Ethnic Disparities in ST-Segment Elevation Myocardial Infarction Outcomes and Processes of Care in Patients With and Without Standard Modifiable Cardiovascular Risk Factors: A Nationwide Cohort Study
Bibliographic record
Abstract
Trials suggest patients with ST-elevation myocardial infarction (STEMI) without ‘standard modifiable cardiovascular risk factors’ (SMuRFs) have poorer outcomes, but the role of ethnicity has not been investigated. We analyzed 118,177 STEMI patients using the Myocardial Ischaemia National Audit Project (MINAP) registry. Clinical characteristics and outcomes were analyzed using hierarchical logistic regression models; patients with ≥1 SMuRF (n = 88,055) were compared with ‘SMuRFless’ patients (n = 30,122), with subgroup analysis comparing outcomes of White and Ethnic minority patients. SMuRFless patients had higher incidence of major adverse cardiovascular events (MACE) (odds ratio, OR: 1.09, 95% CI 1.02–1.16) and in-hospital mortality (OR: 1.09, 95% CI 1.01–1.18) after adjusting for demographics, Killip classification, cardiac arrest, and comorbidities. When additionally adjusting for invasive coronary angiography (ICA) and revascularisation (percutaneous coronary intervention (PCI) or coronary artery bypass grafts surgery (CABG)), results for in-hospital mortality were no longer significant (OR 1.05, 95% CI .97–1.13). There were no significant differences in outcomes according to ethnicity. Ethnic minority patients were more likely to undergo revascularisation with ≥1 SMuRF (88 vs 80%, P < .001) or SMuRFless (87 vs 77%, P < .001. Ethnic minority patients were more likely undergo ICA and revascularisation regardless of SMuRF status.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".