Extracorporeal membrane oxygenation for COVID-19-associated severe acute respiratory distress syndrome in Canada: Analysis of data from the Canadian Nosocomial Infection Surveillance Program
Bibliographic record
Abstract
Background: Extracorporeal membrane oxygenation (ECMO) for COVID-19 across Canada has not been well-described. We studied trends for patients with COVID-19-related acute respiratory distress syndrome who received ECMO. Methods: Multicentre retrospective cohort study using data from the Canadian Nosocomial Infection Surveillance Program across four different waves. Surveillance data was collected between March 2020 and June 2022. We reported data stratified by ECMO status and wave. Results: ECMO recipients comprised 299 (6.8%) of the 4,408 critically ill patients included. ECMO recipients were younger (median age 49 versus 62 years, p < 0.001), less likely to be vaccinated against COVID-19 (Wave 4 data: 5.3% versus 19%; p = 0.002), and had fewer comorbidities compared to patients who did not receive ECMO. Thirty-day all-cause mortality was similar between the ECMO and non-ECMO groups (23% versus 26%; p = 0.25). Among ECMO recipients, mortality tended to decrease across Waves 1 to 4: 48%, 31%, 18%, and 16%, respectively ( p = 0.04 for trend). However, this was no longer statistically significant when removing the high mortality during Wave 1 ( p = 0.15). Conclusions: Our findings suggest that critically ill patients in Canadian hospitals who received ECMO had different characteristics from those who did not receive ECMO. We also observed a trend of decreased mortality over the waves for the ECMO group. Possible explanatory factors may include potential delay in ECMO initiation during Wave 1, evolution of the virus, better understanding of COVID-19 disease and ECMO use, and new medical treatments and vaccines available in later waves. These findings may provide insight for future potential pandemics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".