Overview
- Developed by MUSC Hollings researchers with CIBMTR collaborators, the study was published Feb. 16 in the Journal of Clinical Investigation.
- The model combines seven immune protein biomarkers measured around day 90–100 post‑transplant with nine clinical variables using Bayesian additive regression trees.
- In testing, biomarker‑informed predictions outperformed clinical data alone, with the largest gains for transplant‑related mortality risk.
- BIOPREVENT stratified patients into distinct low‑ and high‑risk groups with outcome differences observed up to 18 months after transplant.
- The web application supports risk assessment and research rather than treatment decisions, with prospective trials planned to test whether earlier intervention improves outcomes.