Advertisement

Using Body Mass Index Data in the Electronic Health Record to Calculate Cardiovascular Risk

      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 clinicaltrials.gov NCT01077388.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to American Journal of Preventive Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • National Center for Health Statistics
        Deaths and mortality.
        • Expert Panel on Detection Evaluation and Treatment of High Blood Cholesterol in Adults
        Executive summary of the third report of The National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III).
        Jama. 2001; 285: 2486-2497
        • Grundy S.M.
        • Cleeman J.I.
        • Merz C.N.
        • et al.
        • Coordinating Committee of the National Cholesterol Education Program
        Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III Guidelines.
        J Am Coll Cardiol. 2004; 44: 720-732
        • U.S. Preventive Services Task Force
        Screening for lipid disorders in adults.
        • Jackson R.
        • Lawes C.M.
        • Bennett D.A.
        • Milne R.J.
        • Rodgers A.
        Treatment with drugs to lower blood pressure and blood cholesterol based on an individual's absolute cardiovascular risk.
        Lancet. 2005; 365: 434-441
        • Persell S.D.
        • Dunne A.P.
        • Lloyd-Jones D.M.
        • Baker D.W.
        Electronic health record-based cardiac risk assessment and identification of unmet preventive needs.
        Med Care. 2009; 47: 418-424
        • D'Agostino Sr, R.B.
        • Vasan R.S.
        • Pencina M.J.
        • et al.
        General cardiovascular risk profile for use in primary care: the Framingham Heart Study.
        Circulation. 2008; 117: 743-753
        • Gaziano T.A.
        • Young C.R.
        • Fitzmaurice G.
        • Atwood S.
        • Gaziano J.M.
        Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I Follow-up Study cohort.
        Lancet. 2008; 371: 923-931
        • National Heart Lung and Blood Institute, Boston University
        Framingham Heart Study.
        • Lin L.I.
        A concordance correlation coefficient to evaluate reproducibility.
        Biometrics. 1989; 45: 255-268
        • Pignone M.P.
        • Phillips C.J.
        • Lannon C.M.
        • et al.
        Screening for lipid disorders.
        • Robertson I.
        • Phillips A.
        • Mant D.
        • et al.
        Motivational effect of cholesterol measurement in general practice health checks.
        Br J Gen Pract. 1992; 42: 469-472
        • Elton P.J.
        • Ryman A.
        • Hammer M.
        • Page F.
        Randomised controlled trial in northern England of the effect of a person knowing their own serum cholesterol concentration.
        J Epidemiol Community Health. 1994; 48: 22-25
        • Hanlon P.
        • McEwen J.
        • Carey L.
        • et al.
        Health checks and coronary risk: further evidence from a randomised controlled trial.
        Bmj. 1995; 311: 1609-1613
        • Sheridan S.L.
        • Viera A.J.
        • Krantz M.J.
        • et al.
        • Cardiovascular Health Intervention Research and Translation Network Work Group on Global Coronary Heart Disease Risk
        The effect of giving global coronary risk information to adults: a systematic review.
        Arch Intern Med. 2010; 170: 230-239
        • Slack J.
        Risks of ischaemic heart-disease in familial hyperlipoproteinaemic states.
        Lancet. 1969; 2: 1380-1382
        • Stone N.J.
        • Levy R.I.
        • Fredrickson D.S.
        • Verter J.
        Coronary artery disease in 116 kindred with familial type II hyperlipoproteinemia.
        Circulation. 1974; 49: 476-488
        • Law M.R.
        • Wald N.J.
        • Thompson S.G.
        By how much and how quickly does reduction in serum cholesterol concentration lower risk of ischaemic heart disease?.
        Bmj. 1994; 308: 367-372
        • Goldman R.E.
        • Parker D.R.
        • Eaton C.B.
        • et al.
        Patients' perceptions of cholesterol, cardiovascular disease risk, and risk communication strategies.
        Ann Fam Med. 2006; 4: 205-212
        • Persell S.D.
        • Zei C.
        • Cameron K.A.
        • Zielinski M.
        • Lloyd-Jones D.M.
        Potential use of 10-year and lifetime coronary risk information for preventive cardiology prescribing decisions: a primary care physician survey.
        Arch Intern Med. 2010; 170: 470-477
        • Mosca L.
        • Linfante A.H.
        • Benjamin E.J.
        • et al.
        National study of physician awareness and adherence to cardiovascular disease prevention guidelines.
        Circulation. 2005; 111: 499-510
        • National Heart Lung and Blood Institute
        Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents: summary report.
        • U.S. Preventive Services Task Force
        Screening for lipid disorders in children: recommendation statement.
        • Psaty B.M.
        • Rivara F.P.
        Universal screening and drug treatment of dyslipidemia in children and adolescents.
        Jama. 2011 Dec 15; ([Epub ahead of print])
        • Liew S.M.
        • Doust J.
        • Glasziou P.
        Cardiovascular risk scores do not account for the effect of treatment: a review.
        Heart. 2011; 97: 689-697
        • D'Agostino Sr, R.B.
        • Grundy S.
        • Sullivan L.M.
        • Wilson P.
        • Group CHDRP
        Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation.
        Jama. 2001; 286: 180-187
        • Hippisley-Cox J.
        • Coupland C.
        • Vinogradova Y.
        • et al.
        Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2.
        Bmj. 2008; 336: 1475-1482