Machine Learning Prediction of Heart Failure Readmissions: Insights From a Multicenter Emergency Department Trial.

BACKGROUND: Heart failure (HF) readmissions remain common and costly, yet existing prediction models show limited clinical utility, particularly at the time of emergency department (ED) presentation. OBJECTIVES: This study aimed to develop and evaluate machine learning (ML) models to predict 12-month HF readmission among patients presenting to EDs with suspected cardiac conditions. METHODS: This was a subanalysis of a cluster randomized clinical trial (n = 14,131) across 12 South Australia EDs (enrolled April-December 2023), which included patients with symptoms warranting high-sensitivity cardiac troponin T testing who were subsequently followed up for 12 months for HF readmission. The data set was randomly split into training (70%) and test (30%) sets. Four ML algorithms were compared with logistic regression. Features with >25% missing data were excluded. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: Of 14,131 ED patients (mean age 61 years; 49% female), 808 (5.71%) were readmitted for HF at a median of 83 days (IQR: 27-178). In the test set, an eXtreme Gradient Boosting (XGB) model (AUC: 0.861; 95% CI: 0.847-0.875) outperformed logistic regression for the prediction of HF readmission (AUC: 0.846; 95% CI: 0.828-0.861; P = 0.015). AUCs for other ML models were 0.845 (random forest), 0.851 (least absolute shrinkage and selection operator), and 0.842 (neural networks). The Brier score for XGB was 0.151, demonstrating good model calibration. CONCLUSIONS: An XGB-based ML model accurately predicted 12-month HF readmission following ED presentation and outperformed traditional regression methods, supporting its potential role in early risk stratification and targeted preventive care.