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Use of Risk Assessment Tools to Guide Decision-Making in the Primary Prevention of Atherosclerotic Cardiovascular Disease
Open accessAbstract
Risk assessment is a critical step in the current approach to primary prevention of atherosclerotic cardiovascular disease. Knowledge of the 10-year risk for atherosclerotic cardiovascular disease identifies patients in higher-risk groups who are likely to have greater net benefit and lower number needed to treat for both statins and antihypertensive therapy. Current U.S. prevention guidelines for blood pressure and cholesterol management recommend use of the pooled cohort equations to start a process of shared decision-making between clinicians and patients in primary prevention. The pooled cohort equations have been widely validated and are broadly useful for the general U.S. clinical population. But, they may systematically underestimate risk in patients from certain racial/ethnic groups, those with lower socioeconomic status or with chronic inflammatory diseases, and overestimate risk in patients with higher socioeconomic status or who have been closely engaged with preventive healthcare services. If uncertainty remains for patients at borderline or intermediate risk, or if the patient is undecided after a patient-clinician discussion with consideration of risk enhancing factors (e.g., family history), additional testing with measurement of coronary artery calcium can be useful to reclassify risk estimates and improve selection of patients for use or avoidance of statin therapy. This special report summarizes the rationale and evidence base for quantitative risk assessment, reviews strengths and limitations of existing risk scores, discusses approaches for refining individual risk estimates for patients, and provides practical advice regarding implementation of risk assessment and decision-making strategies in clinical practice.
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“Biomarkers such as elevated blood pressure, lipid levels, and inflammatory markers are closely associated with CVD risk, and treatments that reduce these markers can reduce CVD risk in populations with and without diabetes [11–13].”
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The Lancet women and cardiovascular disease Commission: reducing the global burden by 2030
Vogel, B., Mónica Acevedo, B., Appelman, Y., Merz, C. N. B., Chieffo, A., Figtree, G. A., Guerrero, M. E., Kunadian, V., Lam, C. S. P., Maas, A. H. E. M., et al. (2021) · The Lancet · doi:10.1016/s0140-6736(21)00684-x · Cited by 1,587
Lloyd-Jones, D. M., Morris, P. B., Ballantyne, C. M., Birtcher, K. K., Covington, A. M., DePalma, S. M., Minissian, M. B., Orringer, C. E., Smith, S. C., Waring, A. A., et al. (2022) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2022.07.006 · Cited by 569
Self-citationMazzone, P. J., Silvestri, G. A., Patel, S., Kanne, J. P., Kinsinger, L., Wiener, R. S., Hoo, G. W. S., & Detterbeck, F. C. (2018) · CHEST Journal · doi:10.1016/j.chest.2018.01.016 · Cited by 353
Virani, S. S., Morris, P. B., Agarwala, A., Ballantyne, C. M., Birtcher, K. K., Kris-Etherton, P. M., Ladden-Stirling, A. B., Miller, M., Orringer, C. E., & Stone, N. J. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.06.011 · Cited by 341
Self-citationPrediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
Raghunath, S., Ulloa Cerna, A. E., Jing, L., vanMaanen, D. P., Stough, J., Hartzel, D. N., Leader, J. B., Kirchner, H. L., Stumpe, M. C., Hafez, A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0870-z · Cited by 314
Metadata
- Authors
- Lloyd-Jones, D. M., Braun, L. T., Ndumele, C. E., Smith, S. C., Sperling, L. S., Virani, S. S., & Blumenthal, R. S.
- ORCID
- Venue
- Journal of the American College of Cardiology
- Publisher
- Elsevier BV
- Published
- 2019-06
- Volume
- 73
- Issue
- 24
- Pages
- 3153-3167
- ISSN
- 0735-1097 (ISSN-L)
- OpenAlex
- W3137768417
- Cited by
- 302
- References
- 84
- Citations by year
- 2026: 25 · 2025: 54 · 2024: 40 · 2023: 43 · 2022: 44 · 2021: 41 · 2020: 28 · 2019: 26
- FWCI
- 25.12
- Type
- journal article
- Language
- en
- Update notices
- erratum (2019-06-25)
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of the American College of Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Keywords
- atherosclerotic cardiovascular disease, primary prevention, quantitative risk assessment, Pooled Cohort Equations, shared decision-making, statin therapy, antihypertensive therapy, coronary artery calcium, risk reclassification, racial and ethnic disparities, socioeconomic status, chronic inflammatory diseases, family history (medicine), blood pressure control, cholesterol management
- MeSH terms
- Clinical Decision-Making, Decision Making, Shared, Coronary Artery Disease, Female, Humans, Male, Middle Aged, Practice Patterns, Physicians', Primary Prevention, Risk Factors, United States, Risk Assessment, Quality Improvement
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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