Using body mass index data in the electronic health record to calculate cardiovascular risk

Am J Prev Med. 2012 Apr;42(4):342-7. doi: 10.1016/j.amepre.2011.12.009.


Background: Multivariable cardiovascular disease (CVD) risk calculators, such as the Framingham risk equations, can be used to identify populations most likely to benefit from treatments to decrease risk.

Purpose: To determine the proportion of adults within an electronic health record (EHR) for whom Framingham CVD risk scores could be calculated using cholesterol (lab-based) and/or BMI (BMI-based) formulae.

Methods: EHR data were used to identify patients aged 30-74 years with no CVD and at least 2 years continuous enrollment before April 1, 2010, and relevant data from the preceding 5-year time frame. Analyses were conducted between 2010 and 2011 to determine the proportion of patients with a lab- or BMI-based risk score, the data missing, and the concordance between scores.

Results: Of 122,270 eligible patients, 59.7% (n=73,023) had sufficient data to calculate the lab-based risk score and 84.1% (102,795) the BMI-based risk score. Risk categories were concordant in 78.2% of patients. When risk categories differed, BMI-based risk was almost always in a higher category, with 20.3% having a higher and 1.4% a lower BMI- than lab-based risk score. Concordance between lab- and BMI-based risk was greatest among those at lower estimated risk, including people who were younger, female, without diabetes, not obese, and those not on blood pressure- or lipid-lowering medications.

Conclusions: EHR data can be used to classify CVD risk for most adults aged 30-74 years. In the population for the current study, CVD risk scores based on BMI could be used to identify those at low risk for CVD and potentially reduce unnecessary laboratory cholesterol testing.

Trial registration: This study is registered at NCT01077388.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adult
  • Age Factors
  • Aged
  • Body Mass Index*
  • Cardiovascular Diseases / epidemiology*
  • Cardiovascular Diseases / prevention & control
  • Cholesterol / blood*
  • Electronic Health Records / statistics & numerical data*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Risk Assessment / methods
  • Risk Factors
  • Sex Factors
  • Unnecessary Procedures


  • Cholesterol

Associated data