Long‐Term Follow‐up of Percutaneous Dilatational Tracheostomy in the Intensive Care Unit
Bibliographic record
Abstract
OBJECTIVE: The primary objective was to analyze percutaneous dilatation tracheostomy (PDT) management in the intensive care unit (ICU) by comparison with surgical tracheostomy (ST) outside of the ICU, with respect to: (i) long-term postoperative outcomes, including rate of follow-up, return to the emergency department, and major and minor complications; (ii) timing of decannulation, including time to decannulation, decannulation after >30 days, and decannulation at discharge. The secondary objective was to compare perioperative outcomes, including major and minor complications. METHODS: A retrospective study from April 2013 to 2024 at a tertiary referral center. Eligible patients included those over 18 years old without PDT contraindications who received PDT in the ICU or ST. RESULTS: Final analysis included 250 patients (125 [50%] PDT; 125 [50%] ST). The mean (SD) age of patients was 60.05 (16.41) years, and 85 (34.0%) were female. Compared with the ST group, the PDT group experienced significantly decreased long-term follow-up (41 [39.8%] vs. 115 [95.0%], respectively, p < 0.001), increased emergency department returns (61 [64.2%] vs. 31 [26.1%], p < 0.001), longer time to decannulation (estimated median difference: 11.00 days [95% CI: 7.00 to 15.00, p < 0.001]), increased decannulation after >30 days (23 [34.8%] vs. 13 [12.7%], p < 0.001), and similar postoperative complications (8 [8.4%] vs. 8 [6.8%], p = 0.664). The PDT group experienced significantly more perioperative complications (37 [30.1%] vs. 22 [17.6%], p = 0.021). CONCLUSION: The decreased long-term follow-up, delayed decannulation, and increased complications after PDT highlight potential pitfalls in ICU tracheostomy management, demonstrating the need for refined protocols, appropriate consultant involvement, and improved patient selection. LEVEL OF EVIDENCE: 3 Laryngoscope, 135:2306-2313, 2025.
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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".