Impact of the COVID-19 pandemic on Canadian emergency medical system management of out-of-hospital cardiac arrest: A retrospective cohort study
Bibliographic record
Abstract
AIM: We sought to describe the impact of the COVID-19 pandemic on the care provided by Canadian emergency medical system (EMS) clinicians to patients suffering out of hospital cardiac arrest (OHCA), and whether any observed changes persisted beyond the initial phase of the pandemic. METHODS: We analysed cases of adult, non-traumatic, OHCA from the Canadian Resuscitation Outcome Consortium (CanROC) registry who were treated between January 27th, 2018, and December 31st, 2021. We used adjusted regression models and interrupted time series analysis to examine the impact of the COVID-19 pandemic (January 27th, 2020 - December 31st, 2021)on the care provided to patients with OHCA by EMS clinicians. RESULTS: There were 12,947 cases of OHCA recorded in the CanROC registry in the pre-COVID-19 period and 17,488 during the COVID-19 period. We observed a reduction in the cumulative number of defibrillations provided by EMS (aRR 0.91, 95% CI 0.89 - 0.93, p < 0.01), a reduction in the odds of attempts at intubation (aOR 0.33, 95% CI 0.31 - 0.34, p < 0.01), higher rates of supraglottic airway use (aOR 1.23, 95% CI 1.16-1.30, p < 0.01), a reduction in vascular access (aOR for intravenous access 0.84, 95% CI 0.79 - 0.89, p < 0.01; aOR for intraosseous access 0.89, 95% CI 0.82 - 0.96, p < 0.01), a reduction in the odds of epinephrine administration (aOR 0.89, 95% CI 0.85 - 0.94, p < 0.01), and higher odds of resuscitation termination on scene (aOR 1.38, 95% CI 1.31 - 1.46, p < 0.01). Delays to initiation of chest compressions (2 min. vs. 3 min., p < 0.01), intubation (16 min. vs. 19 min., p = 0.01), and epinephrine administration (11 min. vs. 13 min., p < 0.01) were observed, whilst supraglottic airways were inserted earlier (11 min. vs. 10 min., p < 0.01). CONCLUSION: The COVID-19 pandemic was associated with substantial changes in EMS management of OHCA. EMS leaders should consider these findings to optimise current OHCA management and prepare for future pandemics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".