Temporal Validation of a Machine Learning Readmission Model in Patients With Heart Failure With Preserved Ejection Fraction and Chronic Kidney Disease

BACKGROUND: The coexistence of heart failure with preserved ejection fraction and chronic kidney disease is associated with high readmission risks. Existing tools, such as the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, rely on linear modelling, which limits their ability to capture nonlinear interactions. We developed a machine learning model to predict readmission and assessed its performance against temporal data drift. METHODS: In this retrospective study, patients were temporally stratified into derivation (n = 750) and independent validation (n = 130) cohorts. Feature selection used the least absolute shrinkage and selection operator and recursive feature elimination with cross-validation. Twelve algorithms were trained using stratified nested cross-validation. Model stability was evaluated using the Population Stability Index (PSI) and rolling metrics. RESULTS: Ten features were identified, with estimated glomerular filtration rate as the primary predictor. An interaction was observed between high-sensitivity C-reactive protein and N-terminal pro-B-type natriuretic peptide. The random forest model achieved an area under the receiver operating characteristic curve of 0.837 (95% confidence interval, 0.761-0.905) in the validation cohort, compared with 0.551 for the MAGGIC score. Longitudinal analysis indicated temporal stability, with feature PSI scores < 0.25. CONCLUSIONS: A machine learning model that incorporated renal, inflammatory, and hemodynamic parameters demonstrated greater predictive accuracy for 1-year readmission compared with the MAGGIC score. The model maintained temporal stability, in support of its potential utility for risk stratification.