Sex Differences in Clinical Characteristics, Management Strategies, and Outcomes of STEMI With COVID-19: NACMI Registry
Bibliographic record
Abstract
Background Women with ST-segment elevation myocardial infarction (STEMI) had worse outcomes than men prior to the COVID-19 pandemic. Although concomitant COVID-19 infection increases mortality risk in STEMI patients, no studies have evaluated sex differences in this context. Methods The North American COVID-19 STEMI registry is a prospective, multicenter registry of hospitalized STEMI patients with COVID-19 infection. We compared sex differences in clinical characteristics, presentation, management strategies, and in-hospital mortality. Results Among 585 patients with STEMI and COVID-19 infection, 154 (26.3%) were women. Compared to men, women were significantly older, had a higher prevalence of diabetes and stroke/transient ischemic attack, and were more likely to be on statins on presentation. Men more frequently presented with chest pain, whereas women presented with dyspnea. Women more often had STEMI without an identified culprit lesion than men (33% vs 18%, P < .001). The use of percutaneous coronary intervention was significantly higher in men, whereas medical therapy was higher in women. In-hospital mortality was 33% for women and 27% for men ( P = .22). Conclusions In patients presenting with STEMI in the context of COVID-19, the in-hospital mortality rate was 30% and similar for men and women. Lack of an identifiable culprit lesion was common in the setting of COVID-19 for both sexes but more likely in women (1/3 of women vs 1/5 of men). Evaluation of specific underlying etiologies is underway to better define the full impact of COVID-19 on STEMI outcomes and better understand the observed sex differences.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".