Initial Validation of a Machine Learning-Derived Prognostic Test (KidneyIntelX) Integrating Biomarkers and Electronic Health Record Data To Predict Longitudinal Kidney Outcomes.

BACKGROUND

Individuals with type 2 diabetes (T2D) or the apolipoprotein L1 high-risk (-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 Biobank: T2D (=871) and African ancestry with -HR (=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 -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 -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; <0.001). The positive predictive values for KidneyIntelX were 62% and 62% versus 46% and 39% for the clinical models (<0.01) in high-risk (top 15%) stratum for T2D and -HR, respectively. The negative predictive values for KidneyIntelX were 92% in T2D and 96% for -HR versus 85% and 93% for the clinical model, respectively (=0.76 and 0.93, respectively), in low-risk stratum (bottom 50%).

CONCLUSIONS

In patients with T2D or -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.