A systematic review on risk prediction models of post stroke depression
10.3760/cma.j.cn115354-20220618-00426
- VernacularTitle:脑卒中后抑郁风险预测模型的系统评价
- Author:
Qian YOU
1
;
Jing GAO
;
Huan CHEN
;
Dingxi BAI
;
Hao ZHANG
Author Information
1. 成都中医药大学护理学院，成都 611137
- Keywords:
Post-stroke depression;
Risk prediction model;
Systematic review
- From:
Chinese Journal of Neuromedicine
2022;21(9):916-923
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To systematically evaluate the risk prediction models of post-stroke depression (PSD).Methods:Web of Science, The Cochrane Library, PubMed, Embase, CINAHL, CNKI, SinoMed, WanFang Data, and VIP database were searched for literature related to PSD risk prediction models from inception to June 1, 2022. The quality of the included models was evaluated by Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Meta-analysis was performed on influencing factors enjoyed generality in the included models by RevMan 5.3 software.Results:A total of 9 pieces of literature were included, analyzing 11 risk prediction models. The area under the curve (AUC) for all models ranged from 0.726 to 0.854, and the AUC of 7 models was ≥0.8, enjoying a high prediction efficiency but a risk of bias; and the main reasons included not reporting the processing of missing data, incomplete evaluation of model effect, and lack of internal and external validation of the models. Meta-analysis results showed depression or other mental illness ( OR=6.73, 95%CI: 3.87-11.73), Eysenck Personality Questionnaire (EPQ) scores ( OR=1.13, 95%CI: 1.03-1.23), hypertension ( OR=0.47, 95%CI: 0.30-0.74), and Barthel index (BI, OR=0.98, 95%CI: 0.98-0.99) were predictors for PSD. Conclusions:PSD risk prediction models have good predictive performance but with a risk of bias, therefore, the modeling method should be improved in the future. The establishment of PSD risk prediction models should focus on the predictors as history of depression or other mental disorders, EPQ scores, hypertension, and BI.
