Addressing disparities of care in non-ST-segment elevation myocardial infarction patients without standard modifiable risk factors: insights from a nationwide cohort study
Bibliographic record
Abstract
AIMS: The importance of standard modifiable cardiovascular risk factors (SMuRFs) in preventing non-ST-segment elevation myocardial infarction (NSTEMI) is established. However, NSTEMI may present in the absence of SMuRFs, and little is known about their outcomes. METHODS AND RESULTS: We analysed 176 083 adult (≥18 years) hospitalizations with NSTEMI using data from the United Kingdom (UK) Myocardial Infarction National Audit Project (MINAP). Clinical characteristics and all-cause in-hospital mortality were analysed according to SMuRF status, with 135 223 patients presenting with at least one of diabetes, hypertension, hypercholesterolaemia, or current smoking status and 40 860 patients without any SMuRFs. Those with a history of coronary artery disease were excluded. Patients without SMuRFs were more frequently older (median age 72 year vs. 71 years, P < 0.001), male (62% vs. 61%, P < 0.001), and Caucasian (95% vs. 92%, P < 0.001). Those without SMuRFs less frequently received statins (71% vs. 81%, P < 0.001), had their left ventricular (LV) function recorded (62% vs. 65%, P < 0.001) or for those with moderate or severe LV systolic dysfunction were prescribed angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (80% vs. 85%, P < 0.001). Following propensity score matching the odds of all-cause mortality [odds ratio (OR): 0.85, 95% confidence interval (CI): 0.77-0.93], cardiac mortality (OR: 0.85, 95% CI: 0.76-0.94), and major adverse cardiovascular events (MACE) (OR: 0.85, 95% CI: 0.77-0.93) were lower in patients without SMuRFs. CONCLUSION: More than one in five patients presenting with NSTEMI had no SMuRFs, who were less frequently received guideline-recommended management and had lower in-hospital (all-cause and cardiac) mortality and MACE than patients with SMuRFs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".