A machine learning model for predicting worsening renal function using one-year time series data in patients with type 2 diabetes.

Background And Aims: To prevent end-stage renal disease caused by diabetic kidney disease, we created a predictive model for high-risk patients using machine learning.

Methods And Results: The reference point was the time at which each patient's estimated glomerular filtration rate (eGFR) first fell below 60 mL/min/1.73 m.

One-year eGFR decline rate is a good predictor of prognosis of renal failure in patients with type 2 diabetes.

It is difficult to distinguish the onset of renal function decline from the typical variation in estimated glomerular filtration rate (eGFR) measurements in clinical practice. In this study, we used data analysis incorporating smoothing techniques to identify significant trends despite large amounts of noise. We identified the starting points of meaningful eGFR decline based on eGFR trajectories.