A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease.

backgroundIndividuals with diabetic kidney disease (DKD) often suffer cardiac and kidney events. We sought to develop an accurate means by which to stratify risk in DKD.

methodsClinical variables and biomarkers were evaluated for their ability to predict the adjudicated primary composite endpoint of CREDENCE (Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation) by 3 years. Using machine learning techniques, a parsimonious risk algorithm was developed.

resultsThe final model included age, body-mass index, systolic blood pressure, and concentrations of N-terminal pro-B type natriuretic peptide, high sensitivity cardiac troponin T, insulin-like growth factor binding protein-7 and growth differentiation factor-15. The model had an in-sample C-statistic of 0.80 (95% CI = 0.77-0.83; P < 0.001). Dividing results into low, medium and high risk categories, for each increase in level the hazard ratio increased by 3.43 (95% CI = 2.72-4.32; P < 0.001). Low risk scores had negative predictive value of 94%, while high risk scores had positive predictive value of 58%. Higher values were associated with shorter time to event (log rank P < 0.001). Rising values at 1 year predicted higher risk for subsequent DKD events. Canagliflozin treatment reduced score results by 1 year with consistent event reduction across risk levels. Accuracy of the risk model was validated in separate cohorts from CREDENCE and the generally lower risk Canagliflozin Cardiovascular Assessment Study.

conclusionsWe describe a validated risk algorithm that accurately predicts cardio-kidney outcomes across a broad range of baseline risk.

trial registrationCREDENCE (Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation; NCT02065791) and CANVAS (Canagliflozin Cardiovascular Assessment Study; NCT01032629/NCT01989754).