Implications of Five Different Risk Models In Primary Prevention Guidelines

Maneesh Sud; Atul Sivaswamy; Peter C Austin; Husam Abdel-Qadir; Todd J Anderson; David M J Naimark; Douglas S Lee; Idan Roifman; George Thanassoulis; Karen Tu; Harindra C Wijeysundera; Dennis T Ko

doi:10.1093/ehjqcco/qcae034

Implications of Five Different Risk Models In Primary Prevention Guidelines

Eur Heart J Qual Care Clin Outcomes. 2024 May 11:qcae034. doi: 10.1093/ehjqcco/qcae034. Online ahead of print.

Authors

Maneesh Sud^{1

2

3

4}, Atul Sivaswamy³, Peter C Austin^{2

3}, Husam Abdel-Qadir^{2

3

4

5}, Todd J Anderson^{6

7}, David M J Naimark^{2

3

4}, Douglas S Lee^{2

3

4

8}, Idan Roifman^{1

2

3

4}, George Thanassoulis^{9

10}, Karen Tu^{2

11

12}, Harindra C Wijeysundera^{1

2

3

4}, Dennis T Ko^{1

2

3

4}

Affiliations

¹ Schulich Heart Program, Sunnybrook Health Sciences Centre, University of Toronto, Canada.
² Institute of Health Policy Management, and Evaluation,University of Toronto, Canada.
³ ICES, Toronto, Canada.
⁴ Department of Medicine, University of Toronto, Canada.
⁵ Women's College Hospital, University of Toronto, Canada.
⁶ Libin Cardiovascular Institute of Alberta, Canada.
⁷ Cumming School of Medicine, University of Calgary, Canada.
⁸ Peter Munk Cardiac Centre, University Health Network, University of Toronto, Canada.
⁹ Department of Medicine, McGill University, Canada.
¹⁰ Preventive and Genomic Cardiology, McGill University Health Centre, Canada.
¹¹ North York General Hospital, Toronto Western Family Health Team, University Health Network, University of Toronto, Canada.
¹² Department of Family and Community Medicine, University of Toronto, Canada.

PMID: 38734893
DOI: 10.1093/ehjqcco/qcae034

Abstract

Background: A lack of consensus exists across guidelines as to which risk model should be used for the primary prevention of cardiovascular disease (CVD). Our objective was to determine potential improvements in the number needed to treat (NNT) and number of events prevented (NEP) using different risk models in patients eligible for risk stratification.

Methods: A retrospective observational cohort was assembled from primary care patients in Ontario, Canada between January 1st, 2010, to December 31st, 2014 and followed for up to 5 years. Risk estimation was undertaken in patients 40-75 years of age, without CVD, diabetes, or chronic kidney disease using the Framingham Risk Score (FRS), Pooled Cohort Equations (PCEs), a recalibrated FRS (R-FRS), Systematic Coronary Risk Evaluation 2 (SCORE2), and the low-risk region recalibrated SCORE2 (LR-SCORE2).

Results: The cohort consisted of 47,399 patients (59% women, mean age 54). The NNT with statins was lowest for SCORE2 at 40, followed by LR-SCORE2 at 41, R-FRS at 43, PCEs at 55, and FRS at 65. Models that selected for individuals with a lower NNT recommended statins to fewer, but higher risk patients. For instance, SCORE2 recommended statins to 7.9% of patients (5-year CVD incidence 5.92%). The FRS, however, recommended statins to 34.6% of patients (5-year CVD incidence 4.01%). Accordingly, the NEP was highest for the FRS at 406 and lowest for SCORE2 at 156.

Conclusions: Newer models such as SCORE2 may improve statin allocation to higher risk groups with a lower NNT but prevent fewer events at the population level.

Keywords: Framingham Risk Score; Pooled Cohort Equations; Primary prevention; Risk models; SCORE2; Statins.