Prediction Models for Post‐Stroke Hospital Readmission: A Systematic Review

ABSTRACT Objective This study aims to evaluate the predictive performance and methodological quality of post‐stroke readmission prediction models, identify key predictors associated with readmission, and provide guidance for selecting appropriate risk assessment tools. Methods A comprehensive literature search was conducted from inception to February 1, 2024. Two independent researchers screened the literature and extracted relevant data using the CHARMS checklist. Results Eleven studies and 16 prediction models were included, with sample sizes ranging from 108 to 803,124 cases and outcome event incidences between 5.2% and 50.0%. The four most frequently included predictors in the models were length of stay, hypertension, age, and functional disability. Twelve models reported an area under the curve (AUC) ranging from 0.520 to 0.940, and five models provided calibration metrics. Only one model included both internal and external validation, while six models had internal validation. Eleven studies were found to have a high risk of bias (ROB), predominantly in the area of data analysis. Conclusion This systematic review included 16 readmission prediction models for stroke, which generally exhibited good predictive performance and can effectively identify high‐risk patients likely to be readmitted. However, the generalizability of these models remains uncertain due to methodological limitations. Rather than developing new readmission prediction models for stroke, the focus should shift toward external validation and the iterative adaptation of existing models. These models should be tailored to local settings, extended with new predictors if necessary, and presented in an interactive graphical user interface.