Variation among cardiovascular risk calculators in relative risk increases with identical risk factor increases.
BACKGROUND
Risk estimates for the same patient can vary substantially among cardiovascular risk calculators and the reasons are not fully explained. We compared the relative risk increases for consistent risk factors changes across different cardiovascular risk calculators.


METHODS
Five clinicians independently selected 16 calculators providing absolute risk estimations. Hypothetical patients were generated using a combination of seven risk factors [age, gender, smoking, blood pressure, high-density lipoprotein (HDL), total cholesterol and diabetes] dichotomized to high and low risk, generating 2(7) patients (128 total). Relative risk increases due to specific risk factors were determined and compared.


RESULTS
The 16 selected calculators were from six countries, used 5- and 10-year predictions, and estimated CVD or coronary heart disease risk. Across the different calculators for non-diabetic patients, changing age from 50 to 70 produced average relative risk increases from 82 to 395%, gender (female to male) 35-225%, smoking status 31-118%, systolic blood pressure (120-160 mmHg) 16-124%, total cholesterol (4-7 mmol/L) 51-302% and HDL (1.3-0.8 mmol/L) 27-133%. Similar results were found among diabetic patients. Some calculators appeared to have consistently higher relative risk increases over multiple risk factors.


CONCLUSIONS
Cardiovascular risk calculators weigh the same risk factors differently. For each risk factor, the relative risk increase from the calculator with the highest increase was generally three to eight times greater than the relative risk increase from the calculator with lowest increase. This likely contributes to some of the inconsistency in risk calculator estimation. It also limits the use of risk calculators in estimating the benefits of therapy.
Evidence-Based Medicine, Department of Family Medicine-Research Program, University of Alberta, 6-10 University Terrace, Edmonton, AB, T6G 2T4, Canada. michael.allan@ualberta.ca.
G Michael
GM
Allan
Evidence-Based Medicine, Department of Family Medicine-Research Program, University of Alberta, 6-10 University Terrace, Edmonton, AB, T6G 2T4, Canada. faeze.nouri@gmail.com.
Faeze
F
Nouri
Evidence-Based Medicine, Department of Family Medicine-Research Program, University of Alberta, 6-10 University Terrace, Edmonton, AB, T6G 2T4, Canada. tina.korownyk@ualberta.ca.
Christina
C
Korownyk
Evidence-Based Medicine, Department of Family Medicine-Research Program, University of Alberta, 6-10 University Terrace, Edmonton, AB, T6G 2T4, Canada. mkolber@ualberta.ca.
Michael R
MR
Kolber
Alberta Research Centre for Health Evidence, University of Alberta, Edmonton, AB, Canada. bv1@ualberta.ca.
Ben
B
Vandermeer
Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada. james.mccormack@ubc.ca.
James
J
McCormack
D000367:Age Factors; D000368:Aged; D001794:Blood Pressure / Q000502:physiology; D002318:Cardiovascular Diseases / Q000097:blood / Q000175:diagnosis* / Q000503:physiopathology; D002784:Cholesterol / Q000097:blood; D003920:Diabetes Mellitus / Q000503:physiopathology; D005260:Female; D006801:Humans; D008075:Lipoproteins, HDL / Q000097:blood; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods* / Q000706:statistics & numerical data*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012737:Sex Factors; D012907:Smoking / Q000503:physiopathology
D008075:Lipoproteins, HDL; D002784:Cholesterol