Abstract
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 3074 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 3074 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 342-347 |
| Number of pages | 6 |
| Journal | American Journal of Preventive Medicine |
| Volume | 42 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 2012 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health
- Epidemiology
Cite this
Using body mass index data in the electronic health record to calculate cardiovascular risk. / Green, Beverly B.; Anderson, Melissa L.; Cook, Andrea J.; Catz, Sheryl L; Fishman, Paul A.; McClure, Jennifer B.; Reid, Robert.
In: American Journal of Preventive Medicine, Vol. 42, No. 4, 04.2012, p. 342-347.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Using body mass index data in the electronic health record to calculate cardiovascular risk
AU - Green, Beverly B.
AU - Anderson, Melissa L.
AU - Cook, Andrea J.
AU - Catz, Sheryl L
AU - Fishman, Paul A.
AU - McClure, Jennifer B.
AU - Reid, Robert
PY - 2012/4
Y1 - 2012/4
N2 - 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 3074 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 3074 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.
AB - 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 3074 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 3074 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.
UR - http://www.scopus.com/inward/record.url?scp=84858634773&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84858634773&partnerID=8YFLogxK
U2 - 10.1016/j.amepre.2011.12.009
DO - 10.1016/j.amepre.2011.12.009
M3 - Article
C2 - 22424246
AN - SCOPUS:84858634773
VL - 42
SP - 342
EP - 347
JO - American Journal of Preventive Medicine
JF - American Journal of Preventive Medicine
SN - 0749-3797
IS - 4
ER -