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

ABSTRACT:

Aim

Predicting progression in diabetic kidney disease (DKD) is critical to improving outcomes. We sought to develop/validate a machine-learned, prognostic risk score (KidneyIntelX™) combining electronic health records (EHR) and biomarkers.

Methods

This is an observational cohort study of patients with prevalent DKD/banked plasma from two EHR-linked biobanks. A random forest model was trained, and performance (AUC, positive and negative predictive values [PPV/NPV], and net reclassification index [NRI]) was compared with that of a clinical model and Kidney Disease: Improving Global Outcomes (KDIGO) categories for predicting a composite outcome of eGFR decline of ≥5 ml/min per year, ≥40% sustained decline, or kidney failure within 5 years.

Results

In 1146 patients, the m

SUBMITTER: Chan L 

PROVIDER: S-EPMC8187208 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature