Unplanned Extubation in the Neonatal ICU: A Systematic Review, Critical Appraisal, and Evidence-Based Recommendations
Bibliographic record
Abstract
OBJECTIVE: To update the state of knowledge on unplanned extubations (UEs) in neonatal ICUs. This review focuses on the following topics: incidence, risk factors, reintubation after UE, outcomes, and prevention. METHODS: The MEDLINE, EMBASE, CINAHL, Scielo, Lilacs, and Cochrane databases were searched for relevant publications from January 1, 1950, through January 30, 2012. Fifteen articles were selected for data abstraction. The search strategy included the following key words: "unplanned extubation," "accidental extubation," "self extubation," "unintentional extubation," "unexpected extubation," "inadvertent extubation," "unintended extubation," "spontaneous extubation," "treatment interference," and "airway accident." Study quality was assessed using the Newcastle-Ottawa scale. Grades of recommendation were assessed according to the Oxford Centre for Evidence-Based Medicine's levels of evidence system. Studies with Newcastle-Ottawa scale score ≥ 5 that included appropriate statistical analysis were deemed of high methodological quality. RESULTS: The overall mean Newcastle-Ottawa scale score was 3.5. UE rates ranged from 0.14 to 5.3 UEs/100 intubation days, or 1% to 80.8%. Risk factors included restlessness/agitation (13-89%), poor fixation of endotracheal tube (8.5-31%), tube manipulation at the time of UE (17-30%), and performance of a patient procedure at bedside (27.5-51%). One study showed that every day on mechanical ventilation increased the UE risk 3% (relative risk 1.03, P < .001). The association between birth weight/gestational age and UE is controversial. Reintubation rates ranged from 8.3% to 100%. There is still a gap of information about strategies addressed to reduce the incidence of UE. The best method of endotracheal tube securement remains a controversial issue. CONCLUSIONS: Despite numerous publications on UE, there are few studies assessing preventive strategies for adverse events and there is a lack of randomized clinical trials. Recommendations are proposed based on the current available literature.
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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 | 0.053 | 0.186 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.025 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".