Noncontrast Cardiac Magnetic Resonance Imaging Predictors of Heart Failure Hospitalization in Heart Failure With Preserved Ejection Fraction

Background Heart failure patients with preserved ejection fraction (HFpEF) are at increased risk of future hospitalization. Contrast agents are often contra‐indicated in HFpEF patients due to the high prevalence of concomitant kidney disease. Therefore, the prognostic value of a noncontrast cardiac magnetic resonance imaging (MRI) for HF‐hospitalization is important. Purpose To develop and test an explainable machine learning (ML) model to investigate incremental value of noncontrast cardiac MRI for predicting HF‐hospitalization. Study Type Retrospective. Population A total of 203 HFpEF patients (mean, 64 ± 12 years, 48% women) referred for cardiac MRI were randomly split into training validation (143 patients, ~70%) and test sets (60 patients, ~30%). Field strength A 1.5 T, balanced steady‐state free precession (bSSFP) sequence. Assessment Two ML models were built based on the tree boosting technique and the eXtreme Gradient Boosting model (XGBoost): 1) basic clinical ML model using clinical and echocardiographic data and 2) cardiac MRI‐based ML model that included noncontrast cardiac MRI markers in addition to the basic model. The primary end point was defined as HF‐hospitalization. Statistical Tests ML tool was used for advanced statistics, and the Elastic Net method for feature selection. Area under the receiver operating characteristic (ROC) curve (AUC) was compared between models using DeLong's test. To gain insight into the ML model, the SHapley Additive exPlanations (SHAP) method was leveraged. A P‐value