Evidence map›Paper›PMID 35699202›Full record

ArticleJournal of the American Heart Association2022

Prediction of Prehospital Change of the Cardiac Rhythm From Nonshockable to Shockable in Out-of-Hospital Patients With Cardiac Arrest: A Post Hoc Analysis of a Nationwide, Multicenter, Prospective Registry.

Ryo Emoto, Mitsuaki Nishikimi, Muhammad Shoaib, Kei Hayashida, Kazuki Nishida, Kazuya Kikutani, Shinichiro Ohshimo, Shigeyuki Matsui, Nobuaki Shime, Taku Iwami

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in Journal of the American Heart Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.3field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 2 countries.

Ryo EmotoDepartment of Biostatistics Nagoya University Graduate School of Medicine Nagoya Japan.ORCID 0000-0003-1722-6762
Mitsuaki NishikimiDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences Hiroshima University Hiroshima Japan.ORCID 0000-0003-2342-7245
Muhammad ShoaibLaboratory for Critical Care Physiology The Feinstein Institutes for Medical Research Manhasset NY.ORCID 0000-0002-7686-2817
Kei HayashidaLaboratory for Critical Care Physiology The Feinstein Institutes for Medical Research Manhasset NY.ORCID 0000-0002-2287-3318
Kazuki NishidaDepartment of Biostatistics Nagoya University Graduate School of Medicine Nagoya Japan.ORCID 0000-0003-0367-8557
Kazuya KikutaniDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences Hiroshima University Hiroshima Japan.ORCID 0000-0003-3159-0333
Shinichiro OhshimoDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences Hiroshima University Hiroshima Japan.ORCID 0000-0003-1119-3322
Shigeyuki MatsuiDepartment of Biostatistics Nagoya University Graduate School of Medicine Nagoya Japan.
Nobuaki ShimeDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences Hiroshima University Hiroshima Japan.ORCID 0000-0002-9113-6151
Taku IwamiDepartment of Preventive Services, School of Public Health, Graduate School of Medicine Kyoto University Kyoto Japan.
Hiroshima University · JPNagoya University · JPFeinstein Institute for Medical Research · USKyoto University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Predicting a spontaneous rhythm change from nonshockable to shockable before hospital arrival in patients with out-of-hospital cardiac arrest can help emergency medical services develop better strategies for prehospital treatment. The aim of this study was to identify predictors of spontaneous rhythm change before hospital arrival in patients with out-of-hospital cardiac arrest and develop a predictive scoring system. Methods and Results We retrospectively reviewed data of eligible patients with out-of-hospital cardiac arrest with an initial nonshockable rhythm registered in a nationwide registry between June 2014 and December 2017. We performed a multivariable analysis using a Cox proportional hazards model to identify predictors of a spontaneous rhythm change, and a ridge regression model for predicting it. The data of 25 804 patients were analyzed (derivation cohort, n=17 743; validation cohort, n=8061). The rhythm change event rate was 4.1% (724/17 743) in the derivation cohort, and 4.0% (326/8061) in the validation cohorts. Age, sex, presence of a witness, initial rhythm, chest compression by a bystander, shock with an automated external defibrillator by a bystander, and cause of the cardiac arrest were all found to be independently associated with spontaneous rhythm change before hospital arrival. Based on this finding, we developed and validated the Rhythm Change Before Hospital Arrival for Nonshockable score. The Harrell's concordance index values of the score were 0.71 and 0.67 in the internal and external validations, respectively. Conclusions Seven factors were identified as predictors of a spontaneous rhythm change from nonshockable to shockable before hospital arrival. We developed and validated a score to predict rhythm change before hospital arrival.

Indexed as

Cardiopulmonary ResuscitationEmergency Medical ServicesOut-of-Hospital Cardiac ArrestHospitalsHumansRegistriesRetrospective Studiesdefibrillationnonshockableout‐of‐hospital cardiac arrestparamedicsrhythm changeshockable

Identifiers

PMID35699202
PMCPMC9238669
OpenAlexW4282829614

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.