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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".