Incidence, impact and indicators of difficult intubations in the neonatal intensive care unit: a report from the National Emergency Airway Registry for Neonates
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence, indicators and clinical impact of difficult tracheal intubations in the neonatal intensive care unit (NICU). DESIGN: Retrospective review of prospectively collected data on intubations performed in the NICU from the National Emergency Airway Registry for Neonates. SETTING: Ten academic NICUs. PATIENTS: Neonates intubated in the NICU at each of the sites between October 2014 and March 2017. MAIN OUTCOME MEASURES: Difficult intubation was defined as one requiring three or more attempts by a non-resident provider. Patient (age, weight and bedside predictors of difficult intubation), practice (intubation method and medications used), provider (training level and profession) and outcome data (intubation attempts, adverse events and oxygen desaturations) were collected for each intubation. RESULTS: Out of 2009 tracheal intubations, 276 (14%) met the definition of difficult intubation. Difficult intubations were more common in neonates <32 weeks, <1500 g. The difficult intubation group had a 4.9 odds ratio (OR) for experiencing an adverse event and a 4.2 OR for severe oxygen desaturation. Bedside screening tests of difficult intubation lacked sensitivity (receiver operator curve 0.47-0.53). CONCLUSIONS: Difficult intubations are common in the NICU and are associated with adverse event and severe oxygen desaturation. Difficult intubations occur more commonly in small preterm infants. The occurrence of a difficult intubation in other neonates is hard to predict due to the lack of sensitivity of bedside screening tests.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".