Impact of Patient- and System-Level Delays on Reperfusion Among Patients With ST-Elevation Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Patients with ST-elevation myocardial infarction (STEMI) presenting to percutaneous coronary intervention (PCI)-capable hospitals often experience delays for primary PCI (pPCI). We sought to describe the effect of specific delay intervals and patient/system-level factors on STEMI reperfusion times. METHODS: We analyzed all consecutive patients with STEMI who presented to 2 PCI-capable hospital emergency departments (EDs) between June 2007 and March 2016 who received successful pPCI. We excluded patients with prehospital cardiac arrest. We compared specific system delay intervals, patient characteristics, and in-hospital outcomes among patients who received timely (first medical contact-device ≤90/≤120 minutes) vs delayed >90/>120 minutes) pPCI. RESULTS: Of 1936 patients with STEMI, 1127 (58%) presented directly to a PCI-capable hospital via emergency health services (EHS), 499 (26%) were transferred from the ED of a non-PCI hospital, and 310 (16%) self-presented to the ED of a pPCI-capable hospital. Guideline-recommended reperfusion times were met in 47% of direct-EHS, 42% of transfers, and 33% of self-presenters. Each time interval from first medical contact to device deployment was significantly prolonged in the delayed vs timely reperfusion cohorts across all 3 groups, excepting vascular access time. ED dwell time contributed the most to the difference in median reperfusion time within each group. Time of presentation, comorbidities, and sex were each significantly associated with delayed reperfusion. Within the EHS-direct group, prolonged reperfusion and ED dwell times were significantly associated with increased mortality, major bleeding, and cardiogenic shock. CONCLUSION: Ongoing efforts to identify and reduce ED dwell time and other systemic pPCI delays may improve STEMI outcomes, including mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".