Care bundles to reduce unplanned extubation in critically ill children: a systematic review, critical appraisal and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the current evidence for the efficacy of care bundles in reducing unplanned extubations (UEs) in critically ill children. DESIGN: Systematic review according to the Cochrane guidelines and meta-analysis using random-effects modelling. METHODS: We searched MEDLINE, EMBASE, CINAHL, Web of Science, Scopus, Cochrane and SciELO databases from inception until April 2021. We conducted a quality appraisal for each study using the Newcastle-Ottawa Scale and Standards for Quality Improvement Reporting Excellence (SQUIRE) V.2.0 checklist. MAIN OUTCOME: The primary outcome measure was UE rates per 100 intubation days. RESULTS: We screened 10 091 records and finally included 11 studies. Six studies were pre/post-intervention studies, and five were interrupted time-series studies. The methodological quality was 'good' in 70%, and the remaining as 'fair' (30%). The most frequently used implementation strategies were staff education (100%), root cause analysis (100%), and audit and feedback (82%). Key bundle care components comprised identification of high-risk patients, endotracheal tube care and sedation protocol. Not all studies fully completed the SQUIRE V.2.0 checklist. Meta-analysis revealed a reduction in UE rate following the introduction of care bundles (rate ratio: 0.40 (95% CI: 0.19 to 0.84); p=0.02), which equates to a 60% reduction in UE rates. CONCLUSIONS: We found that identifying high-risk patients, endotracheal tube care and protocol-directed sedation are core elements in care bundles for preventing UEs. However, there are several methodological gaps in the literature, including poor evaluation of adherence to bundle components. Future studies should address these gaps to strengthen their validity.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.032 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".