Evidence map›Paper›PMID 36600249›Full record

SynthesisEuropean journal of medical research2023

Survival outcome among patients with out-of-hospital cardiac arrest who received cardiopulmonary resuscitation in China: a systematic review and meta-analysis.

Guozhong Zhou, Yan Wang, Zihong Sun, Mingqi Yuan, Yunlin Ma, Qianxi Wu, Chunyan Wu, Jing Xu, Yongyi Li, Yunchuan Liu and 2 more

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in European journal of medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
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

13 citing papers in PubMed.

  1. Trial
  2. [A method for predicting neurological outcomes after cardiac arrest based on time-frequency domain feature fusion].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026
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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

12 authors.

Guozhong Zhou *Department of Science and Research, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, Yunnan, China.
Yan Wang *Department of Emergency Medicine, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, Yunnan, China.
Zihong SunSchool of Basic Medical Sciences, Kunming Medical University, Kunming, 650051, Yunnan, China.
Mingqi YuanIntensive Care Unit, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, Yunnan, China.
Yunlin MaDepartment of Emergency Medicine, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, Yunnan, China.
Qianxi WuIntensive Care Unit, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, Yunnan, China.
Chunyan WuIntensive Care Unit, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, Yunnan, China.
Jing XuEmergency Center, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, Yunnan, China.
Yongyi LiEmergency Center of Yunnan Province, Kunming, 650031, Yunnan, China.
Yunchuan LiuNursing Department, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, Yunnan, China.
Zhenzhou WangEmergency Center of Yunnan Province, Kunming, 650031, Yunnan, China. 408599838@qq.com.
Chao SongDepartment of Medical Imaging, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, 650302, Yunnan, China. chaoge6870@163.com.

Funding

the Special Project "Spring City Plan" Famous Doctor C202012016the Talent Project of Kunming Health Science and Technology 2022-SW [Leading Talents]-001
6 · The paper itself

Abstract

backgroundThis study aimed to assess the survival outcomes among patients with out-of-hospital cardiac arrest (CA) who received cardiopulmonary resuscitation (CPR) in China.

methodsRelevant studies, published between January 1, 2010 and September 5, 2022, were retrieved from databases, including EMBASE, PubMed, Cochrane Library, the China Biology Medicine disk, China National Knowledge Infrastructure, and Wanfang databases. We included clinical studies in which all patients were diagnosed with CA and underwent out-of-hospital CPR, and the outcome variables were at least one of the following: return of spontaneous circulation (ROSC), survival to admission, survival to hospital discharge, 1-month survival, achieved good neurological outcomes, and 1-year survival. Two investigators independently extracted the study data and assessed its quality using a modified Newcastle-Ottawa Scale tool. The data were pooled using random-effects models.

resultsOf the 3620 identified studies, 49 (63,378 patients) were included in the meta-analysis. The pooled ROSC rate was 9.0% (95% confidence interval [CI] 7.5-10.5%, I

conclusionThe survival outcomes of out-of-hospital CPR in China are far below the world average. Therefore, the policy of providing automated external defibrillators (AEDs) in public places and strengthening CPR training for healthcare providers and public personnel should be encouraged and disseminated nationwide. Trial registration This study was registered in PROSPERO (CRD42022326165) on 29 April 2022.

Indexed as

Cardiopulmonary ResuscitationEmergency Medical ServicesOut-of-Hospital Cardiac ArrestChinaHospitalizationHumansCardiopulmonary resuscitationEmergency medical servicesMeta-analysisOut-of-hospital cardiac arrestSurvival outcome

Identifiers

PMID36600249
PMCPMC9811716

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.