Artificial neural networks and liver transplantation: Are we ready for self‐driving cars?

The persistent shortage of donor organs has motivated efforts to predict posttransplant outcome, maximize survival benefit, and encourage the appropriate allocation of donor organs. Machine learning, a branch of statistics and computer science that has revolutionized the analysis of large and complex data sets, and its application to medicine has accelerated in recent years. Not only have machine-learning algorithms been used to develop the technology for Google Home, Siri, and self-driving cars, they have also been used to predict hospitalization in patients with heart failure, remission in patients with inflammatory bowel disease, and graft failure after solid organ transplantation. In this issue of Liver Transplantation, Ayll on et al. applied an artificial neural network (ANN), a type of machine-learning algorithm, to a cohort of 858 liver transplantation (LT) recipients from 2002 to 2010 at King’s College Hospital (KCH) using 55 donor, procurement, and recipient variables. This ANN had been previously used to develop a model in a Spanish multicenter cohort (Model for Allocation of Donor and Recipient in Espa~ na [MADR-E]) of 1003 LT recipients with an area under the curve (AUC), or c-statistic, of 0.82 for 3-month graft failure. The KCH model was developed using the same ANN architecture (although the predictive model was different) used for the MADR-E study, which is designed to maximize the accuracy of graft survival by the correct classification rate (CCR) and the sensitivity of graft failure by minimum sensitivity (MS). In the KCH cohort, the ANN resulted in an AUC of 0.94 for 3month graft failure, superior to other published models. This result implies that for 2 randomly drawn grafts, 1 of graft survival and 1 of graft failure, the model would correctly assign higher 3-month graft risk to the failed graft 94% of the time—compared with AUCs of 0.73 for donor Model for End-Stage Liver Disease (DMELD), 0.82 for survival outcomes following liver transplantation (SOFT), and 0.84 for balance of risk (BAR), in this cohort. The ANN was also trained to predict 1-year graft failure and resulted in an AUC of 0.82, also superior to previously developed scores (AUCs of 0.63 for D-MELD, 0.65 for SOFT, and 0.71 for BAR). Variables reported to have the highest weight in the KCH model included pretransplant status, Model for End-Stage Liver Disease (MELD) at transplant, time on waiting list, etiology of liver disease, cold ischemia time, and donor hypertension, cause of death, and aspartate aminotransferase (AST) levels. The authors illustrated examples in which organ acceptance can be guided using the probability of graft survival and nonsurvival in combination with the MELD of a potential recipient. A donor-recipient match is chosen if their predicted 3-month graft survival by the CCR model is >3% of a competing match or if their predicted graft failure by the MS model is >5%—these cutoffs represent approximately 1 standard deviation in the Abbreviations: ANN, artificial neural network; AST, aspartate aminotransferase; AUC, area under the curve; BAR, balance of risk; CCR, correct classification rate; D-MELD, donor Model for EndStage Liver Disease; KCH, King’s College Hospital; LT, liver transplantation; MADR-E, Model for Allocation of Donor and Recipient in Espa~ na; MELD, Model for End-Stage Liver Disease; MS, minimum sensitivity; SOFT, survival outcomes following liver transplantation.