Role of Urinary Biomarkers in RWE Clinical Paradigm

ABSTRACT

Purpose: Due to heterogeneity observed in the kidney transplant population, it has been extremely challenging for traditional methods such as histopathology to predict graft outcomes. In this real-world evidence(RWE) study, we applied machine learning (ML) models to a multi-analyte urinary biomarker assay to predict whether a kidney allograft would experience a rejection episode. Method(s) A cohort of 550 (37.5% biopsy matched) urine samples from patients across 3 renal transplant centers were used to develop a predictive ML model (scaled 0-100) to prognosticate allograft failure. Samples were collected between 1-1539 days post-transplant from allograft recipients with ages ranging from 7-77 years. Of the 206 biopsy matched samples, acute kidney allograft rejection (AR) and no-rejection (NR) phenotypes were confirmed in 136 and 70 respectively. We also evaluated the developed ML model on two additional cohorts of 15 COVID+ transplant recipients and 30 non-transplant healthy population. The ML model incorporates clinico-demographics with 6 urinary biomarkers Clusterin, total protein, CXCL10, Creatinine, cfDNA and methylated cfDNA. Monte Carlo confidence intervals for the model incorporated biomarker assay and sample variances. Result(s) The novel rejection score was able to discriminate AR from NR efficiently. Score below 32 classified stable allograft, score range of 32 - 55 identified progression of AR, and Score > 55 identified AR with high sensitivity 92%, and specificity 89%;AUC 96% and accuracy 91%(figure). The associated NPV and PPV of 87% and 93% respectively. In the COVID cohort with 86% clinician assessed rejection, the median score was 51(IQR30-87). In the non-transplants the median score was 19(IQR13-26). It was established that presence of COVID was not a confounder in the model. Conclusion(s) The accuracy of the novel rejection score emphasizes the promise of applying ML algorithms as an aid to decision-making in evaluating graft outcomes with high sensitivity and specificity. Moreover, this RWE retrospective analysis demonstrates the efficacy of the urine multi-analyte approach to accurately predict acute rejection in kidney transplant recipients. (Figure Presented).