Prediction of 90 day readmission in heart failure with preserved ejection fraction by interpretable machine learning.

AIMS

Certain critical risk factors of heart failure with preserved ejection fraction (HFpEF) patients were significantly different from those of heart failure with reduced ejection fraction (HFrEF) patients, resulting in the limitations of existing predictive models in real-world situations. This study aimed to develop a machine learning model for predicting 90 day readmission for HFpEF patients.

METHODS AND RESULTS

Data were extracted from electronic health records from 1 August 2020 to 1 August 2021 and follow-up records of patients with HFpEF within 3 months after discharge. Feature extraction was performed by univariate analysis combined with the least absolute shrinkage and selection operator (LASSO) algorithms. Machine learning models like eXtreme Gradient Boosting (XGBoost), random forest, neural network and logistic regression were adopted to construct models. The discrimination and calibration of each model were compared, and the Shapley Additive exPlanations (SHAP) method was used to explore the interpretability of the model. The cohort included 746 patients, of whom 103 (13.8%) were readmitted within 90 days. XGBoost owned the best performance [area under the curve (AUC) = 0.896, precision-recall area under the curve (PR-AUC) = 0.868, sensitivity = 0.817, specificity = 0.837, balanced accuracy = 0.827]. The Kolmogorov-Smirnov (KS) statistic was 0.694 at 0.468 in the XGBoost model. SHAP identified the top 12 risk features, including activities of daily living (ADL), left atrial dimension (LAD), left ventricular end-diastolic diameter (LVDD), shortness, nitrates, length of stay, nutritional risk, fall risk, accompanied by other symptoms, educational level, anticoagulants and edema.

CONCLUSIONS

Our model could help medical agencies achieve the early identification of 90 day readmission risk in HFpEF patients and reveal risk factors that provide valuable insights for treatments.