Introduction
The clinical management of end-stage heart disease is a continually evolving practice that has changed drastically over the last decade. Survival after orthotopic heart transplantation (OHT) has continued to improve.1 Despite improving longevity after transplant, donor organ supply remains inadequate to meet demand,2 and transplant programs face increased public and private scrutiny of their outcomes.3Simultaneously, technologic innovations in mechanical circulatory support platforms have demonstrated parallel improvement in clinical outcomes,4-6 thus increasing the potential alternative viable treatment options for heart failure patients. Therefore, an accurate prognostic model using pre-operative data for individualized donor and recipient selection would be of profound clinical utility in OHT.
Prior risk models for predicting survival after OHT have displayed only modest discriminatory capability. Examples of such algorithms include The Donor Risk Index (DRI),7 Risk Stratification Score (RSS),8 Index for Mortality Prediction After Cardiac Transplantation (IMPACT)9 and International Heart Transplant Survival Algorithm (IHTSA)10. With increasing interest in the application of machine learning (ML) to predictive analytics in clinical medicine,11 we aimed to evaluate whether modern ML techniques could improve risk prediction in OHT.