The Value of Carotid Artery Plaque and Intima‐Media Thickness for Incident Cardiovascular Disease: The Multi‐Ethnic Study of Atherosclerosis
Bibliographic record
Abstract
BACKGROUND: Carotid artery plaques are associated with coronary artery atherosclerotic lesions. We evaluated various ultrasound definitions of carotid artery plaque as predictors of future cardiovascular disease (CVD) and coronary heart disease (CHD) events. METHODS AND RESULTS: We studied the risk factors and ultrasound measurements of the carotid arteries at baseline of 6562 members (mean age 61.1 years; 52.6% women) of the Multi-Ethnic Study of Atherosclerosis (MESA). ICA lesions were defined subjectively as >0% or ≥25% diameter narrowing, as continuous intima-media thickness (IMT) measurements (maximum IMT or the mean of the maximum IMT of 6 images) and using a 1.5-mm IMT cut point. Multivariable Cox proportional hazards models were used to estimate hazard ratios for incident CVD, CHD, and stroke. Harrell's C-statistics, Net Reclassification Improvement, and Integrated Discrimination Improvement were used to evaluate the incremental predictive value of plaque metrics. At 7.8-year mean follow-up, all plaque metrics significantly predicted CVD events (n=515) when added to Framingham risk factors. All except 1 metric improved the prediction of CHD (by C-statistic, Net Reclassification Improvement, and Integrated Discrimination Improvement. Mean of the maximum IMT had the highest NRI (7.0%; P=0.0003) with risk ratio of 1.43/mm; 95% CI 1.26-1.63) followed by maximum IMT with an NRI of 6.8% and risk ratio of 1.27 (95% CI 1.18-1.38). CONCLUSION: Ultrasound-derived plaque metrics independently predict cardiovascular events in our cohort and improve risk prediction for CHD events when added to Framingham risk factors.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".