Evidence map›Paper›PMID 22424246›Full record

ArticleAmerican journal of preventive medicine2012

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

Beverly B Green, Melissa L Anderson, Andrea J Cook, Sheryl Catz, Paul A Fishman, Jennifer B McClure, Robert Reid

Registry-linked trialAbstract read
In one paragraph

Article in American journal of preventive medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01077388 (Collaborative Behavioral e-Care to Decrease Cardiovascular Risk), which is not on this map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
3.2field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT01077388 phase3completednot on this map

Collaborative Behavioral e-Care to Decrease Cardiovascular Risk (e-Compare)

TypeinterventionalSponsorKaiser PermanenteRan2010 to 2012Enrolled100ConditionsCVDArmsDietician electronic support
3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.

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  19. The electronic health record for translational research.Journal of cardiovascular translational research · 2014
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Beverly B GreenGroup Health Permanente, University of Washington, Seattle, USA. green.b@ghc.org
Melissa L Anderson
Andrea J Cook
Sheryl Catz
Paul A Fishman
Jennifer B McClure
Robert Reid
Kaiser Permanente Washington Health Research Institute · USUniversity of Washington · USGroup Health Cooperative · US

Funding

Collaborative Behavioral e-Care to Decrease Cardiovascular Risk (e-Compare)RC1HL100590 · NHLBI · KAISER FOUNDATION HEALTH PLAN OF WASHINGTON · PI GREEN, BEVERLY BETH · 2009 to 2010
$989k
NHLBI NIH HHS RC1 HL100590NHLBI NIH HHS RC1HL100590
6 · The paper itself

Abstract

backgroundMultivariable 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.

methodsEHR 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.

resultsOf 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.

conclusionsEHR 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 registrationThis study is registered at clinicaltrials.gov NCT01077388.

Indexed as

Body Mass IndexAdultAgedAge FactorsCardiovascular DiseasesCholesterolElectronic Health RecordsFemaleHumansMaleMiddle AgedRisk AssessmentRisk FactorsSex FactorsUnnecessary ProceduresCholesterol

Identifiers

PMID22424246
PMCPMC3308122
OpenAlexW2085140923

What OpenQuestion holds

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LicenceTDM
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.