Cardiovascular Risk Prediction: Basic Concepts, Current Status, and Future Directions
Abstract
Few topics have received as much attention in the cardio-vascular literature over the last 5 years as risk prediction. The assessment of risk has been a key element in efforts to define risk factors for cardiovascular disease (CVD), to identify novel markers of risk for CVD, to identify and assess potential targets of therapy, and to enhance the cost-effective implementation of therapies for both primary and secondary prevention of CVD. With the publication of the Third Report of the National Cholesterol Education Program’s Adult Treat-ment Panel (ATP-III)1 in 2001 and similar guidelines from other national and international bodies,2–5 risk prediction assumed a central role in the field of CVD prevention. Since then, numerous attempts have been made to refine and improve risk assessment methods. A basic review of the history and principles of risk prediction, their statistical underpinnings, and their clinical implications is thus required to help clinicians and researchers understand the increasingly complex approaches being undertaken to refine CVD risk prediction. Other articles in this series address the current adoption, utility, and effect of risk estimation algorithms in clinical prevention practice. Rationale for CVD Risk Prediction Risk estimates can theoretically be used to raise population awareness of diseases (such as CVD) that cause a significant burden of morbidity and mortality, to communicate knowledge about that risk to individuals and subgroups, and to motivate adherence to recommended lifestyle changes or therapies. In clinical practice, risk prediction algorithms have been used most directly to identify individuals at high risk for developing CVD in the short term to select those individuals for more intensive preventive interventions. The prime example of this latter “high-risk ” prevention strategy, which relies heavily on the quantitative prediction of CVD risk, is the approach promul-gated by the ATP-III panel.1 In this algorithm, the stated underlying assumption is that the intensity of treatment and risk factor reduction should match the level of absolute predicted risk.1,6 Therefore, it is useful to understand the current and potential future roles of quantitative risk prediction in guidelines.
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- Authors
- Lloyd-Jones, D. M.
- Affiliations
- From the Department of Preventive Medicine and Bluhm Cardiovascular Institute, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill.
- Venue
- Circulation
- Published
- 2010-04-20
- Volume
- 121
- Issue
- 15
- Pages
- 1768-1777
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- OpenAlex
- W2051512233
- Cited by
- 705
- References
- 77
- Citations by year
- 2026: 21 · 2025: 26 · 2024: 31 · 2023: 35 · 2022: 46 · 2021: 43 · 2020: 45 · 2019: 31 · 2018: 46 · 2017: 48 · 2016: 54 · 2015: 67 · 2014: 70 · 2013: 45 · 2012: 51
- FWCI
- 6.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Circulation (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Keywords
- cardiovascular risk prediction, cardiovascular risk, risk assessment
- MeSH terms
- Algorithms, Cardiovascular Diseases, Humans, Predictive Value of Tests, Risk Factors, Models, Statistical, Practice Guidelines as Topic, Risk Assessment
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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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