Approach to risk stratification of atherosclerotic cardiovascular disease
Bibliographic record
Abstract
OBJECTIVE: To outline the 2021 Canadian Cardiovascular Society (CCS) dyslipidemia guidelines and to present the current approaches to cardiovascular risk stratification, including the incorporation of biomarkers and imaging tests. SOURCES OF INFORMATION: Current guidelines were reviewed and an Ovid MEDLINE literature search was performed. MAIN MESSAGE: Cardiovascular disease (CVD) is the leading cause of global mortality, with ischemic heart disease contributing to nearly half of these deaths. Risk stratification is undertaken to identify patients who would benefit from primary prevention for atherosclerotic CVD (ASCVD), but commonly used methods for risk stratification are imperfect. The CCS guidelines endorse that the presence of risk modifiers (family history of premature ASCVD, high-sensitivity C-reactive protein level ≥2.0 mg/L, lipoprotein[a] level ≥500 mg/L [≥50 mg/dL], or coronary artery calcium >0) supports the use of statin therapy in those at intermediate risk (Framingham risk score 10% to 19.9%) who do not otherwise meet the recommendations for statin use. The CCS guidelines recommend statin therapy in patients at intermediate risk when cholesterol levels are elevated (low-density lipoprotein cholesterol level ≥3.5 mmol/L, non-high-density lipoprotein cholesterol level ≥4.2 mmol/L, or apolipoprotein B level ≥1.05 g/L). In addition, statin therapy should be considered for patients at low risk (Framingham risk score 5% to 9.9%) with elevated cholesterol levels, especially if risk modifiers are present. When cholesterol levels are not elevated, evidence still favours the use of statins in intermediate-risk patients when risk modifiers are present and in men 50 years and older and women 60 years and older with 1 additional risk factor. CONCLUSION: Biomarkers and imaging tests have the potential to improve ASCVD risk stratification by reclassifying any patient whose risk has been inaccurately estimated by traditional methods. Recently published guidelines by the CCS suggest the use of biomarkers and imaging in certain patient groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".