Subclinical coronary atherosclerosis and cardiovascular risk stratification in heterozygous familial hypercholesterolemia patients undergoing statin treatment
Bibliographic record
Abstract
PURPOSE OF REVIEW: To discuss the heterogeneity of atherosclerotic cardiovascular disease (ASCVD) risk in heterozygous familial hypercholesterolemia and evidence and limitations of clinical risk scores and subclinical coronary atherosclerosis (SCA) imaging to evaluate risk. RECENT FINDINGS: Risk evaluation in contemporary familial hypercholesterolemia cohorts needs to consider the cause of the familial hypercholesterolemia phenotype, for example the presence of autosomal molecular defects that impart a greater ASCVD risk than in polygenic hypercholesterolemia, prospective follow-up and the impact of statin treatment. As atherosclerosis is multifactorial, clinical scores like the Montreal familial hypercholesterolemia score and SAFEHEART risk equation have been proposed to stratify ASCVD in statin-treated, molecularly defined familial hypercholesterolemia individuals. However, these scores need further validation. SCA distribution in familial hypercholesterolemia individuals undergoing conventional lipid-lowering treatment is heterogeneous, with 45-50% of individuals not presenting any coronary artery calcification (CAC). One study suggests that the absence of CAC associates with no ASCVD events in asymptomatic familial hypercholesterolemia individuals undergoing statin therapy despite elevated residual LDL-cholesterol levels. In contrast, the presence of CAC was independently associated with ASCVD events. SUMMARY: ASCVD risk is heterogeneous in statin-treated familial hypercholesterolemia individuals. Further studies are necessary to determine how risk stratification, especially with SCA detection, impacts on prescription of proprotein convertase subtilisin kexin type 9 inhibitors within a cost-constrained environment.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".