MétaCan
Menu
Back to cohort

Risk Factors for Adverse Events in Emergency Department Procedural Sedation for Children

2017· article· en· W2748371715 on OpenAlexafffundabout
Maala Bhatt, David W. Johnson, Jason Chan, Monica Taljaard, Nick Barrowman, Ken Farion, Samina Ali, Suzanne Beno, Andrew Dixon, C. Michelle McTimoney, Alexander Sasha Dubrovsky, Nadia Sourial, Mark G. Roback

Bibliographic record

VenueJAMA Pediatrics · 2017
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsDalhousie UniversitySickKids FoundationMontreal Children's HospitalUniversity of TorontoUniversity of AlbertaHospital for Sick ChildrenIzaak Walton Killam Health CentreStollery Children's HospitalAlberta Children's HospitalChildren's Hospital of Eastern OntarioUniversity of CalgaryMcGill UniversityUniversity of OttawaOttawa Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineEmergency departmentSedationAdverse effectIncidence (geometry)VomitingEmergency medicineObservational studyPediatricsProspective cohort studyAnesthesiaSurgeryInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Importance: Procedural sedation for children undergoing painful procedures is standard practice in emergency departments worldwide. Previous studies of emergency department sedation are limited by their single-center design and are underpowered to identify risk factors for serious adverse events (SAEs), thereby limiting their influence on sedation practice and patient outcomes. Objective: To examine the incidence and risk factors associated with sedation-related SAEs. Design, Setting, and Participants: This prospective, multicenter, observational cohort study was conducted in 6 pediatric emergency departments in Canada between July 10, 2010, and February 28, 2015. Children 18 years or younger who received sedation for a painful emergency department procedure were enrolled in the study. Of the 9657 patients eligible for inclusion, 6760 (70.0%) were enrolled and 6295 (65.1%) were included in the final analysis. Exposures: The primary risk factor was receipt of sedation medication. The secondary risk factors were demographic characteristics, preprocedural medications and fasting status, current or underlying health risks, and procedure type. Main Outcomes and Measures: Four outcomes were examined: SAEs, significant interventions performed in response to an adverse event, oxygen desaturation, and vomiting. Results: Of the 6295 children included in this study, 4190 (66.6%) were male and the mean (SD) age was 8.0 (4.6) years. Adverse events occurred in 736 patients (11.7%; 95% CI, 6.4%-16.9%). Oxygen desaturation (353 patients [5.6%]) and vomiting (328 [5.2%]) were the most common of these adverse events. There were 69 SAEs (1.1%; 95% CI, 0.5%-1.7%), and 86 patients (1.4%; 95% CI, 0.7%-2.1%) had a significant intervention. Use of ketamine hydrochloride alone resulted in the lowest incidence of SAEs (17 [0.4%]) and significant interventions (37 [0.9%]). The incidence of adverse sedation outcomes varied significantly with the type of sedation medication. Compared with ketamine alone, propofol alone (3.7%; odds ratio [OR], 5.6; 95% CI, 2.3-13.1) and the combinations of ketamine and fentanyl citrate (3.2%; OR, 6.5; 95% CI, 2.5-15.2) and ketamine and propofol (2.1%; OR, 4.4; 95% CI, 2.3-8.7) had the highest incidence of SAEs. The combinations of ketamine and fentanyl (4.1%; OR, 4.0; 95% CI, 1.8-8.1) and ketamine and propofol (2.5%; OR, 2.2; 95% CI, 1.2-3.8) had the highest incidence of significant interventions. Conclusions and Relevance: The incidence of adverse sedation outcomes varied significantly with type of sedation medication. Use of ketamine only was associated with the best outcomes, resulting in significantly fewer SAEs and interventions than ketamine combined with propofol or fentanyl.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.306
Teacher spread0.282 · 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

Citations158
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueJAMA PediatricsSame topicAnesthesia and Sedative AgentsFrench-language works237,207