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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
Cardiovascular risk in patients without known cardiovascular disease
R G Carbone1, M F Algahim, S Rizzo
1Respiratory Unit, Department of Internal Medicine, Regional Hospital, Aosta, Italy, and DIMI, University of Genoa, Genoa, Italy. patriarca125@tiscali.it
Insights
Identifying atherosclerotic cardiovascular disease (CVD) risks aids patient education. This review analyzes CVD risk models, recommending prediction of myocardial infarction, cardiovascular death, and cerebrovascular events for better management.
Area of Science:
- Cardiology
- Public Health
- Preventive Medicine
Background:
- Atherosclerotic cardiovascular disease (CVD) poses significant health risks.
- Effective patient education and management rely on accurate risk assessment.
- Existing risk models offer insights but require careful selection.
Purpose of the Study:
- To review and analyze various risk models for atherosclerotic cardiovascular disease (CVD).
- To guide the selection of appropriate risk prediction tools for clinical practice.
- To emphasize the importance of risk stratification in managing CVD.
Main Methods:
- Literature review of validated CVD risk models.
- Analysis of models predicting myocardial infarction, cardiovascular death, and cerebrovascular events.
- Stratification of CVD risks based on model outputs.
Main Results:
- Multiple risk models are available for assessing atherosclerotic cardiovascular disease (CVD).
- Models predicting myocardial infarction, cardiovascular death, and/or cerebrovascular events are recommended.
- Risk stratification aids in identifying patients requiring intervention.
Conclusions:
- Accurate CVD risk assessment is crucial for patient management and education.
- Utilizing validated risk models improves physician recognition of at-risk individuals.
- Targeted interventions, including ATP III treatment and blood pressure control, are essential for mitigating CVD risks.
Abstract:
Understanding the risks of atherosclerotic cardiovascular disease (CVD) allows for better patient education and management. Multiple risk models have been validated in large patient populations and provide insights into the risks associated with CVD. When assessing such risks, we suggest using a model that predicts myocardial infarction, cardiovascular death, and/or cerebrovascular events. In this review, we analyze several risk models and stratify the risks associated with CVD. We suggest that appropriate profiling of patients at-risk of CVD will lead to better physician recognition and treatment of modifiable risk factors, appropriate application of ATP III treatment for hyperlipidemia, and achieving optimal blood pressure control.
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