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The impact of real-time feedback on ventilation quality during out-of-hospital cardiac arrest: A before-and-after study

2024· article· en· W4402577998 on OpenAlexaffabout
Ian R. Drennan, M.K. Lee, Jean-Philippe Héroux, Andrew Lee, John C. Riches, Jonathan Peppler, Annabel Poitras, Sheldon Cheskes

Bibliographic record

VenueResuscitation · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsSunnybrook HospitalSouth Bruce Grey Health CentreTrent UniversityRegional Municipality of WaterlooSunnybrook Health Science Centre
FundersZOLL Medical Corporation
KeywordsMedicineVentilation (architecture)Quality (philosophy)Emergency medicineIntensive care medicineMedical emergencyMeteorology

Abstract

fetched live from OpenAlex

INTRODUCTION: Ventilations are a critical component of cardiopulmonary resuscitation (CPR). There is conflicting evidence, however, on the most appropriate method of ventilation during cardiac arrest management. Recent evidence has suggested that regardless of the optimal ventilation strategy, ventilations are often not delivered compliant with guideline recommendations. Recent technological advancements have allowed for accurate measurement and real-time feedback of ventilation rate and volume during resuscitation. Simulation studies have found significant improvements in ventilations with the use of real-time feedback during simulated cardiac arrest. The use of feedback has not been studied in clinical practice. The objective of this study was to determine whether the use of real-time feedback improves compliance with pre-defined targets for ventilation rate and volume during out-of-hospital cardiac resuscitation. METHODS: This was a before-and-after study with four paramedic services in Ontario, Canada. We enrolled adult, out-of-hospital cardiac arrest (OHCA) patients where the ZOLL Accuvent® device was utilized to measure ventilation rate and volume. In the before phase (without feedback), the Accuvent® was used to measure ventilations, however, providers were blinded to the real-time feedback. In the after phase (with feedback), the feedback dashboard was activated and providers used the real-time feedback to guide their ventilations. All other aspects of resuscitation remained consistent throughout the study. The main objective of the study was to compare the proportion of each case that was compliant with pre-defined ventilation targets with real-time feedback and without real-time feedback. We also examined the use of advanced airways on ventilation quality and examined for associations between ventilation parameters and return of circulation. RESULTS: We enrolled 412 patients in the study (191 in the before phase without feedback and 221 in the after phase with feedback). Overall, we found significant improvements in both ventilation rate and volume in the after phase (with real-time feedback) compared to the before phase (without real-time feedback). We did not find any differences in ventilation compliance with or without advanced airways, or intra-arrest or post-cardiac arrest. CONCLUSION: The use of real-time feedback was associated with an increased proportion of ventilations that were compliant with pre-defined targets during cardiac resuscitation. Further work is required to improve the use of real-time ventilation feedback, and to determine the impact of ventilations on patient outcomes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.147
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.325
Teacher spread0.313 · 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.

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

Citations15
Published2024
Admission routes2
Has abstractyes

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