Objective To conduct a comprehensive review of functional prognosis prediction models for stroke patients,offering insights for clinical nursing practice and research.Methods A search was conducted across various databases including PubMed,Web of Science,Embase,CINAHL,Cochrane Library,China National Knowledge Infrastructure,Wanfang Database,VIP Database,and China Biomedical Literature Database from their inception to April 1,2024.There were 2 researchers who independently screened the literature,extracted data,and assessed the quality of the studies.Results The review included 19 studies on the development of functional prognosis prediction models for stroke patients,resulting in a total of 57 models.The area under the receiver operating characteristic curve ranged from 0.730 to 0.974.External validation was performed for 3 of the models.Age,gender,NIHSS,and history of stroke emerged as the principal predictors in the functional prognosis prediction models for stroke patients.Conclusion Research on functional prognosis prediction models for stroke patients is still in its early stages,with models demonstrating reasonable predictive power but carrying a high risk of bias.Future studies should standardize model development and validation.The aim should be to create user-friendly models with strong predictive capabilities,thereby providing healthcare professionals with solid foundations for clinical decision-making.

Objective To conduct a comprehensive review of functional prognosis prediction models for stroke patients,offering insights for clinical nursing practice and research.Methods A search was conducted across various databases including PubMed,Web of Science,Embase,CINAHL,Cochrane Library,China National Knowledge Infrastructure,Wanfang Database,VIP Database,and China Biomedical Literature Database from their inception to April 1,2024.There were 2 researchers who independently screened the literature,extracted data,and assessed the quality of the studies.Results The review included 19 studies on the development of functional prognosis prediction models for stroke patients,resulting in a total of 57 models.The area under the receiver operating characteristic curve ranged from 0.730 to 0.974.External validation was performed for 3 of the models.Age,gender,NIHSS,and history of stroke emerged as the principal predictors in the functional prognosis prediction models for stroke patients.Conclusion Research on functional prognosis prediction models for stroke patients is still in its early stages,with models demonstrating reasonable predictive power but carrying a high risk of bias.Future studies should standardize model development and validation.The aim should be to create user-friendly models with strong predictive capabilities,thereby providing healthcare professionals with solid foundations for clinical decision-making.

Objective:To systematically evaluate the bias risk and applicability of short-term mortality risk prediction models within 30 days after stroke, providing a basis for selecting or developing standardized risk prediction models.Methods:Research on short-term mortality risk prediction models within 30 days after stroke was electronically retrieved from China National Knowledge Infrastructure, WanFang Data, VIP, and China Biomedical Database, PubMed, Web of Science, Embase, Cochrane Library and CINAHL. The search period was from database establishment to December 5, 2023. Two researchers independently conducted literature screening and quality evaluation.Results:Twelve studies were included, and a total of 31 models were internally validated, with 7 models undergoing external validation based on internal validation. 26 models reported discriminative power, and 18 models reported calibration methods. The most frequent predictors of modeling were age, hypertension, atrial fibrillation, diabetes and admission Glasgow Coma Scale score. Due to methodological problems such as insufficient sample size, improper handling of missing variables, and inadequate reporting of modeling information, all included studies were rated as high risk of bias.Conclusions:The research on short-term mortality risk prediction models for stroke patients is still in the development stage. Although it has good applicability, the risk of bias is relatively high. Future research should be designed and reported based on prediction model risk of bias assessment tool (PROBAST) and transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) to avoid common problems summarized in this study and reduce the risk of bias.