Enhancing readmission prediction model in older stroke patients by integrating insight from readiness for hospital discharge: Prospective cohort study

Abstract

Background

The 30-day hospital readmission rate is a key indicator of healthcare quality and system efficiency. This study aimed to develop machine-learning (ML) models to predict unplanned 30-day readmissions in older patients with ischemic stroke (IS) using a prospective cohort design.

Methods

Patients were divided into two datasets: dataset I (January 2020–December 2021) for model development and dataset II (January 2022–December 2023) for validation. A diffusion model was applied to address data imbalance. Eleven machine-learning methods, including Random Forest (RF), Logistic Regression, CatBoost, eXtreme Gradient Boosting Light Gradient Boosting Machine, K-Nearest Neighbors Support Vector Machine, Multi-Layer Perceptron, and Gaussian Naive Bayes, and 2 ensemble learning models, were constructed to predict readmissions. Bayesian optimization was used to fine-tune the hyperparameters of these models. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUC). Shapley Additive Explanations (SHAP) were utilized to identify and interpret the significance of predictive variables.

Results

Dataset I included 489 patients, while dataset II comprised 418 patients, with readmission rates of 15.3 % and 16.0 %, respectively. The RF model achieved the highest predictive performance (AUC = 0.9116, sensitivity = 0.8806, specificity = 0.7806). SHAP analysis identified readiness for hospital discharge as the most significant predictor of readmission.

Conclusion

The RF model shows promise for predicting unplanned 30-day readmissions in older patients with IS. Multi-center studies with larger sample sizes are needed to validate these findings.