The BIOPREVENT machine-learning algorithm predicts chronic graft-versus-host disease and mortality risk using posttransplant biomarkers.

ABSTRACT: BACKGROUNDChronic graft-versus-host disease (cGVHD) is a major contributor to nonrelapse mortality (NRM) following hematopoietic cell transplantation (HCT). Whether machine-learning (ML) models with biomarkers improve the accuracy for predicting future cGVHD/NRM is not established.METHODSWe developed BIOPREVENT (BIOmarkers PREVENTion), a ML algorithm using data from 1,310 HCT recipients, incorporating 7 plasma proteins measured at Day 90/100 post-HCT and 9 clinical variables. Patients were divided into training and validation datasets. ML models - including CoxXGBoost, Group SCAD, Adaptive Group Lasso, Random Survival Forests, and Bayesian Additive Regression Trees (BART) - were used to estimate time-varying Area Under the ROC Curve (AUCt) at Days 180, 270, 360, and 540. Deep learning mode

SUBMITTER: Martens MJ 

PROVIDER: S-EPMC12904722 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature