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Risk Prediction Models in CKD.

Blake Lerner1, Sean Desrochers1, Navdeep Tangri2

  • 1Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada.

Seminars in Nephrology
|April 16, 2017
PubMed
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Chronic kidney disease (CKD) affects millions, increasing mortality. Prediction models like the Kidney Failure Risk Equation improve resource allocation and clinical decisions for CKD patients.

Area of Science:

  • Nephrology
  • Clinical Prediction Modeling
  • Public Health

Background:

  • Chronic kidney disease (CKD) impacts 20 million Americans, leading to significant morbidity and mortality.
  • Efficient resource allocation is crucial for managing CKD and benefiting the healthcare system.
  • Prediction models are underutilized in nephrology despite their potential for clinical decision support.

Purpose of the Study:

  • To highlight the utility of existing prediction models in nephrology.
  • To emphasize the need for developing new prediction models for various CKD outcomes.

Main Methods:

  • The Kidney Failure Risk Equation (KFRE) is presented as an example of a prediction model.
  • KFRE utilizes routinely collected laboratory values for clinical decision-making.
Keywords:
Risk prediction modelschronic kidney diseasedialysis

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Main Results:

  • The KFRE informs critical decisions including referral triage, intensive care needs, modality education timing, and dialysis access planning.
  • The study identifies a lack of predictive models for dialysis survival, quality of life, home modality success, fistula failure, and cardiovascular risk in CKD.

Conclusions:

  • Existing prediction models like KFRE offer valuable tools for managing CKD patients.
  • Further development of novel prediction models is essential to address critical gaps in predicting diverse CKD-related outcomes and improving patient care.