Review on cardiovascular risk prediction
Abstract
Summary: The objectives were to review the currently available and widely used cardiovascular risk assessment models and to examine the evidence available on new biomarkers and the nonclinical measures in improving the risk prediction in the population level. Identification of individuals at risk of cardiovascular disease (CVD), to better target prevention and treatment, has become a top research priority. Cardiovascular risk prediction has progressed with the development and refinement of risk prediction models based upon established clinical factors, and the discovery of novel biomarkers, lifestyle, and social factors may offer additional information on the risk of disease. However, a significant proportion of individuals who have a myocardial infarction still are categorized as low risk by many of the available methods. Although novel biomarkers can improve risk prediction, including B‐type natriuretic peptides which have shown the best predictive capacity per unit cost, there is concern that the use of risk prediction strategies which rely upon new/or expensive biomarkers could further broaden social inequalities in CVD. In contrast, nonclinical factors such as work stress, social isolation, and early childhood experience also appear to be associated with cardiovascular risk and have the potential to be utilized for the baseline risk stratification at the population level. A stepwise approach of nonclinical methods followed by risk scores consisting of clinical risk factors may offer a better option for initial and subsequent screening, preserving more specialized approaches including novel biomarkers for enhanced risk stratification at population level in a cost‐effective manner.
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Non-systematic reviews have identified similar findings with respect to increased risk of mortality, cardiovascular disease and outcomes, and mental health, with less evidence for other physical health conditions and behaviours.
“Non-systematic reviews have identified similar findings with respect to increased risk of mortality 59-64 , cardiovascular disease and outcomes 62, 65-72 , mental health 61, 62, 64, 73-90 , with less evidence for other physical health conditions and behaviours 72, 76, 80, 91-93 .”
From An overview of systematic reviews on the public health consequences of social isolation and loneliness (2017) · cites this paper as [68]
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Leigh-Hunt, N., Bagguley, D., Bash, K., Turner, V., Turnbull, S., Valtorta, N., & Caan, W. (2017) · Public Health · doi:10.1016/j.puhe.2017.07.035 · Cited by 2,158 · see citing works
- Authors
- Ruwanpathirana, T., Owen, A., & Reid, C. M.
- Affiliations
- Monash University (AU)
- Corresponding author
- Reid, Christopher M.
- Venue
- Cardiovascular Therapeutics
- Publisher
- Wiley
- Published
- 2015-03-26
- Volume
- 33
- Issue
- 2
- Pages
- 62-70
- ISSN
- 1755-5914 (ISSN-L)
- 1755-5922
- OpenAlex
- W2023878276
- Cited by
- 31
- References
- 110
- Citations by year
- 2024: 2 · 2022: 7 · 2021: 3 · 2020: 4 · 2019: 4 · 2018: 2 · 2017: 5 · 2016: 3 · 2015: 1
- FWCI
- 0.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cardiovascular Therapeutics (publishedVersion)
- PubMed (publishedVersion)
- eSpace (Curtin University) (submittedVersion)
- Funders
- National Health and Medical Research Council (546272)
- 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 › Surgery › Lipoproteins and Cardiovascular Health
- Keywords
- cardiovascular risk prediction, cardiovascular disease, risk biomarkers, brain natriuretic peptide, occupational stress, social isolation, early childhood experiences, risk stratification, cardiovascular screening, cost-effectiveness
- MeSH terms
- Age Factors, Cardiovascular Diseases, Decision Support Techniques, Humans, Predictive Value of Tests, Preventive Health Services, Prognosis, Recurrence, Risk Factors, Sex Factors, Socioeconomic Factors, Time Factors, Biomarkers, Risk Assessment, Genetic Predisposition to Disease
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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