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A time-dependent predictive model for cardiocerebral vascular events in chronic hemodialysis patients: insights from

Haowen Zhong1,2, Mengbi Zhang1,2, Yingye Xie1,2

  • 1Department of Nephrology, Dongguan Tungwah Hospital, Dongguan, China.

Frontiers in Medicine
|June 19, 2025
PubMed

Insights

Predicting cardiocerebral vascular events (CVCs) in hemodialysis (HD) patients is crucial. A new time-dependent model using routine lab tests accurately forecasts CVC progression in HD patients.

Area of Science:

  • Nephrology
  • Cardiology
  • Neurology
  • Biostatistics

Background:

  • Conventional risk factors for cardiocerebral vascular events (CVCs) are not directly applicable to hemodialysis (HD) patients.
  • Accurate risk prediction for CVCs is essential for clinical decision-making in HD patients.

Purpose of the Study:

  • To develop and validate time-dependent predictive models for CVC progression specifically in HD patients.

Main Methods:

  • Development and validation of time-dependent predictive models using demographic, clinical, and laboratory data from 3 dialysis centers (2017-2021).
  • Utilized time-dependent Cox proportional hazards regression.
  • Assessed model performance using concordance index, Akaike information criterion, and net reclassification improvement.

Main Results:

  • The most accurate predictive model incorporated age, sex, hemoglobin, serum albumin, serum phosphate, white blood cell count, blood flow rate, and ultrafiltration volume.
  • The model achieved a C-index of 0.704 in the development cohort and 0.775 in the validation cohort.
  • This model demonstrated superior accuracy compared to a traditional Cox model in the validation cohort.

Conclusions:

  • A time-dependent predictive model utilizing routinely collected laboratory tests can accurately forecast the progression of CVCs in hemodialysis patients.
  • This model offers a valuable tool for improving clinical decision-making and patient management in this population.
Abstract

Keywords:
cardiocerebral vascular eventshemodialysislaboratory testspredictive modeltime-dependent

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