Using machine learning, researchers identified serum creatinine levels, pre-transplant body weight, age of patients, and bkpyv infection as key predictors for early allograft loss in kidney transplant patients. The model achieved a high precision of 0.81, sensitivity of 0.61, specificity of 0.89, and auc value of 0.84. These findings highlight the potential of machine learning tools to aid in clinical decision-making for kidney transplantation.
Journal Article by Fabreti-Oliveira RA, Nascimento E (…) Veloso AA et 2 al. in Transpl Immunol
Copyright © 2024. Published by Elsevier B.V.