ArticleFrontiers in neuroscience2026
Early prediction of post-stroke depression using polysomnography parameters: a prospective cohort study.
Article in Frontiers in neuroscience, 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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Abstract
Background: Post-stroke depression (PSD) affects approximately one-third of stroke survivors and significantly impacts functional recovery. This study aimed to develop and validate a prediction model for PSD using polysomnography (PSG) parameters combined with clinical characteristics. Methods: We conducted a prospective cohort study enrolling 437 acute ischemic stroke patients who underwent PSG assessment within 7 days of stroke onset between January 2022 and December 2024. The primary outcome was PSD at 3-month follow-up, defined as Patient Health Questionnaire-9 (PHQ-9) score ≥10. Multivariate logistic regression identified independent predictors, and machine learning algorithms were compared for model performance. Results: Among 390 patients completing follow-up, 139 (35.6%) developed PSD. Independent predictors included sleep latency (OR = 2.14, 95% CI: 1.68-2.73), arousal index (OR = 2.13, 95% CI: 1.65-2.94), sleep efficiency (OR = 0.74, 95% CI: 0.55-0.96), heart rate variability RMSSD (OR = 0.77, 95% CI: 0.61-0.96), NIHSS score (OR = 1.35, 95% CI: 1.04-1.76), and prior stroke history (OR = 1.35, 95% CI: 1.09-1.71). The gradient boosting model achieved the highest discriminative performance (AUC = 0.763; bootstrap-validated AUC = 0.738, 95% CI: 0.682-0.794). Risk stratification demonstrated a five-fold gradient in PSD rates across probability categories (14.3 to 70.3%). Conclusion: PSG parameters, particularly sleep efficiency, sleep latency, and arousal index, are significant independent predictors of PSD. Integrating objective sleep assessment into early stroke management may facilitate identification of high-risk patients for targeted preventive interventions.
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