Prediction of New-Onset Diabetes Mellitus within 12 Months after Liver Transplantation-A Machine Learning Approach.

ABSTRACT:

Background

Liver transplantation (LT) is a routine therapeutic approach for patients with acute liver failure, end-stage liver disease and/or early-stage liver cancer. While 5-year survival rates have increased to over 80%, long-term outcomes are critically influenced by extrahepatic sequelae of LT and immunosuppressive therapy, including diabetes mellitus (DM). In this study, we used machine learning (ML) to predict the probability of new-onset DM following LT.

Methods

A cohort of 216 LT patients was identified from the Disease Analyzer (DA) database (IQVIA) between 2005 and 2020. Three ML models comprising random forest (RF), logistic regression (LR), and eXtreme Gradient Boosting (XGBoost) were tested as predictors of new-onset DM within 12 months after LT.

Results

SUBMITTER: Loosen SH 

PROVIDER: S-EPMC10381881 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature