Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict rapid progression of diabetic kidney disease

Abstract

ABSTRACTImportanceDiabetic kidney disease (DKD) is the leading cause of kidney failure in the United States and predicting progression is necessary for improving outcomes.ObjectiveTo develop and validate a machine-learned, prognostic risk score (KidneyIntelX™) combining data from electronic health records (EHR) and circulating biomarkers to predict DKD progression.DesignObservational cohort studySettingTwo EHR linked biobanks: Mount Sinai BioMe Biobank and the Penn Medicine Biobank.ParticipantsPatients with prevalent DKD (G3a-G3b with all grades of albuminuria (A1-A3) and G1 & G2 with A2-A3 level albuminuria) and banked plasma.Main outcomes and measuresPlasma biomarkers soluble tumor necrosis factor 1/2 (sTNFR1, sTNFR2) and kidney injury molecule-1 (KIM-1) were measured at baseline. Patients were divided into derivation [60%] and validation sets [40%]. The composite primary end point, progressive decline in kidney function, including the following: rapid kidney function decline (RKFD) (estimated glomerular filtration rate (eGFR) decline of ≥5 ml/min/1.73m2/year), ≥40% sustained decline, or kidney failure within 5 years. A machine learning model (random forest) was trained and performance assessed using standard metrics.ResultsIn 1146 patients with DKD the median age was 63, 51% were female, median baseline eGFR was 54 ml/min/1.73 m2, urine albumin to creatinine ratio (uACR) was 61 mg/g, and follow-up was 4.3 years. 241 patients (21%) experienced progressive decline in kidney function. On 10-fold cross validation in the derivation set (n=686), the risk model had an area under the curve (AUC) of 0.77 (95% CI 0.74-0.79). In validation (n=460), the AUC was 0.77 (95% CI 0.76-0.79). By comparison, the AUC for an optimized clinical model was 0.62 (95% CI 0.61-0.63) in derivation and 0.61 (95% CI 0.60-0.63) in validation. Using cutoffs from derivation, KidneyIntelX stratified 46%, 37% and 16.5% of validation cohort into low-, intermediate- and high-risk groups, with a positive predictive value (PPV) of 62% (vs. PPV of 37% for the clinical model and 40% for KDIGO; p < 0.001) in the high-risk group and a negative predictive value (NPV) of 91% in the low-risk group. The net reclassification index for events into high-risk group was 41% (p<0.05).Conclusions and RelevanceA machine learned model combining plasma biomarkers and EHR data improved prediction of progressive decline in kidney function within 5 years over KDIGO and standard clinical models in patients with early DKD.