Leveraging Machine Learning Methods to Predict Post-Transplant Diabetes in Solid Organ Transplant Recipient - Supplemental Files

Résumé fourni par la source

Post-transplant diabetes mellitus (PTDM) is a common complication following solid organ transplantation and is associated with increased morbidity and mortality. This study aimed to identify both generalizable and solid organ-specific predictors of PTDM with machine learning methods. In this study, we used 2002-2023 data from the Scientific Registry of Transplant Recipients (SRTR) to examine the risk of PTDM development across the liver (n=128 016), kidney (n=360 562), heart (n= 45 505) and lung (36 260) transplant recipients. We examined various features at baseline using logistic regression and random forest models. Performance was evaluated using area under the receiver operating characteristic curve (AUROC), Brier scores, sensitivity, and specificity. Shapley Additive exPlanations were used for model interpretation and to identify key relevant features

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