Abstract
The prelisting variables essential for creating an accurate heart transplant allocation score based on survival are unknown. To identify these we studied mortality of adults on the active heart transplant waiting list in the Scientific Registry of Transplant Recipients database from January 1, 2004 to August 31, 2015. There were 33 069 candidates awaiting heart transplantation: 7681 UNOS Status 1A, 13 027 Status 1B, and 12 361 Status 2. During a median waitlist follow-up of 4.3 months, 5514 candidates died. Variables of importance for waitlist mortality were identified by machine learning using Random Survival Forests. Strong correlates predicting survival were estimated glomerular filtration rate (eGFR), serum albumin, extracorporeal membrane oxygenation, ventricular assist device, mechanical ventilation, peak oxygen capacity, hemodynamics, inotrope support, and type of heart disease with less predictive variables including antiarrhythmic agents, history of stroke, vascular disease, prior malignancy, and prior tobacco use. Complex interactions were identified such as an additive risk in mortality based on renal function and serum albumin, and sex-differences in mortality when eGFR >40 mL/min/1.73 m. Most predictive variables for waitlist mortality are in the current tiered allocation system except for eGFR and serum albumin which have an additive risk and complex interactions.