Machine Learning Models for Predicting Medium-Term Heart Failure Prognosis: Discrimination and Calibration Analysis

Background: The number of patients with heart failure (HF) is increasing with population aging, shifting care from hospitals to clinics. Predicting medium-term prognosis after discharge can improve clinical care and reduce readmissions; however, no established model has been evaluated with both discrimination and calibration. Objectives: This study aimed to develop machine learning (ML) models and assess their feasibility in predicting the medium-term prognosis of patients with HF. Methods: This study included 4,904 patients with HF admitted to four affiliated hospitals at Nippon Medical School (2018–2023). Four ML models—logistic regression, random forests, extreme gradient boosting, and light gradient boosting—were developed to predict the endpoints of death or emergency hospitalization within 180 days of discharge. The patients were randomly divided into training and validation sets (8:2), and the ML models were trained on the training dataset and evaluated using the validation dataset. Results: All models demonstrated acceptable performance as assessed by the area under the precision-recall curve. The models showed favorable agreement between the predicted and observed outcomes in the calibration evaluations with the calibration slope and Brier score. Successful risk stratification of medium-term outcomes was achieved for individual patients with HF. The SHapley Additive exPlanations algorithm highlighted the significant contribution of nursing care needs to prediction, alongside established laboratory values for HF prognosis. Conclusions: ML models effectively predict the 180-day prognosis of patients with HF, and the influence of nursing care needs underscores the importance of multidisciplinary collaboration in HF care.