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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".