Leveraging machine learning methods to predict post-transplant diabetes in solid organ transplant recipients
Abstract
Background: 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.
Materials & 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 (n = 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.
Results: The incidence of PTDM ranged from 15.0% in kidney to 20.2% in lung recipients. Compared to logistic regression, random forest demonstrated modestly improved discrimination (AUROC: 0.62-0.63) and calibration (Brier: 0.124-0.156). It was found across all models that age and BMI were consistent predictors.
Prednisone and cyclosporine maintenance were often found to be associated with higher PTDM risk while mycophenolate mofetil was seen as protective. SHAP analysis demonstrated complex, nonlinear interactions and supported known risk patterns such as increased age and male sex.
Conclusion: Our machine learning model identified risk factors common to post-transplant diabetes across organ transplant recipients, guiding more optimal patient survival post-transplant.
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BibTeX
@article{varghese2026leveraging,
title = {{Leveraging machine learning methods to predict post-transplant diabetes in solid organ transplant recipients}},
author = {Rhea Varghese and Yingji Sun and Saba Maleki and Mamatha Bhat},
journal = {Journal of the Endocrine Society},
year = {2026},
month = oct,
doi = {10.1210/jendso/bvag236},
url = {https://europepmc.org/article/PMC/PMC13725161},
note = {CC BY},
}
RIS
TY - JOUR
TI - Leveraging machine learning methods to predict post-transplant diabetes in solid organ transplant recipients
AU - Rhea Varghese
AU - Yingji Sun
AU - Saba Maleki
AU - Mamatha Bhat
PY - 2026
DA - 2026/10/15
JO - Journal of the Endocrine Society
DO - 10.1210/jendso/bvag236
UR - https://europepmc.org/article/PMC/PMC13725161
AB - Background: 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. Materials & 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 (n = 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. Results: The incidence of PTDM ranged from 15.0% in kidney to 20.2% in lung recipients. Compared to logistic regression, random forest demonstrated modestly improved discrimination (AUROC: 0.62-0.63) and calibration (Brier: 0.124-0.156). It was found across all models that age and BMI were consistent predictors. Prednisone and cyclosporine maintenance were often found to be associated with higher PTDM risk while mycophenolate mofetil was seen as protective. SHAP analysis demonstrated complex, nonlinear interactions and supported known risk patterns such as increased age and male sex. Conclusion: Our machine learning model identified risk factors common to post-transplant diabetes across organ transplant recipients, guiding more optimal patient survival post-transplant.
SN - 2472-1972
ER -