P0052 / #780: A MULTIFACETED QUALITY IMPROVEMENT INITIATIVE TO DECREASE IATROGENIC WITHDRAWAL SYNDROME IN CHILDREN NEEDING INTUBATION AND VENTILATION
Bibliographic record
Abstract
Aims & Objectives: Iatrogenic withdrawal syndrome (IWS) is still a common complication in pediatric critical care. The objectives of this study were to improve the assessment of iatrogenic withdrawal syndrome (IWS) and to reduce its incidence and severity in children that were intubated and ventilated in a tertiary medical-surgical pediatric intensive care unit (PICU). Methods: This quality improvement (QI) study describes the effect of 4 interventions on IWS: a non-pharmacological bundle of measures for pain and agitation; education through simulation, newsletters, and posters; and the introduction of order sets reflecting the pain and agitation management protocol. The team monitored sedation, analgesia, and withdrawal scores biweekly, utilizing an electronic medical record report and direct observation. Interviews with frontline staff helped to optimize the interventions. Data were analyzed with process control charts, and the progress was displayed in a QI board in the PICU. Results: 293 intubated and ventilated patients, admitted from January 2018 to December 2019 were included, 211 of them assessed for IWS. The proportion of intubated patients assessed for withdrawal increased from 40% to 100%. The incidence of IWS did not change significantly with the interventions, oscillating around the mean of 83% in the high-risk population. The severity of withdrawal was reduced and sustained to 4, from 6 before the intervention. Unplanned extubation, severe pain or under sedation episodes were not affected by the interventions. Conclusions: This QI project was able to improve assessment and sustain a reduction in the severity of withdrawal, without compromising patient comfort and safety.
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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 | 0.007 | 0.014 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".