Racial Variations in Treatment and Outcomes of Black and White Patients With High-Risk Non–ST-Elevation Acute Coronary Syndromes
Bibliographic record
Abstract
BACKGROUND: Black patients with acute myocardial infarction are less likely than whites to receive coronary interventions. It is unknown whether racial disparities exist for other treatments for non-ST-segment elevation acute coronary syndromes (NSTE ACS) and how different treatments affect outcomes. METHODS AND RESULTS: Using data from 400 US hospitals participating in the CRUSADE (Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes with Early Implementation of the ACC/AHA Guidelines?) National Quality Improvement Initiative, we identified black and white patients with high-risk NSTE ACS (positive cardiac markers and/or ischemic ST-segment changes). After adjustment for demographics and medical comorbidity, we compared the use of therapies recommended by the American College of Cardiology/American Heart Association guidelines for NSTE ACS and outcomes by race. Our study included 37,813 (87.3%) white and 5504 (12.7%) black patients. Black patients were younger; were more likely to have hypertension, diabetes, heart failure, and renal insufficiency; and were less likely to have insurance coverage or primary cardiology care. Black patients had a similar or higher likelihood than whites of receiving older ACS treatments such as aspirin, beta-blockers, or ACE inhibitors but were significantly less likely to receive newer ACS therapies, including acute glycoprotein IIb/IIIa inhibitors, acute and discharge clopidogrel, and statin therapy at discharge. Blacks were also less likely to receive cardiac catheterization, revascularization procedures, or smoking cessation counseling. Acute risk-adjusted outcomes were similar between black and white patients. CONCLUSIONS: Black patients with NSTE ACS were less likely than whites to receive many evidence-based treatments, particularly those that are costly or newer. Longitudinal studies are needed to assess the long-term impact of these treatment disparities on clinical outcomes.
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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 | 0.001 | 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".