The Impact of Implementing a “Pain, Agitation, and Delirium Bundle” in a Pediatric Intensive Care Unit: Improved Delirium Diagnosis
Bibliographic record
Abstract
Abstract Delirium is associated with significant negative outcomes, yet it remains underdiagnosed in children. We describe the impact of implementing a pain, agitation, and delirium (PAD) bundle on the rate of delirium detection in a pediatric intensive care unit (PICU). This represents a single-center, pre-/post-intervention retrospective and prospective cohort study. The study was conducted at a PICU in a quaternary university-affiliated pediatric hospital. All patients consecutively admitted to the PICU in October and November 2017 and 2018. Purpose of the study was describe the impact of the implementation of a PAD bundle. The rate of delirium detection and the utilization of sedative and analgesics in the pre- and post-implementation phases were measured. A total of 176 and 138 patients were admitted during the pre- and post-implementation phases, respectively. Of them, 7 (4%) and 44 (31.9%) were diagnosed with delirium (p < 0.001). Delirium was diagnosed in the first 48 hours of PICU admission and lasted for a median of 2 days (interquartile range [IQR]: 2–4). Delirium diagnosis was higher in patients receiving invasive ventilation (p < 0.001). Compliance with the PAD bundle scoring was 79% for the delirium scale. Score results were discussed during medical rounds for 68% of the patients in the post-implementation period. The number of patients who received opioids and benzodiazepines and the cumulative doses were not statistically different between the two cohorts. More patients received dexmedetomidine and the cumulative daily dose was higher in the post-implementation period (p < 0.001). The implementation of a PAD bundle in a PICU was associated with an increased recognition of delirium diagnosis. Further studies are needed to evaluate the impact of this increased diagnostic rate on short- and long-term outcomes.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".