ArticleAmerican journal of preventive medicine2012
Using body mass index data in the electronic health record to calculate cardiovascular risk.
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.
What it found
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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.
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.
Collaborative Behavioral e-Care to Decrease Cardiovascular Risk (e-Compare)
Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- Discrimination and calibration performances of non-laboratory-based and laboratory-based cardiovascular risk predictions: a systematic review.Open heart · 2025Pooled it
- Associations of diabetes, hypertension and obesity with COVID-19 mortality: a systematic review and meta-analysis.BMJ global health · 2023Pooled it
- Clinic, Home, and Kiosk Blood Pressure Measurements for Diagnosing Hypertension: a Randomized Diagnostic Study.Journal of general internal medicine · 2022Trial
- Comparing Non-Laboratory-Based and Laboratory-Based Cardiovascular Risk Predictions: Systematic Review and Meta-Analysis.Health science reports · 2026Review
- Performance of non-laboratory Framingham risk scores using self-reported data for remote cardiovascular risk assessment.Scientific reports · 2025Article
- A comparison of laboratory-based and office-based Framingham risk scores to predict 10-year risk of cardiovascular diseases: a population-based study.Journal of translational medicine · 2023Article
- Laboratory-based versus non-laboratory-based World Health Organization risk equations for assessment of cardiovascular disease risk.BMC medical research methodology · 2023Article
- Comparison of laboratory-based and non-laboratory-based WHO cardiovascular disease risk charts: a population-based study.Journal of translational medicine · 2022Article
- Identifying Predictors of Suicide in Severe Mental Illness: A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS).Frontiers in psychiatry · 2020Article
- Cardiovascular Health Trends in Electronic Health Record Data (2012-2015): A Cross-Sectional Analysis of The Guideline Advantage™.EGEMS (Washington, DC) · 2019Article
- Observational study protocol for evaluating control of hypertension and the effects of social determinants.BMJ open · 2019Article
- Illustrating Informed Presence Bias in Electronic Health Records Data: How Patient Interactions with a Health System Can Impact Inference.EGEMS (Washington, DC) · 2017Article
- A rule-based electronic phenotyping algorithm for detecting clinically relevant cardiovascular disease cases.BMC research notes · 2017Article
- Using Electronic Health Records for Population Health Research: A Review of Methods and Applications.Annual review of public health · 2016Review
- Coronary artery disease risk assessment from unstructured electronic health records using text mining.Journal of biomedical informatics · 2015Article
- Article
- Relationship between cardiovascular risk and lipid testing in one health care system: a retrospective cohort study.BMC health services research · 2015Article
- Article
- The electronic health record for translational research.Journal of cardiovascular translational research · 2014Review
- Hidden in plain sight: bias towards sick patients when sampling patients with sufficient electronic health record data for research.BMC medical informatics and decision making · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
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.
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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.