Machine learning‐based prediction of mortality after heart transplantation in adults with congenital heart disease: A UNOS database analysis

Background Machine learning (ML) is increasingly being applied in Cardiology to predict outcomes and assist in clinical decision‐making. We sought to develop and validate an ML model for the prediction of mortality after heart transplantation (HT) in adults with congenital heart disease (ACHD). Methods The United Network for Organ Sharing (UNOS) database was queried from 2000 to 2020 for ACHD patients who underwent isolated HT. The study cohort was randomly split into derivation (70%) and validation (30%) datasets that were used to train and test a CatBoost ML model. Feature selection was performed using SHapley Additive exPlanations (SHAP). Recipient, donor, procedural, and post‐transplant characteristics were tested for their ability to predict mortality. We additionally used SHAP for explainability analysis, as well as individualized mortality risk assessment. Results The study cohort included 1033 recipients (median age 34 years, 61% male). At 1 year after HT, there were 205 deaths (19.9%). Out of a total of 49 variables, 10 were selected as highly predictive of 1‐year mortality and were used to train the ML model. Area under the curve (AUC) and predictive accuracy for the 1‐year ML model were .80 and 75.2%, respectively, and .69 and 74.2% for the 3‐year model, respectively. Based on SHAP analysis, hemodialysis of the recipient post‐HT had overall the strongest relative impact on 1‐year mortality after HΤ, followed by recipient‐estimated glomerular filtration rate, age and ischemic time. Conclusions ML models showed satisfactory predictive accuracy of mortality after HT in ACHD and allowed for individualized mortality risk assessment.