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Association of Extubation Failure Rates With High-Flow Nasal Cannula, Continuous Positive Airway Pressure, and Bilevel Positive Airway Pressure vs Conventional Oxygen Therapy in Infants and Young Children

2023· review· en· W4379376253 on OpenAlexaff
Narayan P. Iyer, Alexandre T. Rotta, Sandrine Essouri, José Roberto Fioretto, Hannah J. Craven, Elizabeth C. Whipple, Padmanabhan Ramnarayan, Samer Abu‐Sultaneh, Robinder G. Khemani

Bibliographic record

VenueJAMA Pediatrics · 2023
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Heart, Lung, and Blood InstituteNational Institute of Child Health and Human DevelopmentSchool of Medicine, Indiana UniversityNational Institutes of Health
KeywordsMedicineNasal cannulaContinuous positive airway pressureAnesthesiaMean airway pressureRandomized controlled trialOdds ratioPediatric intensive care unitRelative riskPositive airway pressureRespiratory failureMechanical ventilationCrossover studyIntensive care medicineSurgeryInternal medicineConfidence intervalCannulaPlaceboObstructive sleep apnea

Abstract

fetched live from OpenAlex

Importance: Extubation failure (EF) has been associated with worse outcomes in critically ill children. The relative efficacy of different modes of noninvasive respiratory support (NRS) to prevent EF is unknown. Objective: To study the reported relative efficacy of different modes of NRS (high-flow nasal cannula [HFNC], continuous positive airway pressure [CPAP], and bilevel positive airway pressure [BiPAP]) compared to conventional oxygen therapy (COT). Data Sources: MEDLINE, Embase, and CINAHL Complete through May 2022. Study Selection: Randomized clinical trials that enrolled critically ill children receiving invasive mechanical ventilation for more than 24 hours and compared the efficacy of different modes of postextubation NRS. Data Extraction and Synthesis: Random-effects models were fit using a bayesian network meta-analysis framework. Between-group comparisons were estimated using odds ratios (ORs) or mean differences with 95% credible intervals (CrIs). Treatment rankings were assessed by rank probabilities and the surface under the cumulative rank curve (SUCRA). Main Outcomes and Measures: The primary outcome was EF (reintubation within 48 to 72 hours). Secondary outcomes were treatment failure (TF, reintubation plus NRS escalation or crossover to another NRS mode), pediatric intensive care unit (PICU) mortality, PICU and hospital length of stay, abdominal distension, and nasal injury. Results: A total of 11 615 citations were screened, and 9 randomized clinical trials with a total of 1421 participants were included. Both CPAP and HFNC were found to be more effective than COT in reducing EF and TF (CPAP: OR for EF, 0.43; 95% CrI, 0.17-1.0 and OR for TF 0.27, 95% CrI 0.11-0.57 and HFNC: OR for EF, 0.64; 95% CrI, 0.24-1.0 and OR for TF, 0.34; 95% CrI, 0.16- 0.65). CPAP had the highest likelihood of being the best intervention for both EF (SUCRA, 0.83) and TF (SUCRA, 0.91). Although not statistically significant, BiPAP was likely to be better than COT for preventing both EF and TF. Compared to COT, CPAP and BiPAP were reported as showing a modest increase (approximately 3%) in nasal injury and abdominal distension. Conclusions and Relevance: The studies included in this systematic review and network meta-analysis found that compared with COT, EF and TF rates were lower with modest increases in abdominal distension and nasal injury. Of the modes evaluated, CPAP was associated with the lowest rates of EF and TF.

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)0.012
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.046
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.013
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations43
Published2023
Admission routes1
Has abstractyes

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