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Background. Early prediction of whether a liver allograft will be utilized for transplantation may allow better resource deployment during donor management and improve organ allocation. The national donor management goals (DMG) registry contains critical care data collected during donor management. We developed a machine learning model to predict transplantation of a liver graft based on data from the DMG registry. Methods. Several machine learning classifiers were trained to predict transplantation of a liver graft. We utilized 127 variables available in the DMG dataset. We included data from potential deceased organ donors between April 2012 and January 2019. The outcome was defined as liver recovery for transplantation in the operating room. The prediction was made based on data available 12–18 h after the time of authorization for transplantation. The data were randomly separated into training (60%), validation (20%), and test sets (20%). We compared the performance of our models to the Liver Discard Risk Index. Results. Of 13 629 donors in the dataset, 9255 (68%) livers were recovered and transplanted, 1519 recovered but used for research or discarded, 2855 were not recovered. The optimized gradient boosting machine classifier achieved an area under the curve of the receiver operator characteristic of 0.84 on the test set, outperforming all other classifiers. Conclusions. This model predicts successful liver recovery for transplantation in the operating room, using data available early during donor management. It performs favorably when compared to existing models. It may provide real-time decision support during organ donor management and transplant logistics.

Background. Rates of kidney transplantation vary substantially across dialysis facilities in the United States. Whether distance between the dialysis facility and transplant center associates with variations in transplantation rates has not been examined. Methods. We performed a retrospective study of adults treated with dialysis between 2005 and 2015, according to the US Renal Data System. We examined the association between distance from dialysis facility to transplant center and time to kidney transplantation (primary outcome) and waitlist registration (secondary outcome) using Fine-Gray models. We also performed sensitivity analyses using the distance from each patient’s dialysis facility to the nearest transplant center as the predictor so that patients who were never registered on the waitlist (and therefore would not have a transplant center) could be included. Results. In total, 178 885 waitlisted patients were included for our primary analysis. As distance between dialysis facility and transplant center increased, lower hazard of transplantation (subhazard ratio [HR], 0.92; 95% confidence interval [CI], 0.91-0.94, if distance was 10 to