ECMO During COVID-19: A Society of Thoracic Surgeons/Extracorporeal Life Support Organization Survey
Bibliographic record
Abstract
Background The Society of Thoracic Surgeons Workforce on Critical Care and the Extracorporeal Life Support Organization sought to identify how the coronavirus disease 2019 (COVID-19) pandemic has changed the practice of venoarterial (VA) and venovenous (VV) extracorporeal membrane oxygenation (ECMO) programs across North America. Methods A 26-question survey covering 6 categories (ECMO initiation, cannulation, management, anticoagulation, triage/protocols, and credentialing) was emailed to 276 North American Extracorporeal Life Support Organization centers. ECMO practices before and during the COVID-19 pandemic were compared. Results Responses were received from 93 (34%) programs. The percentage of high-volume (>20 cases per year) VV ECMO programs increased during the pandemic from 29% to 41% ( P < .001), as did institutions requiring multiple clinicians for determining initiation of ECMO (VV ECMO, 25% to 43% [ P = .001]; VA ECMO, 20% to 32% [ P = .012]). During the pandemic, more institutions developed their own protocols for resource allocation (23% before to 51%; P < .001), and more programs created sharing arrangements to triage patients and equipment with other centers (31% to 57%; P < .001). Direct thrombin inhibitor use increased for both VA ECMO (13% to 18%; P = .025) and VV ECMO (12% to 24%; P = .005). Although cardiothoracic surgeons remained the primary cannulating proceduralists, VV ECMO cannulations performed by pulmonary and critical care physicians increased (13% to 17%; P = .046). Conclusions The Society of Thoracic Surgeons/Extracorporeal Life Support Organization collaborative survey indicated that the pandemic has affected ECMO practice. Further research on these ECMO strategies and lessons learned during the COVID-19 pandemic may be useful in future global situations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".