Tracheostomy in Critically Ill COVID-19 Patients on Extracorporeal Membrane Oxygenation: A Single-Center Experience
Bibliographic record
Abstract
OBJECTIVES: Novel coronavirus-19 (COVID-19) has led to over 6 million fatalities globally. An estimated 75% of COVID-19 patients who require critical care admission develop acute respiratory distress syndrome (ARDS) needing invasive mechanical ventilation (IMV) and/or extracorporeal membrane oxygenation (ECMO). Due to prolonged ventilation requirements, these patients often also require tracheostomy. We performed a review of clinical outcomes in COVID-19 patients on ECMO at a high-volume tertiary care center in Hamilton, Ontario, Canada. METHODOLOGY: We performed a retrospective case series, including 24 adult patients diagnosed with COVID-19 who required IMV, veno-venous (ECMO), and tracheostomy. All patients were included from April to December 2021. We extracted demographic and clinical variables pertaining to the tracheostomy procedure and ECMO therapy. We performed descriptive statistical analyses. This study was approved by the Hamilton Integrated Research Ethics Board (14217-C). RESULTS: We included 24 consecutive patients with COVID-19 who required tracheostomy while undergoing ECMO therapy. The mean age was 49.4 years [standard deviation (SD): 7.33], the majority of patients were male (75%), with mean body mass index of 32 (SD: 8.81). Overall mortality rate was 33.3%. Percutaneous tracheostomy was performed most frequently (83.3%) and, similar to open tracheostomy, was associated with a low rate of perioperative bleeding complications. Within surviving patients, the mean time to IMV weaning and decannulation was 60.2 (SD: 24.6) and 49.4 days (SD: 21.8), respectively. CONCLUSION: Percutaneous tracheostomy appears to be safe in COVID-19 patients on ECMO and holding anticoagulation 24 hours prior to and after tracheostomy may limit bleeding events in these patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".