Improving Unplanned Extubation Rates in a Single Pediatric Intensive Care Unit Led by a Respiratory Therapist
Bibliographic record
Abstract
BACKGROUND: Unplanned extubations (UE) are directly associated with morbidity, mortality, and increased health care costs among critically-ill children. Multi-center implementation of the Solutions for Patient Safety Network prevention bundle has been successful, but UE rates remain a common cause of preventable health care harm. METHODS: This was a longitudinal (2020-2025), multi-intervention quality improvement (QI) project in a single quaternary care pediatric intensive care unit (PICU) driven largely by a new QI respiratory therapist. Interventions built upon the initial implementation of the UE prevention bundle. The smart aim of this project was to sustainably decrease UE/100 invasive ventilation days in the PICU at Children's of Alabama by 50% through multiple Plan-Do-Study-Act (PDSA) cycles. RESULTS: The baseline event rate was 0.58 UE/100 invasive ventilation days. Criteria for a center line shift to 0.16 UE/100 invasive mechanical ventilation days (72.5% decrease) were met in the first quarter of 2023 following four PDSA cycles and sustained through the end of the project. There was no change in invasive ventilation days/patient/quarter, but the percentage of PICU patients exposed to invasive ventilation by quarter starting dropped from 42% to 33% beginning in the first quarter of 2021. There were no center line shifts in post-UE outcomes over the project period including re-intubation within 1 h, re-intubation with cardiopulmonary resuscitation, or no re-intubation. CONCLUSIONS: Through multiple interventions, UE/100 invasive ventilation days decreased by 72.5% and has been sustained from the first quarter of 2023 through the second quarter of 2025 without impacting balancing measures such as invasive ventilation duration. These results support the importance of building upon the Solutions for Patient Safety Network UE prevention bundle and having a dedicated champion to drive improvement and sustainability.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".