BioWaitIndex: a novel machine learning-derived biomarker for improved prediction of liver transplant waitlist outcomes

BackgroundLiver transplantation remains limited by organ shortage, necessitating precise waitlist risk stratification. The Model for End-Stage Liver Disease (MELD) remains the cornerstone of liver allocation and effectively predicts near-term mortality when assessed dynamically during waitlist follow-up. However, MELD measured at initial transplant evaluation may not fully capture baseline multisystem vulnerability, particularly among candidates with lower MELD scores who may remain on the waitlist for prolonged periods.MethodsThe BioWaitIndex (BWI) was developed using a random forest model in an NHANES development cohort comprising 13,830 participants. The trained model was first temporally validated in a separate, non-overlapping sample from subsequent NHANES cycles comprising 8,151 participants. It was then independently clinically validated in a separate, non-overlapping, IRB-approved cohort of 1,502 adult liver transplant candidates from Houston Methodist Hospital. In the clinical validation cohort, associations between BWI measured at initial transplant evaluation and subsequent waitlist outcomes were assessed using competing-risk regression.ResultsIn multivariable competing-risks analyses with BWI and MELD both measured at initial evaluation, each unit increase in BWI was associated with a greater likelihood of transplantation (sHR 1.02, 95% CI 1.01–1.03; p < 0.001) and a lower risk of waitlist death or clinical deterioration (sHR 0.97, 95% CI 0.97–0.98; p < 0.001). Adding BWI to MELD at evaluation improved net reclassification for waitlist mortality by 31.2% (p < 0.001) and for transplantation by 11.1% (p = 0.026), with consistent associations across sexes.ConclusionBWI may complement dynamic MELD assessment by providing earlier information about baseline multisystem vulnerability and identifying candidates whose risk may not be fully reflected by their MELD score at evaluation.