PloS one
Feasibility of a machine learning model for predicting weight gain after kidney transplantation machine learning and weight gain after kidney transplantation
By de Freitas JMC, Ballester PL, Pedrollo EF and 4 others.
Abstract
Background
Excessive weight gain after kidney transplantation adversely affects patient and graft outcomes. Machine Learning has been increasingly used to improve risk prediction across a range of transplant-related outcomes, yet its application to post-transplant weight trajectories remains unexplored. This study is the first to evaluate the feasibility of using Machine Learning algorithms to identify predictors of weight gain after kidney transplantation.
Methods
This retrospective cohort study analyzed 374 adult kidney transplant recipients. We developed an Elastic Net regression model with 10-fold cross-validation to predict body weight variation at 12 months post-transplant. The analysis screened a comprehensive set of demographic and clinical variables, including recipient characteristics, donor type, dialysis duration, and pre-transplant hypertension, to identify predictors within the training (75%) and testing (25%) datasets.