Abstract 12740: Racial and Ethnic Disparities in Mortality in Patients Presenting With STEMI With COVID-19: NACMI Registry
Bibliographic record
Abstract
Introduction: COVID-19 infection disproportionally impacts non-Whites with higher morbidity and mortality. However, differences by race and ethnicity in COVID-19 patients with concomitant STEMI have not been previously described. Methods: The North American COVID-19 STEMI (NACMI) registry is a prospective, observational registry enrolling COVID-19 patients with concomitant STEMI from 64 centers in Canada and the United States from March 2020 to December 2021. We compared clinical characteristics, treatment strategies, and in-hospital mortality risks by race/ethnicity. Results: Among 679 STEMI patients with concomitant COVID-19, 54.5% were White, 14.3% Black, 19.4% Hispanic/Latinx, and 11.8% Asian/Indigenous/Other. Blacks had the highest prevalence of current smokers, and Whites had the lowest prevalence of diabetes. The rate of high-risk features including cardiac arrest, cardiogenic shock, and inotropic support was comparable between the groups. Presence of infiltrates and cardiomegaly was higher in Blacks and Hispanic/Latinx; whereas, COVID-19 severity was similar in the groups. Blacks and Hispanic/Latinx and Blacks were more likely to not have coronary angiography performed. Among patients that underwent angiography, Whites and Hispanic/Latinx were more likely to be treated with primary PCI. In-hospital mortality was highest in Hispanic/Latinx (38%) and the Asian/Indigenous/Other group (Table). Conclusions: Despite no difference in high-risk features in patients with COVID-19 and STEMI, there was a significant difference in in-hospital mortality between Whites and non-Whites with highest risk in Hispanic/Latinx and Asian/Indigenous/Other. Further research in needed to explore discrepant outcomes in racial/ethnic minorities in this patient population and the role of discrepant management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".