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Record W2068071448 · doi:10.3109/10903127.2010.497902

Incidence of Rearrest After Return of Spontaneous Circulation in Out-of-Hospital Cardiac Arrest

2010· article· en· W2068071448 on OpenAlexfundno aff
David D. Salcido, Amanda M. Stephenson, Joseph P. Condle, Clifton W. Callaway, James J. Menegazzi

Bibliographic record

VenuePrehospital Emergency Care · 2010
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health Research
KeywordsMedicineAsystoleVentricular fibrillationReturn of spontaneous circulationPulseless electrical activityInternal medicineCardiopulmonary resuscitationCardiologyEmergency medical servicesPopulationIncidence (geometry)Emergency medicineResuscitation

Abstract

fetched live from OpenAlex

BACKGROUND: Return of spontaneous circulation (ROSC) occurs in 35.0 to 61.0% of emergency medical services (EMS)-treated out-of-hospital cardiac arrests (OHCAs); however, not all patients achieving ROSC survive to hospital arrival or discharge. Previous studies have estimated the incidence of some types of rearrest(RA) at 61.0 to 79.0%, and the electrocardiogram (ECG) waveform characteristics of prehospital RA rhythms have not been previously described. OBJECTIVES: We sought to determine the incidence of RA in OHCA, to classify RA events by type, and to measure the time from ROSC to RA. We also conducted a preliminary analysis of the relationship between first EMS-detected rhythms and RA, as well as the effect of RA on survival. METHODS: The Pittsburgh Regional Clinical Center of the National Heart, Lung, and Blood Institute (NHLBI) -sponsored Resuscitation Outcomes Consortium (ROC) provided cases from a population-based cardiac arrest surveillance program, ROC Epistry. Only OHCA cases of nontraumatic etiology with available and adequate ECG files were included. We analyzed defibrillator-monitor ECG tracings (Philips MRX), patient care reports (PCRs), and defibrillator audio recordings from EMS-treated cases of OHCA spanning the period from October 2006 to December 2008. We identified ROSC and RA through interpretation of ECG tracings and audio recordings. Rearrest events were categorized as ventricular fibrillation (VF), pulseless ventricular tachycardia (VT), asystole, and pulseless electrical activity (PEA) based on ECG waveform characteristics. Proportions of RA rhythms were stratified by first EMS rhythm and compared using Pearson's chi-square test. Logistic regression was used to test the predictive relationship between RA and survival to hospital discharge. RESULTS: Return of spontaneous circulation occurred in 329 of 1,199 patients (27.4% [95.0% confidence interval (CI): 25.0-30.0%]) treated for cardiac arrest. Of these, 113 had ECG tracings that were available and adequate for analysis. Rearrest occurred in 41 patients (36.0% [95.0% CI: 26.0-46.0%]), with a total of 69 RA events. Survival to hospital discharge in RA cases was 23.1% (95.0% CI: 11.1-39.3%), compared with 27.8% (95.0% CI: 17.9-39.6%) in cases without RA. Counts of RA events by type were as follows: 17 VF (24.6% [95% CI: 15.2-36.5%]), 20 pulseless VT (29.0% [95.0% CI: 18.7-41.2%]), 26 PEA (37.0% [95.0% CI: 26.3-50.2%]), and six asystole (8.8% [95.0% CI: 3.3-18.0%]). Rearrest was not predictive of survival to hospital discharge; however, initial EMS rhythm was predictive of RA shockability. The overall median (interquartile range) time from ROSC to RA among all events was 3.1 (1.6-6.3) minutes. CONCLUSION: In this sample, the incidence of RA was 38.0%. The most common type of RA was PEA. Shockability of first EMS rhythm was found to predict subsequent RA rhythm shockability.

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)0.004
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.005
GPT teacher head0.253
Teacher spread0.248 · 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

Citations62
Published2010
Admission routes1
Has abstractyes

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