Development and validation of a machine learning model to predict the risk of readmission within one year in HFpEF patients: Short title: Prediction of HFpEF readmission.

BACKGROUND

Heart failure with preserved ejection fraction (HFpEF) is associated with elevated rates of readmission and mortality. Accurate prediction of readmission risk is essential for optimizing healthcare resources and enhancing patient outcomes.

METHODS

We conducted a retrospective cohort study utilizing HFpEF patient data from two institutions: the First Affiliated Hospital Zhejiang University School of Medicine for model development and internal validation, and the Affiliated Hospital of Xuzhou Medical University for external validation. A machine learning (ML) model was developed and validated using 53 variables to predict the risk of readmission within one year. The model's performance was assessed using several metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, model training time, model prediction time and brier score. SHAP (SHapley Additive exPlanations) analysis was employed to enhance model interpretability, and a dynamic nomogram was constructed to visualize the predictive model.

RESULTS

Among the 766 HFpEF patients included in the study, 203 (26.5%) were readmitted within one year. The LightGBM model exhibited the highest predictive performance, with an AUC of 0.88 (95% confidence interval (CI):0.84-0.91), an accuracy of 0.79, a sensitivity of 0.81, and a specificity of 0.78. Key predictors included the E/e' ratio, NYHA classification, LVEF, age, BNP levels, MLR, history of atrial fibrillation (AF), use of ACEI/ARB/ARNI, and history of myocardial infarction (MI). External validation also demonstrated strong predictive performance, with an AUC of 0.87 (95%CI:0.83-0.91).

CONCLUSIONS

The LightGBM model exhibited robust performance in predicting one-year readmission risk among HFpEF patients, providing a valuable tool for clinicians to identify high-risk individuals and implement timely interventions.