Atherosclerotic cardiovascular disease risk assessment
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ASCVD Risk Assessment: Global Risk Scores and Clinical Tools
Atherosclerotic cardiovascular disease (ASCVD) risk assessment is foundational in preventive cardiology. The process typically begins with global risk scores, such as the Pooled Cohort Equations (PCE), which use standard clinical measures to estimate a person's 10-year risk of ASCVD events. These tools help categorize individuals into low, borderline, intermediate, or high-risk groups, guiding decisions about lifestyle changes and pharmacologic therapy. The PCE is widely validated and recommended in US guidelines for primary prevention, but it may underestimate or overestimate risk in certain populations, such as those with different racial/ethnic backgrounds, socioeconomic status, or chronic inflammatory diseases. In cases of uncertainty, additional testing like coronary artery calcium (CAC) scoring can further refine risk estimates and inform treatment decisions 13458.
Traditional and Nontraditional Risk Factors in ASCVD Risk Assessment
Traditional risk factors—such as age, blood pressure, cholesterol levels, smoking, and diabetes—remain central to ASCVD risk assessment. Age is a particularly strong driver of 10-year ASCVD risk for both men and women. The inclusion of stroke as an endpoint and race-specific coefficients in risk calculators allows for better identification of at-risk individuals, especially among African Americans and women 345.
Nontraditional risk markers, including CAC score, ankle-brachial index (ABI), high-sensitivity C-reactive protein (hsCRP), and family history, can provide additional predictive value. Among these, CAC scoring has shown the most significant improvement in risk discrimination when added to traditional risk models, while ABI and family history are also independent predictors. However, hsCRP adds little to no improvement in risk prediction when combined with traditional models .
Lipid Markers: LDL-Cholesterol, Non-HDL-Cholesterol, and Apolipoprotein B
LDL-cholesterol is a well-established risk factor and the primary therapeutic target in ASCVD prevention. However, non-HDL-cholesterol and apolipoprotein B (apoB) are increasingly recognized as more accurate markers in certain populations, such as those with high triglycerides, diabetes, or very low LDL-cholesterol. Non-HDL-cholesterol includes all atherogenic lipoproteins and is less affected by triglyceride variability, while apoB directly measures the number of atherogenic particles. Both non-HDL-cholesterol and apoB are considered superior to LDL-cholesterol in predicting ASCVD risk in specific subgroups and are recommended as secondary targets in some guidelines 210.
HDL-Cholesterol and the "U"-Shaped Risk Curve
HDL-cholesterol has a complex relationship with ASCVD risk. Both low and very high HDL-cholesterol levels are associated with increased risk of ASCVD and all-cause mortality, forming a "U"-shaped curve. This relationship is independent of traditional risk factors, but the causal role of HDL-cholesterol remains uncertain. In risk assessment, HDL-cholesterol is considered a risk enhancer, especially when levels are very low or very high .
Risk Enhancers and Special Populations
Risk assessment should also consider additional "risk enhancers," such as novel biomarkers, inflammatory factors, female-specific risk factors, social determinants of health, and high-risk ethnic groups. These factors are especially important for individuals at borderline or intermediate risk, where they can influence the decision to initiate preventive therapies 145.
Machine Learning and Feature Selection in ASCVD Risk Prediction
Recent advances include the use of machine learning algorithms and feature selection techniques to improve ASCVD risk prediction. For example, models using L1-regularized mutual information feature selection combined with advanced classifiers have demonstrated high accuracy in identifying individuals at risk, suggesting a potential role for artificial intelligence in future risk assessment strategies .
Conclusion
Accurate ASCVD risk assessment is essential for targeting preventive therapies and reducing cardiovascular events. The process relies on validated risk scores, consideration of both traditional and nontraditional risk factors, and may be enhanced by advanced lipid markers and machine learning approaches. Clinician-patient discussions remain central, ensuring that risk assessment leads to personalized and effective prevention strategies 13458.
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