Flattening the other curve: Reducing emergency department STEMI delays during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has been associated with ST-Elevation Myocardial Infarction (STEMI) reperfusion delays despite reduced emergency department (ED) volumes. However, little is known about ED contributions to these delays. We sought to measure STEMI delays and ED quality benchmarks over the course of the first two waves of the pandemic. STUDY: This study was a multi-centre, retrospective chart review from two urban, academic medical centres. We obtained ED volumes, COVID-19 tests and COVID-19 cases from the hospital databases and ED Code STEMIs with culprit lesions from the cath lab. We measured door-to-ECG (DTE) time and ECG-to-Activation (ETA) time during the phases of the pandemic in our jurisdiction: pre-first wave (Jan-Mar 2020), first wave (Apr-June 2020), post-first wave (July-Nov 2020), and second wave (Dec 2020 to Feb 2021). We calculated median DTE and ETA times and compared them to the 2019 baseline using Wilcox rank-sum test. We calculated the percentages of DTE ≤10 min and of ETA ≤10 min and compared them to baseline using chi-square test. We also utilized Statistical Process Control (SPC) Xbar-R charts to assess for special cause variation. RESULTS: COVID-19 cases began during the pre-wave phase, but there was no change in ED volumes or STEMI quality metrics. During the first wave ED volumes fell by 40%, DTE tripled (10.0 to 29.5 min, p = 0.016), ETA doubled (8.5 to 17.0 min, p = 0.04), and percentages for both DTE ≤10 min and ETA ≤10 min fell by three-quarters (each from more than 50%, to both 12.5%, both p < 0.05). After the first wave all STEMI quality benchmarks returned to baseline and did not significantly change during the second wave. A brief period of special cause variation was noted for DTE during the first wave. CONCLUSIONS: Both DTE and ETA metrics worsened during the first wave of the pandemic, revealing how it negatively impacted the triage and diagnosis of STEMI patients. But these normalized after the first wave and were unaffected by the second wave, indicating that nurses and physicians adapted to the pandemic to maintain STEMI quality of care. DTE and ETA metrics can help EDs identify delays to reperfusion during the pandemic and beyond.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".