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<p>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. 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 &lt; 60 mL/min/1.73&#xa0;m² or proteinuria at baseline. The median age of participants was 69.0 years [IQR: 62.0–77.0], and 52% (5,493/10,474) of the cohort were male. The Median baseline eGFR was 52.7 mL/min/1.73&#xa0;m² [IQR: 44.7–57.8]. Predictors included demographic, clinical, and laboratory data. We compared three models: linear regression, LightGBM, and LSTM networks. Among 10,474 patients (median age 69.0 years), the LightGBM model achieved superior performance (RMSE = 2.95 mL/min/1.73&#xa0;m²/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. 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.</p>

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Prediction of estimated glomerular filtration rate slope and kidney prognosis of patients with chronic kidney disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月17日

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  • Hajime Nagasu,
  • Takaya Nakashima,
  • Katsuhito Ihara,
  • Ryo Fujimori,
  • Tadahiro Goto,
  • Daisuke Nitta,
  • Seiji Kishi,
  • Tamaki Sasaki,
  • Naoki Kashihara

摘要

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. 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/1.73 m² or proteinuria at baseline. The median age of participants was 69.0 years [IQR: 62.0–77.0], and 52% (5,493/10,474) of the cohort were male. The Median baseline eGFR was 52.7 mL/min/1.73 m² [IQR: 44.7–57.8]. Predictors included demographic, clinical, and laboratory data. We compared three models: linear regression, LightGBM, and LSTM networks. Among 10,474 patients (median age 69.0 years), the LightGBM model achieved superior performance (RMSE = 2.95 mL/min/1.73 m²/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. 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.