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Renal failure occurs when the kidneys lose their ability to filter waste products from the blood effectively. It can be classified into two types: acute renal failure (ARF) and chronic renal failure (CRF).
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Predicting 3-year all-cause mortality in patients undergoing hemodialysis using machine learning.

Aiko Okubo1, Toshiki Doi2,3, Kenichi Morii2,3

  • 1Division of Nephrology, Ichiyokai Harada Hospital, 7-10 Kairoyama-cho, Saeki-ku, Hiroshima, 731-5134, Japan. aiko437689@gmail.com.

Journal of Nephrology
|March 6, 2025
PubMed
Summary

This study developed a new risk model using electrocardiogram (ECG) findings to predict death in hemodialysis (HD) patients. The model effectively identifies high-risk individuals, improving patient care and treatment strategies.

Keywords:
HemodialysisMortalityRisk model

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Area of Science:

  • Nephrology
  • Cardiology
  • Medical Informatics

Background:

  • Hemodialysis (HD) patients have low survival rates, with cardiovascular events being the primary cause of mortality.
  • Existing risk models often overlook electrocardiogram (ECG) findings, a crucial aspect of cardiovascular health.

Purpose of the Study:

  • To develop and validate a novel risk prediction model for all-cause mortality in patients undergoing hemodialysis.
  • To incorporate electrocardiogram (ECG) parameters into a risk model for enhanced predictive accuracy.

Main Methods:

  • A cohort of 454 patients undergoing HD was analyzed from April 2008 to March 2021.
  • Multivariate Cox regression identified independent predictors of mortality, including age, serum albumin, stroke history, atrial fibrillation, and corrected QT interval.
  • A nomogram-based risk model was developed and validated using area under the curve (AUC) and calibration plots.

Main Results:

  • The 3-year follow-up revealed a 21.5% mortality rate (98 deaths).
  • The novel risk model demonstrated good predictive performance with an AUC of 0.83 (95% CI, 0.79-0.87), 80.1% sensitivity, and 75.6% specificity.
  • Cross-validation confirmed the model's robustness with an AUC of 0.82.

Conclusions:

  • The developed risk model effectively stratifies hemodialysis patients based on their 3-year all-cause mortality risk.
  • This tool can aid in early identification of high-risk patients, enabling personalized treatment and safer prescriptions.