Systematic review of risk prediction models for cognitive impairment in stroke patients
10.3760/cma.j.cn115682-20240116-00318
- VernacularTitle:脑卒中患者认知障碍风险预测模型的系统评价
- Author:
Chen YAO
1
;
Jianhua ZHANG
;
Zixin ZHANG
;
Yujia ZHANG
;
Jiaqing HAO
;
Yuan LIU
;
Luqian YUAN
Author Information
1. 大连医科大学附属第二医院护理部，大连 116027
- Keywords:
Stroke;
Cognitive impairment;
Prediction model;
Systematic review
- From:
Chinese Journal of Modern Nursing
2024;30(28):3866-3872
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To systematically review the risk prediction models for cognitive impairment in stroke patients, aiming to provide references for clinical healthcare professionals in selecting or constructing high-quality risk assessment tools.Methods:A computerized search was conducted in PubMed, Embase, Web of Science, OVID, Cochrane Library, SinoMed, CNKI, Wanfang Database, and VIP to identify studies related to risk prediction models for cognitive impairment in stroke patients. The search was limited to articles published up to August 1, 2023. Two researchers independently screened the literature, extracted data, and assessed the risk of bias and applicability of the included studies using PROBAST.Results:A total of 26 articles were included. The applicability of the studies was generally good, but all studies had some degree of bias risk, mainly arising from unreasonable study designs, inappropriate time intervals between predictor assessment and outcome determination, insufficient sample sizes, unreasonable handling of continuous variables, omission of missing data, lack of reporting of calibration, and overfitting of the models. Meta-analysis results showed that age ( OR=0.05, 95% CI: 0.033-0.057), education level ( OR=-0.13, 95% CI: -0.171 - -0.082), history of diabetes ( OR=2.32, 95% CI: 1.867-2.881), history of hypertension ( OR=0.67, 95% CI: 0.420-0.918), and NIHSS score ( OR=0.40, 95% CI: 0.331-0.469) were factors for cognitive impairment in stroke patients. Conclusions:While various risk prediction models for cognitive impairment in stroke patients exist, they suffer from methodological flaws and high bias risks, with some commonalities and controversies in predictors. Future research should adhere to the principles of transparent reporting of individual prognosis or diagnosis of multivariate prediction models, develop localized prediction models with low bias risk and good applicability, and conduct internal and external validations to demonstrate their applicability and feasibility in clinical practice.
