Derivation and independent validation of kidneyintelX.dkd: A prognostic test for the assessment of diabetic kidney disease progression

Abstract Aims To develop and validate an updated version of KidneyIntelX (kidneyintelX.dkd) to stratify patients for risk of progression of diabetic kidney disease (DKD) stages 1 to 3, to simplify the test for clinical adoption and support an application to the US Food and Drug Administration regulatory pathway. Methods We used plasma biomarkers and clinical data from the Penn Medicine Biobank (PMBB) for training, and independent cohorts (Bio Me and CANVAS) for validation. The primary outcome was progressive decline in kidney function (PDKF), defined by a ≥40% sustained decline in estimated glomerular filtration rate or end‐stage kidney disease within 5 years of follow‐up. Results In 573 PMBB participants with DKD, 15.4% experienced PDKF over a median of 3.7 years. We trained a random forest model using biomarkers and clinical variables. Among 657 Bio Me participants and 1197 CANVAS participants, 11.7% and 7.5%, respectively, experienced PDKF. Based on training cut‐offs, 57%, 35% and 8% of Bio Me participants, and 56%, 38% and 6% of CANVAS participants were classified as having low‐, moderate‐ and high‐risk levels, respectively. The cumulative incidence at these risk levels was 5.9%, 21.2% and 66.9% in Bio Me and 6.7%, 13.1% and 59.6% in CANVAS. After clinical risk factor adjustment, the adjusted hazard ratios were 7.7 (95% confidence interval [CI] 3.0‐19.6) and 3.7 (95% CI 2.0‐6.8) in Bio Me , and 5.4 (95% CI 2.5‐11.9) and 2.3 (95% CI 1.4‐3.9) in CANVAS, for high‐ versus low‐risk and moderate‐ versus low‐risk levels, respectively. Conclusions Using two independent cohorts and a clinical trial population, we validated an updated KidneyIntelX test (named kidneyintelX.dkd), which significantly enhanced risk stratification in patients with DKD for PDKF, independently from known risk factors for progression.