Developing a machine learning model to predict renal function decline within three years using one-year longitudinal...

The increasing prevalence of type 2 diabetes mellitus (T2DM) has led to an increase in diabetic kidney disease (DKD), which is presently a major cause of dialysis initiation. Accurately predicting renal function decline is clinically important. This study aimed to evaluate whether machine learning can predict renal outcomes based on one-year fluctuations in the estimated glomerular filtration rate (eGFR) in patients with T2DM. Unlike prior studies, which predicted renal decline only after the eGFR dropped below 60 mL/min/1.73 m² and used pre-SGLT2 inhibitor data, we analyzed post-SGLT2 data and assessed the risk at any clinical time point.

We included outpatient T2DM cases with a mean eGFR ≥45 mL/min/1.73 m². Using one year of retrospective data, we predicted ≥30% eGFR decline over the subsequent 3 years. Data were extracted semiannually, allowing multiple datasets for each patient.

Among the 21,872 datasets, 7,216 met the criteria for analysis and 125 reached the primary endpoint. A baseline model using demographic and average laboratory data achieved an AUC of 0.77. When additional features were added, including smoothed mean proteinuria, glycosylated hemoglobin range, proteinuria intercept, variability indices (e.g., SDs of eGFR, creatinine, hemoglobin, and uric acid), regression coefficients, laboratory extremes, and medication duration, the area under the curve improved to 0.82.

Our machine learning model accurately predicted the renal outcomes at any time point in patients with preserved renal function. Routinely collected clinical data may allow earlier identification of high-risk patients, in turn enabling timely therapeutic intervention.