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A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
09:02

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Published on: February 2, 2021

A simple algorithm to predict incident kidney disease.

Abhijit V Kshirsagar1, Heejung Bang, Andrew S Bomback

  • 1Division of Nephrology and Hypertension, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7155, USA. sagar@med.unc.edu

Archives of Internal Medicine
|December 10, 2008
PubMed
Summary
This summary is machine-generated.

A new algorithm identifies individuals at high risk for chronic kidney disease (CKD) using common risk factors. This tool aids in early detection and prevention strategies for CKD.

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Last Updated: Jun 27, 2026

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
09:02

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion

Published on: February 2, 2021

Area of Science:

  • Nephrology
  • Epidemiology
  • Biostatistics

Background:

  • Chronic kidney disease (CKD) poses a significant global health challenge.
  • Current methods lack algorithms to quantify the impact of combined risk factors on incident CKD.
  • Predictive modeling is needed to identify at-risk populations.

Purpose of the Study:

  • To develop and validate prediction algorithms for incident chronic kidney disease (CKD).
  • To quantify the effect of concurrent risk factors on CKD development.
  • To stratify risk in middle-aged and older adults.

Main Methods:

  • A combined cohort of 14,155 participants from the Atherosclerosis Risk in Communities Study and Cardiovascular Health Study was analyzed.
  • Participants were aged 45 years or older with an estimated glomerular filtration rate (eGFR) > 60 mL/min/1.73 m(2).
  • Prediction algorithms were developed and validated to identify incident CKD (eGFR < 60 mL/min/1.73 m(2)) over a 9-year follow-up.

Main Results:

  • Three prediction algorithms were evaluated, including a simplified 8-predictor model.
  • The simplified model identified age, anemia, female sex, hypertension, diabetes mellitus, peripheral vascular disease, and history of heart failure/cardiovascular disease as key predictors.
  • A numeric score of ≥3 in the simplified model captured 70% of incident cases with a 17% positive predictive value for developing CKD.

Conclusions:

  • A validated algorithm using common variables effectively stratifies risk for future CKD in middle-aged and older adults.
  • This tool can inform population-level prevention initiatives.
  • The algorithm facilitates risk-based discussions between healthcare providers and patients regarding kidney disease.