Racial Disparities in Management and Outcomes of Out-of-Hospital Cardiac Arrest Complicating Myocardial Infarction: A National Study From England and Wales
Bibliographic record
Abstract
BackgroundStudies of racial disparities in care of patients admitted with an out-of-hospital cardiac arrest (OHCA) in the setting of acute myocardial infarction (AMI) have shown inconsistent results. Whether these differences in care exist in the universal healthcare system in United Kingdom is unknown.MethodsPatients admitted with a diagnosis of AMI and OHCA between 2010 and 2017 from the Myocardial Ischaemia National Audit Project (MINAP) were studied. All patients were stratified based on ethnicity into a Black, Asian, or minority ethnicity (BAME) group vs a White group. We used multivariable logistic regression models to evaluate the predictors of clinical outcomes and treatment strategy.ResultsFrom 14,287 patients admitted with AMI complicated by OHCA, BAME patients constituted a minority of patients (1185 [8.3%]), compared with a White group (13,102 [91.7%]). BAME patients were younger (median age [interquartile range]) for BAME group, 58 [50-70] years; for White group, 65 [55-74] years). Cardiogenic shock (BAME group, 33%; White group, 20.7%; P < 0.001) and severe left ventricular impairment (BAME group, 21%; White group, 16.5%; P < 0.003) were more frequent among BAME patients. BAME patients were more likely to be seen by a cardiologist (BAME group, 95.9%; White group, 92.5%; P < 0.001) and were more likely to receive coronary angiography than the White group (odds ratio [OR] 1.5, 95% confidence interval [CI] 1.2-1.88). The BAME group had significantly higher in-hospital mortality (OR 1.26, 95% CI 1.04-1.52) and re-infarction (OR 1.52, 95% CI 1.06-2.18) than the White group.ConclusionsBAME patients were more likely to be seen by a cardiologist and receive coronary angiography than White patients. Despite this difference, the in-hospital mortality of BAME patients, particularly in the Asian population, was significantly higher.
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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".