Unplanned Extubations Requiring Reintubation in Pediatric Critical Care: An Epidemiological Study
Bibliographic record
Abstract
OBJECTIVES: Unplanned extubations are an infrequent but life-threatening adverse event in pediatric critical care. Due to the rarity of these events, previous studies have been small, limiting the generalizability of findings and the ability to detect associations. Our objectives were to describe unplanned extubations and explore predictors of unplanned extubation requiring reintubation in PICUs. DESIGN: Retrospective observational study and multilevel regression model. SETTING: PICUs participating in Virtual Pediatric Systems (LLC). PATIENTS: Patients (≤ 18 yr) who had an unplanned extubation in PICU (2012-2020). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We developed and trained a multilevel least absolute shrinkage and selection operator (LASSO) logistic regression model in the 2012-2016 sample that accounted for between-PICU variations as a random effect to predict reintubation after unplanned extubation. The remaining sample (2017-2020) was used to externally validate the model. Predictors included age, weight, sex, primary diagnosis, admission type, and readmission status. Model calibration and discriminatory performance were evaluated using Hosmer-Lemeshow goodness-of-fit (HL-GOF) and area under the receiver operating characteristic curve (AUROC), respectively. Of the 5,703 patients included, 1,661 (29.1%) required reintubation. Variables associated with increased risk of reintubation were age (< 2 yr; odds ratio [OR], 1.5; 95% CI, 1.1-1.9) and diagnosis (respiratory; OR, 1.3; 95% CI, 1.1-1.6). Scheduled admission was associated with decreased risk of reintubation (OR, 0.7; 95% CI, 0.6-0.9). With LASSO (lambda = 0.011), remaining variables were age, weight, diagnosis, and scheduled admission. The predictors resulted in AUROC of 0.59 (95% CI, 0.57-0.61); HL-GOF showed the model was well calibrated (p = 0.88). The model performed similarly in external validation (AUROC, 0.58; 95% CI, 0.56-0.61). CONCLUSIONS: Predictors associated with increased risk of reintubation included age and respiratory primary diagnosis. Including clinical factors (e.g., oxygen and ventilatory requirements at the time of unplanned extubation) in the model may increase predictive ability.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".