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Development of Risk Prediction Equations for Incident Chronic Kidney Disease.

Robert G Nelson1, Morgan E Grams2, Shoshana H Ballew2

  • 1Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona.

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Summary

New risk equations can identify individuals at high risk for chronic kidney disease (CKD). These tools, developed using data from over 5 million people, show good accuracy in predicting CKD development.

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Area of Science:

  • Nephrology
  • Epidemiology
  • Biostatistics

Background:

  • Early identification of chronic kidney disease (CKD) risk is crucial for timely intervention and improved patient outcomes.
  • Current assessment tools may not adequately identify individuals at elevated risk for CKD, necessitating the development of more precise prediction models.

Purpose of the Study:

  • To develop and validate risk assessment tools for identifying individuals at increased risk of developing CKD, defined by a reduced estimated glomerular filtration rate (eGFR).

Main Methods:

  • Individual-level data from 34 multinational cohorts (CKD Prognosis Consortium) comprising over 5.2 million participants were analyzed.
  • Two-stage analysis was performed, with models developed separately for individuals with and without diabetes, incorporating demographic and clinical factors.
  • Model performance was assessed using discrimination (C statistic) and calibration, with validation in 9 external cohorts.

Main Results:

  • Risk equations incorporating age, sex, race/ethnicity, eGFR, cardiovascular disease history, smoking status, hypertension, BMI, and albuminuria were developed.
  • For individuals with diabetes, models also included diabetes medications and hemoglobin A1c. The median C statistic for 5-year risk prediction was 0.845 (without diabetes) and 0.801 (with diabetes).
  • Calibration analysis demonstrated acceptable performance across diverse populations, with most study populations showing a slope of observed to predicted risk between 0.80 and 1.25.

Conclusions:

  • Risk prediction equations for incident CKD, developed from a large, diverse population, demonstrate robust discrimination.
  • While calibration was variable across populations, the developed equations show promise for identifying individuals at risk of CKD.
  • Further research is needed to confirm if the clinical application of these risk equations improves patient care and outcomes.