Initial Validation of a Machine Learning-Derived Prognostic Test (KidneyIntelX) Integrating Biomarkers and Electronic Health Record Data To Predict Longitudinal Kidney Outcomes
Kinsuk Chauhan, Girish N. Nadkarni, Fergus Fleming, James R. McCullough, Cijiang He, John Quackenbush, Barbara Murphy, Michael Donovan, Steven G. Coca, Joseph V. Bonventre
Abstract
Background Individuals with type 2 diabetes (T2D) or the apolipoprotein L1 high-risk ( APOL1 -HR) genotypes are at increased risk of rapid kidney function decline (RKFD) and kidney failure. We hypothesized that a prognostic test using machine learning integrating blood biomarkers and longitudinal electronic health record (EHR) data would improve risk stratification. Methods We selected two cohorts from the Mount Sinai Bio Me Biobank: T2D ( n =871) and African ancestry with APOL1 -HR ( n =498). We measured plasma tumor necrosis factor receptors (TNFR) 1 and 2 and kidney injury molecule-1 (KIM-1) and used random forest algorithms to integrate biomarker and EHR data to generate a risk score for a composite outcome: RKFD (eGFR decline of ≥5 ml/min per year), or 40% sustained eGFR decline, or kidney failure. We compared performance to a validated clinical model and applied thresholds to assess the utility of the prognostic test (KidneyIntelX) to accurately stratify patients into risk categories. Results Overall, 23% of those with T2D and 18% of those with APOL1 -HR experienced the composite kidney end point over a median follow-up of 4.6 and 5.9 years, respectively. The area under the receiver operator characteristic curve (AUC) of KidneyIntelX was 0.77 (95% CI, 0.75 to 0.79) in T2D, and 0.80 (95% CI, 0.77 to 0.83) in APOL1 -HR, outperforming the clinical models (AUC, 0.66 [95% CI, 0.65 to 0.67] and 0.72 [95% CI, 0.71 to 0.73], respectively; P <0.001). The positive predictive values for KidneyIntelX were 62% and 62% versus 46% and 39% for the clinical models ( P <0.01) in high-risk (top 15%) stratum for T2D and APOL1 -HR, respectively. The negative predictive values for KidneyIntelX were 92% in T2D and 96% for APOL1 -HR versus 85% and 93% for the clinical model, respectively ( P =0.76 and 0.93, respectively), in low-risk stratum (bottom 50%). Conclusions In patients with T2D or APOL1 -HR, a prognostic test (KidneyIntelX) integrating biomarker levels with longitudinal EHR data significantly improved prediction of a composite kidney end point of RKFD, 40% decline in eGFR, or kidney failure over validated clinical models.