Development and Validation of Risk Prediction Models for Cardiovascular Events in Black Adults: The Jackson Heart

Ervin R Fox1, Tandaw E Samdarshi1, Solomon K Musani2

  • 1Division of Cardiovascular Diseases, Department of Medicine, University of Mississippi Medical Center, Jackson.

JAMA Cardiology
|July 21, 2016
PubMed

Insights

Cardiovascular disease (CVD) risk prediction models validated in white populations may not be ideal for black adults. Existing Framingham Risk Score and ACC/AHA risk algorithms perform well in black individuals, suggesting a unique calculator may not be needed.

Area of Science:

  • Cardiology
  • Epidemiology
  • Public Health

Background:

  • Cardiovascular disease (CVD) risk assessment is crucial for prevention.
  • Existing CVD risk prediction models are primarily based on white populations.
  • The applicability of these models to black adults remains uncertain.

Purpose of the Study:

  • To develop and validate CVD risk prediction models for black adults.
  • To incorporate standard risk factors, biomarkers, and subclinical disease measures.
  • To assess the performance of new models against existing algorithms.

Main Methods:

  • Utilized data from the Jackson Heart Study (JHS), a longitudinal study of 5301 black adults.
  • Collected data on standard risk factors, biomarkers, and subclinical disease measures.
  • Compared developed models with Framingham Risk Score (FHS) and ACC/AHA risk algorithms.

Main Results:

  • A model combining standard risk factors, B-type natriuretic peptide, and ankle-brachial index showed modest improvement.
  • However, reclassification improvement was not substantially different from FHS or ACC/AHA algorithms.
  • Models demonstrated reasonable discrimination in independent cohorts (ARIC, MESA).

Conclusions:

  • Current FHS and ACC/AHA risk algorithms perform well in black individuals.
  • A unique risk calculator specifically for black adults may not be necessary.
  • Existing models provide valuable risk assessment for black populations.
Abstract

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