Prediction of Estimated Glomerular Filtration Rate Slope and Kidney Prognosis of Patients with Chronic Kidney Disease

Background Chronic kidney disease (CKD) is a significant global health challenge, yet the application of eGFR slope as a metric for CKD progression remains underdeveloped in primary care settings. Methods Using data from J-CKD-DB-Ex, Japan’s largest CKD database, we developed and validated a machine learning-based model to predict eGFR slope. The study included 10,474 patients aged ≥ 18 years with eGFR < 60 mL/min/1.73m² or proteinuria at baseline. Predictors included demographic, clinical, and laboratory data. We compared three models: linear regression, LightGBM, and LSTM networks. Results Among 10,474 patients (median age 69.0 years), the LightGBM model achieved superior performance (RMSE = 2.95 mL/min/1.73m²/year) compared to LSTM (RMSE = 3.94) and conventional linear regression (RMSE = 15.87). The model was implemented as a web-based application for clinical use. Conclusion This machine learning-based prediction model achieves superior accuracy in estimating eGFR trajectory and enables real-time prediction using single time-point data. The web-based tool supports early identification of high-risk patients, enabling timely interventions and specialist referrals in primary care settings.