Determinants of tracheostomy decannulation: an international survey
Bibliographic record
Abstract
BACKGROUND: Although tracheostomy is probably the most common surgical procedure performed on critically ill patients, it is unknown when a tracheostomy tube can be safely removed. METHODS: We performed a cross-sectional survey of physicians and respiratory therapists with expertise in the management of tracheostomized patients at 118 medical centers to characterize contemporary opinions about tracheostomy decannulation practice and to define factors that influence these practices. RESULTS: We surveyed 309 clinicians, of whom 225 responded (73%). Clinicians rated patient level of consciousness, ability to tolerate tracheostomy tube capping, cough effectiveness, and secretions as the most important factors in the decision to decannulate a patient. Decannulation failure was defined as the need to reinsert an artificial airway within 48 hours (45% of respondents) to 96 hours (20% of respondents) of tracheostomy removal, and 2% to 5% was the most frequent recommendation for an acceptable recannulation rate (44% of respondents). In clinical scenarios, clinicians who worked in chronic care facilities (30%) were less likely to recommend decannulation than clinicians who worked in weaning (47%), rehabilitation (53%), or acute care (55%) facilities (p = 0.015). Patients were most likely to be recommended for decannulation if they were alert and interactive (odds ratio [OR] 4.76, 95% confidence interval [CI] 3.27 to 6.90; p < 0.001), had a strong cough (OR 3.84, 95% CI 2.66 to 5.54; p < 0.001), had scant thin secretions (OR 2.23, 95% CI 1.56 to 3.19; p < 0.001), and required minimal supplemental oxygen (OR 2.04, 95% CI 1.45 to 2.86; p < 0.001). CONCLUSION: Patient level of consciousness, cough effectiveness, secretions, and oxygenation are important determinants of clinicians' tracheostomy decannulation opinions. Most surveyed clinicians defined decannulation failure as the need to reinsert an artificial airway within 48 to 96 hours of planned tracheostomy removal.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".