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ABSTRACT Background Outcome prediction for live-donor kidney transplantation improves clinical and patient decisions and donor selection. However, the currently used models are of limited discriminative or calibration power and there is a critical need to improve the selection process. We aimed to assess the value of various artificial intelligence (AI) algorithms to improve the risk stratification index. Methods We evaluated pre-transplant variables among 66 914 live-donor kidney transplants (performed between 1 December 2007 and 1 June 2021) from the United Network of Organ Sharing database, randomized into training (80%) and test (20%) sets. The primary outcome measure was death-censored graft survival. We tested four machine learning models for discrimination time-dependent concordance index (CTD) and area under the receiver operating characteristic curve (AUC) and calibration integrated Brier score (IBS). We used decision-curve analysis to assess the potential clinical utility. Results Among the models, the deep Cox mixture model showed the best discriminative performance (AUC = 0.70, 0.68 and 0.68 at 5, 10 and 13 years post-transplant, respectively). CTD reached 0.70, 0.67 and 0.66 at 5, 10 and 13 years post-transplant. The IBS score was 0.09, indicating good calibration. In comparison, applying the Living Kidney Donor Profile Index (LKDPI) on the same cohort produced a CTD of 0.56 and an AUC of 0.55–0.58 only. Decision-curve analysis showed an additional net benefit compared with the LKDPI ‘treat all’ and ‘treat none’ approaches. Conclusion Our AI-based deep Cox mixture model, termed Live-Donor Kidney Transplant Outcome Prediction, outperforms existing prediction models, including the LKDPI, with the potential to improve decisions for optimum live-donor selection by ranking potential transplant pairs based on graft survival. This model could be adopted to improve the outcomes of paired exchange programs.
Ali et al. (2024) studied this question.