Enhancing post-kidney transplant prognostication: an interpretable machine learning approach for longitudinal outcome prediction.

ABSTRACT: Kidney transplantation offers life-extending treatment for patients with end-stage renal disease, yet long-term risks of graft loss and death persist. Traditional prediction models using only baseline data often fail to capture patients' evolving health status post-transplant. In this study, we propose a two-stage machine learning (ML) framework for dynamic, next-year risk prediction of graft loss and death, updated annually with newly available clinical and laboratory data. Using a multi-center cohort from the Swiss Transplant Cohort Study (STCS), we trained and evaluated five ML models across 13 years of follow-up, demonstrating that incorporating longitudinal data significantly improved predictive performance compared to baseline-only models. LightGBM achieved the strongest performance,

SUBMITTER: Fan B 

PROVIDER: S-EPMC12627842 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature