Impact of QRS Duration on Non–ST-Segment Elevation Myocardial Infarction (from a National Registry)
Bibliographic record
Abstract
QRS duration (QRSd) is ill-defined and under-researched as a prognosticator in patients with non-ST-segment myocardial infarction (NSTEMI). We analyzed 240,866 adult (≥18 years) hospitalizations with non-ST-segment elevation myocardial infarction using data from the United Kingdom Myocardial Infarction National Audit Project. Clinical characteristics and all-cause in-hospital mortality were analyzed according to QRSd, with 38,023 patients presenting with a QRSd >120 ms and 202,842 patients with a QRSd <120 ms. Patients with a QRSd >120 ms were more frequently older (median age of 79 years vs 71 years, p <0.001), and of white ethnicity (93% vs 91%, p <0.001). Patients with a QRSd <120 ms had higher frequency of use of aspirin (97% vs 95%, p <0.001), P2Y12 inhibitor (93% vs 89%, p <0.001), angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (82% vs 81%, p <0.001) and β blockers (83% vs 78%, p <0.001). Invasive management strategies were more likely to be used in patients with QRSd <120 ms including invasive coronary angiography (72% vs 54%, p <0.001), percutaneous coronary intervention (46% vs 33%, p <0.001) and coronary artery bypass graft surgery (8% vs 6%, p <0.001). In a propensity score matching analysis, there were no differences between the 2 groups in the adjusted rates of in-hospital all-cause mortality (odds ratio 0.94, 95% confidence interval 0.86 to 1.01) or major adverse cardiac events (odds ratio 0.94, 95% confidence interval 0.85 to 1.02) during the index admission. In conclusion, prolonged QRSd >120 ms in the context of non-ST-segment myocardial infarction is not associated with worse in-hospital mortality or the outcomes of major adverse cardiac events.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".