8 Is LP(a) a predictor of coronary microvascular dysfunction? A retrospective analysis of the COMET-19 study
Bibliographic record
Abstract
Background Lipoprotein-a [Lp(a)] is an established marker of epicardial coronary disease, yet its association with coronary microvascular dysfunction (CMD) is largely unexplored. COMET-19 enrolled 117 patients who developed angina following severe COVID-19 but had no obstructive coronary artery disease (ANOCA). Each participant underwent invasive coronary physiology testing—fractional flow reserve (FFR), coronary flow reserve (CFR) and index of microvascular resistance (IMR)—to identify CMD (CFR<2.0 or IMR≥25), and functional CMD (CFR<2.0 with IMR<25). Methods We retrospectively analysed the COMET-19 cohort comparing LP(a) to CMD and endothelial cell dysfunction (ECD) guideline cut-off values of the American Heart Association (AHA/ACC) [≥125 nmol/L], Canadian Cardiovascular Society (CCS) [≥100 nmol/L], European Atherosclerotic Society (EAS) intermediate level [50–125 nmol/L] or higher level [>125 nmol/L] and the National Lipid Association (NLA) [>100 nmol/L]). Pearson Chi-Square, Spearman Correlation and Odds Ratios were calculated using SPSS v29. Results The mean(SD) age was 65.95(11.4). The median(min-max) Lp(a) was 194 nmol/L (45–499). We could not demonstrate a relationship between elevated Lp(a) and the presence of CMD (CFR or IMR values) (p>0.05). Elevated Lp(a) values did not increase the risk of CMD or ECD across ACC/AHA, CCS, EAS or NLA thresholds (all OR 0.2–1.5: 95% CI crossed unity). There was no correlation between Lp(a) and CMD diagnostic criteria (CFR: ρ= -0.05, p=0.63), (IMR: ρ=0.04, p=0.69) (figure 1). Conclusion A raised serum LP(a) was not associated with increased risk of, or incidence of CMD independent of the LP(a) cut off value criterion employed. LP(a) was not a helpful test to risk-stratify patients for CMD, in this cohort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".