Machine learning to predict transplant outcomes: helpful or hype? A national cohort study

Abstract

An increasing number of studies claim machine learning (ML) predicts transplant outcomes more accurately. However, these claims were possibly confounded by other factors, namely, supplying new variables to ML models. To better understand the prospects of ML in transplantation, we compared ML to conventional regression in a "common" analytic task: predicting kidney transplant outcomes using national registry data. We studied 133 431 adult deceased-donor kidney transplant recipients between 2005 and 2017. Transplant centers were randomly divided into 70% training set (190 centers/97 787 recipients) and 30% validation set (82 centers/ 35 644 recipients). Using the training set, we performed regression and ML procedures [gradient boosting (GB) and random forests (RF)] to predict delayed graft function, one-year acute rejection, death-censored graft failure C, all-cause graft failure, and death. Their performances were compared on the validation set using-statistics. In predicting rejection, regression (C = 0.601 0.611 0.621) actually outperformed GB (C = 0.581 0.591 0.601) and RF (C = 0.569 0.579 0.589). For all other outcomes, the C-statistics were nearly identical across methods (delayed graft function, 0.717-0.723; death-censored graft failure, 0.637-0.642; all-cause graft failure, 0.633-0.635; and death, 0.705-0.708). Given its shortcomings in model interpretability and hypothesis testing, ML is advantageous only when it clearly outperforms conventional regression; in the case of transplant outcomes prediction, ML seems more hype than helpful.

The Independent Effects of Procurement Biopsy Findings on 10-Year Outcomes of Extended Criteria Donor Kidney Transplants

“…Given the widely recognized challenge of accurately predicting transplant outcomes, 77 , 97 , 98 if biopsies do indeed contain statistically and clinically significant information beyond standard parameters, then they can improve our limited ability to risk stratify donor kidneys. However, though in theory more information should lead to better decisions, in the case of biopsy findings, more information may currently be causing more harm than good.…”

Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation

“…After that, we validated ANN methodology in D-R matching in a different health care system (data from King’s College Hospital, KCH), showing that it would be a powerful tool for D-R matching in comparison to other current models [ 21 ]. This methodology has been recently validated using gradient boosting and random forest classifiers [ 22 ] using data from 272 different centres, denoting that outstanding results could be obtained independently of the population location.…”

Artificial intelligence and kidney transplantation

“…In a critical paper, Bae et al [ 39 ] examined whether ML techniques are superior to conventional regression analysis. Studying the records of 133431 adult deceased donor kidney transplant recipients from the national registry data, the authors randomly selected 70% of the transplant centers for training and 30% for validation.…”