RenoTrue: A diabetes-specific machine learning model to estimate glomerular filtration rate for people with diabetes

Existing methods for estimating GFR in people with diabetes have shown inaccuracies when compared to mGFR measurements. We developed and validated an artificial neural network - RenoTrue to improve estimating GFR in people with diabetes.

5,619 individuals from five international cohorts with type 1 and type 2 diabetes was split into training (70%), validation (10%) and test (20%) datasets. RenoTrue was developed to estimate GFR using age, sex, and serum creatinine. The performance was evaluated in the test dataset by estimating agreement, bias (mean difference), and accuracy (p30), and compared to CKD-EPI estimates through a multi-level mixed effect regression model.

Median mGFR was 75 ml/ min per 1.73 m² [IQR: 49, 100] and median age was 59 years [IQR: 38, 69]. RenoTrue demonstrated high agreement (ICC: 0.87 (95% CI: 0.78, 0.93)), low bias (-0.57 (95% CI: -1.59, 0.46) ml/min per 1.73 m²) and p30 of 81% (95% CI: 79%, 83%) compared to mGFR measurements. The 2009 CKD-EPI equation had an ICC of 0.86 (95% CI: 0.77, 0.92), bias of 4.17 (95% CI: 3.14, 5.20) ml/min per 1.73 m² and p30 of 74% (95% CI: 72%, 77%).

For people with diabetes, RenoTrue demonstrated better performance compared to the 2009 CKD-EPI equation in terms of estimating GFR across the full range of GFR.