Prediction Model for eGFR Thresholds Guiding the Optimal Timing of Hemodialysis Preparation in Chronic Kidney Disease

Geo Neul Park1, Yoonwon Choi1, Ji Eun Moon2

  • 1Division of Nephrology, Department of Internal Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, 170, Jomaru-ro, Bucheon 14584, Republic of Korea.

Biomedicines
|December 30, 2025
PubMed

Insights

Predicting the best time for hemodialysis preparation in chronic kidney disease (CKD) is complex. This study identified key factors and developed a model to guide personalized end-stage kidney disease (ESKD) planning for better patient outcomes.

Area of Science:

  • Nephrology
  • Internal Medicine
  • Clinical Prediction Models

Background:

  • Chronic kidney disease (CKD) progression involves multiple factors, making optimal hemodialysis preparation timing challenging.
  • Accurate timing for initiating renal replacement therapy is crucial for patient management and outcomes.

Purpose of the Study:

  • To identify predictive factors for end-stage kidney disease (ESKD) progression.
  • To develop and validate a predictive model for guiding hemodialysis preparation timing in CKD patients.

Main Methods:

  • Retrospective analysis of 507 CKD patients progressing to ESKD requiring hemodialysis.
  • Evaluation of estimated glomerular filtration rate (eGFR) at 6 months pre-hemodialysis (eGFR_6M) and its decline trajectory.
  • Development and validation of a regression model using clinical predictors.

Main Results:

  • Mean eGFR_6M was 11.7 mL/min/1.73 m²; higher in patients with diabetes mellitus (DM), cardiovascular disease (CVD), and hypoalbuminemia.
  • Faster eGFR decline observed in patients with DM, liver cirrhosis (LC), and nephrotic-range proteinuria.
  • A predictive model incorporating sex, mobility, DM, CVD, ejection fraction, BUN, and phosphate demonstrated 70-75% P30 performance.

Conclusions:

  • The developed model offers an objective reference for nephrologists to time dialysis preparation.
  • Personalized ESKD life planning can be supported, potentially improving patient outcomes.
  • Identifying predictors aids in proactive management of CKD progression towards ESKD.

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