ArticleiScience2026
Predictive factors for remission in post-stroke depression: A Markov model cohort study.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
9 authors.
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Abstract
Post-stroke depression (PSD) follows a dynamic remission-relapse course, yet predictors of state transitions remain unclear. Here, we applied a continuous-time multi-state Markov model to 536 ischemic stroke patients with 1,441 longitudinal assessments. We quantified transition intensities between non-PSD, mild-PSD, and severe-PSD states and identified baseline predictors of deterioration and recovery. The transition intensity from mild to non-PSD was 5.5-fold higher than progression to severe-PSD. Education ≥10 years reduced deterioration risk (HR = 0.63), while functional independence (Barthel index >60) and social support predicted remission. Severe-PSD exhibited an apparent short sojourn time (estimated mean 2.7 months) and frequent observed transitions toward milder states; however, in the absence of mortality data, this estimate may be biased by unobserved competing events and should be interpreted as hypothesis-generating rather than definitive. These findings enable early risk stratification and targeted intervention for PSD management.
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