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Cited by 222Health Promotion and Cardiovascular PreventionLipoproteins and Cardiovascular HealthPatient-Provider Communication in Healthcare

Cardiovascular Risk-Estimation Systems in Primary Prevention: Do They Differ? Do They Make a Difference? Can We See the Future?

Open access
Cooney, Marie Therese, Dudina, Alexandra, D'Agostino, Ralph, Graham, Ian M.·2010·Circulation
DOI 10.1161/circulationaha.109.852756
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Abstract

I n this review of cardiovascular risk estimation, we focus on 4 key aspects: (1) The rationale for estimating cardiovascular risk; (2) a comparison of current cardiovascular disease (CVD) risk-estimation systems; (3) an examination of whether there is any evidence that cardiovascular risk estimation improves patient outcomes; and (4) how CVD risk estimation may change in the future.Some of the material presented is derived from a recent general review of cardiovascular risk scores 1 that we have used to underpin the present, more focused discussion. The Rationale for CVD Risk EstimationThe atherosclerosis underlying most CVD is rarely the result of a single risk factor, such as familial hyperlipidemia, but more usually the end result of the combined effect of several risk factors.If one considers single risk factors, even if the evidence for intervention is based on randomized controlled trials, either overtreatment or undertreatment may result.Consider, for example, Table 1, taken from the current Joint European Guidelines on the prevention of CVD in clinical practice.2 Who should receive the statin?The 60-year-old woman with a blood cholesterol level of 8 mmol/L (309 mg/dL) and a 2% 10-year risk of fatal CVD or the man of the same age with a cholesterol level of 5 mmol/L (193 mg/dL) but a 10-fold higher risk because of multiple other risk factors?Current therapeutic trial data do not tell us, but logic would suggest the man, along with, of course, attention to all other factors.These considerations have led the authors of all current guidelines to stress the need to consider the likely impact of all risk factors before making clinical management decisions and, in most cases, to recommend a system of evaluating combined risk factor effects. [2][3][4][5] To be clinically useful, a CVD risk-estimation system should be methodologically robust and easy to use, should address clinically relevant risk factors, and should result in a measurable health gain.These issues are addressed in the next section.

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  • Most guidelines adopt one of the available risk assessment systems.

    “Most guidelines use one of these risk assessment systems [6-8].”

    From 2019 ESC/EAS guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk (2019) · cites this paper as [8]

  • Risk age is independent of the CV endpoint used, can be used in any population regardless of baseline risk or secular mortality changes, and therefore avoids recalibration.

    “Risk age has been shown to be independent of the CV endpoint used [6,8], can be used in any population regardless of baseline risk or secular changes in mortality, and therefore avoids the need for recalibration.”

    From 2019 ESC/EAS guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk (2019) · cites this paper as [8]

  • Most risk estimation systems perform similarly in populations similar to their derivation population and can be recalibrated for other populations.

    “In practice, most risk estimation systems perform rather similarly when applied to populations recognizably similar to that from which the risk estimation system was derived, 6,7 and can be re-calibrated for use in different populations.6”

    From ESC/EAS Guidelines for the management of dyslipidaemias (2011) · cites this paper as [7]

  • Most guidelines use one of the available risk estimation systems.

    “Most guidelines use one of these risk estimation systems. 50-52”

    From 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias (2016) · cites this paper as [52]

  • Risk age has been shown to be independent of the cardiovascular endpoint used.

    “Risk age has been shown to be independent of the CV endpoint used, 51,52 which bypasses the dilemma of whether to use a risk estimation system based on CVD mortality or on the more attractive but less reliable endpoint of total CVD events.”

    From 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias (2016) · cites this paper as [52]

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Authors
Cooney, M. T., Dudina, A., D'Agostino, R., & Graham, I. M.
Affiliations
  • Boston University (US)
  • From the Department of Cardiology (M.T.C., A.D., I.M.G.), Adelaide Meath Hospital incorporating the National Children’s Hospital Tallaght, Dublin, Ireland, and Mathematics and Statistics Department (R.D.), Boston University, Boston, Mass.
ORCID
Venue
Circulation
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2010-07-20
Volume
122
Issue
3
Pages
300-310
ISSN
  • 0009-7322 (ISSN-L)
  • 1524-4539
DOI
10.1161/circulationaha.109.852756
OpenAlex
W2149027941
Cited by
222
References
93
Citations by year
2025: 7 · 2024: 11 · 2023: 8 · 2022: 8 · 2021: 15 · 2020: 10 · 2019: 22 · 2018: 13 · 2017: 13 · 2016: 23 · 2015: 22 · 2014: 16 · 2013: 17 · 2012: 19
FWCI
17.08
Type
journal article
Language
en
Open access
Locations
  • Circulation (publishedVersion)
  • PubMed (publishedVersion)
  • Lenus, The Irish Health Repository (Dr Steevens Hospital Library) (submittedVersion)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
  • Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
  • Health Sciences › Health Professions › General Health Professions › Patient-Provider Communication in Healthcare
Keywords
cardiovascular risk prediction, cardiovascular risk score, primary prevention, cardiovascular disease, risk factor combinations, atherosclerosis, statin therapy, overtreatment and undertreatment, risk prediction model, clinical decision-making
MeSH terms
Cardiovascular Diseases, Humans, Predictive Value of Tests, Risk Factors, Models, Statistical, Risk Assessment

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