LB-11 | Core-Laboratory Angiographic Characteristics and Mortality of Patients With STEMI and COVID-19: Insights from the NACMI Registry
Bibliographic record
Abstract
The SCAI-sponsored, North American COVID Myocardial Infarction (NACMI) Registry is the largest, prospective dataset of patients presenting with ST-elevation myocardial infarction (STEMI) and COVID-19 worldwide. We present the results of the independent angiographic core laboratory analysis. For this pre-specified independent angiographic analysis, sites were invited to send angiograms to the Cardiovascular Imaging Research Core Lab (Vancouver, BC, Canada). Quantitative coronary angiography percent diameter stenosis (%DS), Thrombolysis In Myocardial Infarction (TIMI) flow, Myocardial Blush Grade (MBG) and Thrombus Grade Burden (TGB) were assessed. Percutaneous coronary intervention (PCI) was classified as unsuccessful if there was residual DS >50% and/or 0 and DS ≥ 50% in ≥ 2 arteries, respectively. Angiograms of 241 patients from 17 sites (12 US, 5 CAN) were analyzed. The Table displays baseline and post PCI angiographic findings. In-hospital mortality was 20%, with a relative risk [95% CI] of 4.42 [1.52, 10.2] for MV thrombotic disease, 2.26 [1.26, 4.01] for TGB of > 2, 2.84 [1.59, 5.19] for unsuccessful PCI, and 1.58 [0.876, 2.96] for MV stenotic disease. Patients with COVID-19 and STEMI have unique and independently-verified angiographic findings, particularly high thrombus burden in multiple vascular territories leading to suboptimal reperfusion with PCI and higher risk of complications. These angiographic findings may contribute to the high mortality rate in these patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".