Coronary atheroma regression and adverse cardiac events: A systematic review and meta-regression analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: The relationship between plaque regression induced by dyslipidemia therapies and occurrence of major adverse cardiovascular events (MACE) is controversial. We performed a systematic review and meta-regression of dyslipidemia therapy studies reporting MACE and intravascular ultrasound (IVUS) measures of change in coronary atheroma. METHODS: Prospective studies of dyslipidemia therapies reporting percent atheroma volume (PAV) measured by IVUS and reporting death, myocardial infarction, stroke, unstable angina or transient ischemic attack (MACE) were included. The association between mean change in PAV and MACE was examined using meta-regression via mixed-effects binomial logistic regression models, unadjusted and adjusted for mean age, baseline PAV, baseline low density lipoprotein-cholesterol and study duration. RESULTS: The study included 17 prospective studies published between 2001 and 2018 totaling 6333 patients. Study duration varied from 11 to 104 weeks. Mean change in PAV, across the study arms, ranged from -5.6% to 3.1%. MACE ranged from 0 to 72 events per study arm: 13 study arms (38%) reported no events, 8 (24%) reported 1-2 events and 13 (38%) reported 3 or more events. Meta-regression demonstrated a decline in the odds of MACE associated with reduction in mean PAV: unadjusted odds ratio (OR): 0.78, 95% Confidence Interval (CI): [0.63, 0.96], p = 0.018; adjusted OR: 0.82, 95% CI: [0.70, 0.95], p = 0.011, per 1% decrease in mean PAV. CONCLUSIONS: A 1% reduction in mean PAV as induced by dyslipidemia therapies was associated with a 20% reduction in the odds of MACE.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".