Delirium in Pediatric Patients With Respiratory Insufficiency Requiring Noninvasive Ventilation
Bibliographic record
Abstract
Background: Delirium is associated with increased length of stay, duration of mechanical ventilation, in-hospital mortality, and cost. Independent predictors of delirium include age < 2 years, developmental delay, severity of illness, mechanical ventilation, and administration of benzodiazepines and anticholinergic medications. Although patients receiving noninvasive ventilation (NIV) may have been included in prior studies, there are no data specifically focusing on delirium in children receiving NIV. Our primary aim was to investigate the prevalence of delirium in patients on NIV in the pediatric intensive care unit (PICU) and evaluate potentially modifiable risk factors for delirium. Methods: This was a single-center, retrospective study evaluating the prevalence of delirium as established by the Cornell Assessment of Pediatric Delirium (CAPD). We evaluated PICU patients ≤ 18 years old with respiratory insufficiency requiring ≥ 48 h of NIV. Patients receiving invasive mechanical ventilation were excluded from the analysis. Results: There were 202 patients that received ≥ 48 h of NIV during the study period. Of these patients, 43 patients had at least one CAPD score documented while on NIV. There were a total of 143 days on NIV and 137 days with CAPD documentation. The prevalence of delirium, defined as a CAPD score ≥ 9, was 67.4% (29 of 43 patients). Sixty-nine percent of the patients who experienced delirium received benzodiazepines, compared with 14% who did not experience delirium (P = 0.001). Most patients (83.7%) in this cohort received dexmedetomidine. Of patients who received dexmedetomidine and had delirium, 68% received benzodiazepines compared to 25% in the non-delirious group (P = 0.046). Conclusions: Delirium is common in young pediatric patients receiving NIV. As previously shown in the invasive mechanical ventilation population, benzodiazepine exposure continues to be a potentially modifiable risk factor for delirium.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".