Value and Limitations of Existing Scores for the Assessment of Cardiovascular Risk
Open accessAbstract
Atherosclerotic cardiovascular diseases (CVDs) are the biggest causes of death worldwide. In most people, CVD is the product of a number of causal risk factors. Several seemingly modest risk factors may, in combination, result in a much higher risk than an impressively raised single factor. For this reason, risk estimation systems have been developed to assist clinicians to assess the effects of risk factor combinations in planning management strategies. In this article, the performances of the major risk estimation systems are reviewed. Most perform usably well in populations that are similar to the one used to derive the system, and in other populations if calibrated to allow for different CVD mortality rates and different risk factor distributions. The effect of adding "new" risk factors to age, sex, smoking, lipid status, and blood pressure is usually small, but may help to appropriately reclassify some of those patients who are close to a treatment threshold to a more correct "treat/do not treat" category. Risk estimation in the young and old needs more research. Quantification of the hoped-for benefits of the multiple risk estimation approach in terms of improved outcomes is still needed. But, it is likely that the widespread use of such an approach will help to address the issues of both undertreatment and overtreatment.
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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 [6]
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 [6]
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 [6]
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 [51]
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 [51]
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”
From 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias (2016) · cites this paper as [51]
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Zannad, F. M., Backer, G. D., Graham, I. M., Lorenz, M., Mancia, G., Morrow, D. A., Reiner, Ž., Köenig, W., Dallongeville, J., MacFadyen, R. J., et al. (2012) · Fundamental and Clinical Pharmacology · doi:10.1111/j.1472-8206.2011.01023.x · Cited by 46 · see citing works
Self-citationYano, Y., Kim, H. C., Lee, H., Azahar, N., Ahmed, S., Kitaoka, K., Kaneko, H., Kawai, F., Mizuno, A., & Viera, A. J. (2022) · Hypertension · doi:10.1161/hypertensionaha.122.18459 · Cited by 46 · see citing works
Reliability of a 3D Body Scanner for Anthropometric Measurements of Central Obesity
Medina‐Inojosa, J. R. (2016) · Obesity Open Access · doi:10.16966/2380-5528.122 · Cited by 44 · see citing works
Kasartzian, D.-I., & Tsiampalis, T. (2025) · Life · doi:10.3390/life15010094 · Cited by 49 · see citing works
Aspirin for Primary Prevention of Cardiovascular Disease Events
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Current State and Future of Polygenic Risk Scores in Cardiometabolic Disease: A Scoping Review
Phulka, J., Ashraf, M., Bajwa, B. K., Paré, G., & Laksman, Z. (2023) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.122.003834 · Cited by 39 · see citing works
- Authors
- Cooney, M. T., Dudina, A. L., & Graham, I. M.
- Affiliations
- Tallaght University Hospital (IE)
- Corresponding author
- Graham, Ian M.
- Venue
- Journal of the American College of Cardiology
- Publisher
- Elsevier BV
- Published
- 2009-09
- Volume
- 54
- Issue
- 14
- Pages
- 1209-1227
- ISSN
- 0735-1097 (ISSN-L)
- OpenAlex
- W197402492
- Cited by
- 436
- References
- 90
- Citations by year
- 2026: 14 · 2025: 19 · 2024: 14 · 2023: 18 · 2022: 28 · 2021: 28 · 2020: 28 · 2019: 27 · 2018: 28 · 2017: 20 · 2016: 38 · 2015: 34 · 2014: 28 · 2013: 24 · 2012: 26
- FWCI
- 3.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of the American College of Cardiology (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 › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Keywords
- cardiovascular risk assessment, atherosclerotic cardiovascular disease, risk factor combinations, calibration, undertreatment, overtreatment, young and elderly patients, cardiovascular mortality
- MeSH terms
- Age Factors, Cardiovascular Diseases, Humans, Models, Cardiovascular, Risk Factors, Risk Assessment
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