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Post-stroke depression risk prediction models in stroke patients: A systematic review
1Nursing Department of Chongqing Jiangjin District Traditional Chinese Medicine Hospital, Chongqing, China.
General Hospital Psychiatry
|July 13, 2025
Summary
This systematic review found that post-stroke depression (PSD) risk prediction models show promising performance but require optimization. Future research should focus on improving model quality and applicability for better patient prognosis.
Area of Science:
- Neurology
- Psychiatry
- Medical Informatics
Background:
- Post-stroke depression (PSD) is a significant complication impacting patient outcomes.
- Risk prediction models are crucial for early identification and intervention of PSD.
- The quality and applicability of existing PSD prediction models require thorough evaluation.
Purpose of the Study:
- To systematically review and evaluate published risk prediction models for post-stroke depression.
- To assess the quality, applicability, and predictive performance of current PSD risk models.
Main Methods:
- Comprehensive literature search across multiple databases (e.g., PubMed, EMbase, Web of Science) up to March 2025.
- Independent screening, data extraction, and risk of bias assessment by two researchers.
- Qualitative systematic review of 12 included studies on PSD risk prediction models.
Main Results:
- 13 risk prediction models were identified, with AUC/C-index values ranging from 0.726 to 0.928.
- All models exhibited a high risk of bias, and three demonstrated poor applicability.
- Common predictors included Barthel Index, NIHSS score, age, hypertension, and education level.
Conclusions:
- PSD risk prediction models demonstrate promising predictive performance.
- Significant limitations exist in data sources, study design, and data processing, necessitating model optimization.
- Future research should focus on external validation and developing higher-quality, more applicable PSD prediction models.
