for managing PSD.
