Delays in ST-Elevation Myocardial Infarction Care During the COVID-19 Lockdown: An Observational Study
Bibliographic record
Abstract
BACKGROUND: Management of ST-elevated myocardial infarction (STEMI) necessitates rapid reperfusion. Delays prolong myocardial ischemia and increase the risk of complications, including death. The COVID-19 pandemic may have impacted STEMI management. We evaluated the relative volume of hospitalizations and clinical time intervals within a regional STEMI system. METHODS: 494 patients with STEMI were grouped into pre-lockdown, lockdown and re-opening cohorts. Clinical, temporal and outcome data were collected and compared between groups for both urban and rural patients, receiving primary percutaneous coronary intervention (PCI) and pharmacoinvasive revascularization, respectively. Data was compared to a 10-year historical comparator. RESULTS: During pre-lockdown there was 238 cases versus 193 in lockdown; a 19.0% reduction in volume. When lockdown was compared to the median caseload from a 10-year historical cohort, a 19.8% reduction was observed. For patients treated with primary PCI during lockdown, median symptom-to-balloon time increased by 44-minutes [217 (IQR 157-387) vs. 261 (160-659) minutes; p=0.03]; driven by an increase in median symptom-to-door time of 41-minutes [136 (IQR 80-267) vs. 177 (IQR 90-569) minutes; p<0.01]. Only patients transferred from non-PCI facilities demonstrated an increase in door-to-reperfusion time [116 (IQR 93-150) vs. 139 (IQR 100-199) minutes; p<0.01]. More patients had left ventricular dysfunction during the lockdown [35% vs. 44%; p=0.04], but there was no difference in mortality. CONCLUSION: During the COVID-19 lockdown, fewer patients presented with STEMI. Time-to-reperfusion was significantly prolonged and appeared driven predominantly by patient-level and transfer delays. Public education and systems-level changes will be integral to STEMI care during the second wave of COVID-19.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".