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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
Concept and usefulness of cardiovascular risk profiles
William B Kannel1, Ralph B D'Agostino, Lisa Sullivan
1National Heart, Lung, and Blood Institute's Framingham Study, National Institutes of Health, Boston University School of Medicine, Framingham, Mass 01702, USA. billkannel@yahoo.com
Insights
Multivariable risk factor analysis is crucial for identifying individuals at high risk of atherosclerotic cardiovascular disease (CVD). Addressing multiple risk factors can prevent various cardiovascular conditions.
Area of Science:
- Cardiovascular Medicine
- Public Health
- Epidemiology
Background:
- Atherosclerotic cardiovascular disease (CVD) remains a significant public health challenge despite diagnostic and therapeutic advancements.
- Multivariable risk factor analysis is vital for identifying high-risk individuals and understanding disease pathogenesis.
- Traditional risk factor assessment is limited by practical constraints and specific research hypotheses.
Observation:
- The Framingham Study demonstrated that evaluating CVD risk is most effective using a combination of established risk factors.
- Risk factors rarely act in isolation; their impact varies significantly with the presence of other factors.
- Approximately half of all CVD cases in the general population stem from individuals with multiple, borderline risk factor abnormalities.
Findings:
- A multivariable risk formulation for coronary disease, including age, sex, cholesterol ratio, blood pressure, glucose intolerance, smoking, and left ventricular hypertrophy, also predicts peripheral artery disease, heart failure, and stroke.
- Shared risk factors contribute to the predictive power of this formulation across different cardiovascular conditions.
- Correcting one CVD risk factor can offer protection against multiple other cardiovascular diseases.
Implications:
- Multivariable risk stratification is essential for efficiently identifying individuals likely to develop CVD and quantifying their risk.
- This approach aids in targeted preventive strategies and resource allocation for cardiovascular health.
- Understanding shared risk factors highlights the interconnectedness of cardiovascular diseases and the potential for broad protective effects through risk factor modification.
Abstract:
Despite major advances in the diagnosis and treatment of atherosclerotic cardiovascular disease (CVD) in the past century, it remains a serious clinical and public health problem. Multivariable risk factor analysis is now commonly performed to identify high-risk candidates for CVD who need preventive measures and to seek out clues to the pathogenesis of the disease. The set of risk factors used for the former is constrained by practical considerations, and the set of risk factors used for the latter is constrained by the hypothesis being tested. This report reviews the evolution and usefulness of multivariable risk functions crafted for estimating risk of clinical manifestations of atherosclerosis and for gaining insights into their pathogenesis. Decades of evaluation of CVD risk factors by the Framingham Study led to the conclusion that CVD risk evaluation is most fruitfully appraised from the multivariable risk posed by a set of established risk factors. Such assessment is essential because risk factors seldom occur in isolation, and the risk associated with each varies widely depending on the burden of associated risk factors. About half the CVD in the general population arises from the segment with multiple marginal risk factor abnormalities. Although disease-specific profiles are available, a multivariable risk formulation for coronary disease comprised of age, sex, the total/high-density lipoprotein cholesterol ratio, systolic blood pressure, glucose intolerance, cigarette smoking, and electrocardiography-left ventricular hypertrophy is also predictive of peripheral artery disease, heart failure, and stroke because of shared risk factors. Correcting risk factors for any particular CVD has the potential to protect against > or =1 of the others. Multivariable risk stratification is now recognized as essential in efficiently identifying likely candidates for CVD and quantifying the hazard.
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