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Record W4361002254 · doi:10.1097/ccm.0000000000005847

Implementation of Video Laryngoscope-Assisted Coaching Reduces Adverse Tracheal Intubation-Associated Events in the PICU*

2023· article· en· W4361002254 on OpenAlexaff
John S. Giuliano, Ashwin Krishna, Natalie Napolitano, Josep Panisello, Asha Shenoi, Ronald C. Sanders, Kyle J. Rehder, Awni Al‐Subu, Calvin A. Brown, Lauren Edwards, Lisa V. Wright, Matthew Pinto, Ilana Harwayne‐Gidansky, Simon Parsons, Amy Romer, Elizabeth Laverriere, Justine Shults, Nicole K. Yamada, Catharine M. Walsh, Vinay Nadkarni, Akira Nishisaki

Bibliographic record

VenueCritical Care Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsThe Wilson CentreAlberta Children's Hospital
FundersAgency for Healthcare Research and Quality
KeywordsMedicineHypoxemiaAdverse effectIntubationAnesthesiaOdds ratioTracheal intubationLaryngoscopyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate implementation of a video laryngoscope (VL) as a coaching device to reduce adverse tracheal intubation associated events (TIAEs). DESIGN: Prospective multicenter interventional quality improvement study. SETTING: Ten PICUs in North America. PATIENTS: Patients undergoing tracheal intubation in the PICU. INTERVENTIONS: VLs were implemented as coaching devices with standardized coaching language between 2016 and 2020. Laryngoscopists were encouraged to perform direct laryngoscopy with video images only available in real-time for experienced supervising clinician-coaches. MEASUREMENTS AND MAIN RESULTS: The primary outcome was TIAEs. Secondary outcomes included severe TIAEs, severe hypoxemia (oxygen saturation < 80%), and first attempt success. Of 5,060 tracheal intubations, a VL was used in 3,580 (71%). VL use increased from baseline (29.7%) to implementation phase (89.4%; p < 0.001). VL use was associated with lower TIAEs (VL 336/3,580 [9.4%] vs standard laryngoscope [SL] 215/1,480 [14.5%]; absolute difference, 5.1%; 95% CI, 3.1-7.2%; p < 0.001). VL use was associated with lower severe TIAE rate (VL 3.9% vs SL 5.3%; p = 0.024), but not associated with a reduction in severe hypoxemia (VL 15.7% vs SL 16.4%; p = 0.58). VL use was associated with higher first attempt success (VL 71.8% vs SL 66.6%; p < 0.001). In the primary analysis after adjusting for site clustering, VL use was associated with lower adverse TIAEs (odds ratio [OR], 0.61; 95% CI, 0.46-0.81; p = 0.001). In secondary analyses, VL use was not significantly associated with severe TIAEs (OR, 0.72; 95% CI, 0.44-1.19; p = 0.20), severe hypoxemia (OR, 0.95; 95% CI, 0.73-1.25; p = 0.734), or first attempt success (OR, 1.28; 95% CI, 0.98-1.67; p = 0.073). After further controlling for patient and provider characteristics, VL use was independently associated with a lower TIAE rate (adjusted OR, 0.65; 95% CI, 0.49-0.86; p = 0.003). CONCLUSIONS: Implementation of VL-assisted coaching achieved a high level of adherence across the PICUs. VL use was associated with reduced adverse TIAEs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.041
GPT teacher head0.397
Teacher spread0.356 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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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