Tracheostomy in critically ill patients with SARS 2 COVID-19 infection: a prospective observational multi-center study of shortand long-term outcomes
Bibliographic record
Abstract
Background We report the characteristics, timing, and factors related to the decision to perform a tracheostomy in patients with confirmed COVID-19 infection admitted to eight Italian intensive care units (ICUs). Materials and methods Prospective observational cohort study of patients with COVID-19 disease on mechanical ventilation. Long-term functional impairment (up to 180 days' post-hospital discharge) was assessed using the Karnofsky scale. Kaplan--Meier analysis assessed differences in survival and freedom from tracheostomy in relation to ICU stay. Cox regression model was used to assess which variables impacted on tracheostomy as a categorical outcome. Results A total of 248 patients were recruited in the eight participating ICUs. Patients undergoing tracheostomy (n = 128) had longer ICU (25 (18--36) vs. 10 (7--16), P = 0.001) and hospital (37 (26.5--50) vs. 19 (8.5--34.5) P = 0.02) stays. ICU and hospital mortality of patients tracheostomized was 34% and 37%, respectively. Cumulative survival Kaplan--Meier analysis documented improved survival rates in patients undergoing tracheostomy (Log-Rank, Mantel--Cox = 4.8, P = 0.028). Median Karnofsky scale values improved over time but were similar between survivors receiving or not receiving tracheostomy. No healthcare worker involved in the tracheostomy procedure developed COVID-19 infection during the study period. Conclusions Patients with COVID-19 infection who underwent tracheostomy had a better cumulative survival but similar long-term functional outcomes at 30, 60, and 180 days after hospital discharge.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".