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Record W4362692337 · doi:10.1097/pcc.0000000000003167

Unplanned Extubations Requiring Reintubation in Pediatric Critical Care: An Epidemiological Study

2023· article· en· W4362692337 on OpenAlexaff
Krista Wollny, Deborah McNeil, Stephana J. Moss, Tolulope T. Sajobi, Simon Parsons, Karen Benzies, Amy Metcalfe

Bibliographic record

VenuePediatric Critical Care Medicine · 2023
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsAlberta Children's HospitalAlberta HealthUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsMedicineLogistic regressionReceiver operating characteristicEmergency medicineRetrospective cohort studyOdds ratioGeneralizability theoryObservational studyPediatricsInternal medicineStatistics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.040
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.150
GPT teacher head0.510
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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