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Abstract Su305: Chest Compression Fraction, Bag-mask Ventilation, And Survival From Out-of-hospital Cardiac Arrest: A Multicenter Study

2024· article· en· W4404344103 on OpenAlexaff
Betty Yang, Elisabete Aramendi, Brian G. Leroux, Xabier Jaureguibeitia, Sarah Shaver, Mary P. Chang, Thomas D. Rea, Peter J. Kudenchuk, Jim Christenson, Christian Sandrock, Clifton W. Callaway, David D. Salcido, Jonas Carson, Jennifer Blackwood, Henry E. Wang, Ahamed H. Idris

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsOttawa HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineCardiopulmonary resuscitationVentilation (architecture)Multicenter studyCardiologyInternal medicineEmergency medicineResuscitationRandomized controlled trial

Abstract

fetched live from OpenAlex

Background: Ventilation during cardiopulmonary resuscitation (CPR) is poor. Better lung inflation during chest compression pauses is associated with better return of spontaneous circulation (ROSC), survival, and functional neurological survival. Chest compression quality, quantified by chest compression fraction (CCF) >80%, has also served as a guideline target for improved outcomes. We hypothesized that greater chest compression fraction modifies the association between ventilation quality and outcomes. Methods: We studied patients suffering out-of-hospital cardiac arrest from six sites of the Resuscitation Outcomes Consortium (ROC) Trial of Continuous or Interrupted Chest Compressions during CPR. We performed a subanalysis of patients assigned to the 30:2 CPR arm with ≥ two minutes of thoracic bioimpedance signal recorded with a cardiac defibrillator/monitor. Detectable ventilation waveforms were defined as having a bioimpedance amplitude ≥0.5 Ohm (corresponding to ≥ 250 ml tidal volume) and duration ≥1 sec. We defined a chest compression pause as a break in chest compressions of 3 - 15 sec duration. We dichotomized ventilation into two groups: 1) better lung inflation (≥ one lung inflation waveform in ≥50% of chest compression pauses), and 2) poor lung inflation (waveforms in <50% pauses). We analyzed CCF during bag-mask prior to intubation (if any). We used multivariable logistic regression to assess the relationship among CCF, ventilation, and the outcomes of ROSC, survival, and functional neurologic survival. Results: Among 1,976 patients, mean age was 65 years, 66% were male. Most patients received poor ventilation (60%, n=1177) over ventilation over 9.8 ± 4.9 minutes. Median CCF was 0.78 (0.71, 90.84) and 0.78 (0.70, 0.83) for poor and better lung inflation, respectively. For CCF <85%, survival was significantly greater in those who received better ventilation, when compared to poor ventilation, and had greater pauses per minute (Figure A). The beneficial interaction between ventilation and outcome was greater with CCFs of 85% or less, compared to higher CCFs (Figure B). With CCFs >85%, survival did not improve significantly with better ventilation. These results persisted after adjustment for Ustein variables and ROC site. Conclusions: We observed improved survival with better ventilation until CCF exceeded 85% in the preintubation period, suggesting an optimal strategy with better ventilation and chest compression fractions of 85% or less.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.017
GPT teacher head0.292
Teacher spread0.276 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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