Evidence map›Paper›PMID 29940296›Full record

ArticleResuscitation2018

Arrest etiology among patients resuscitated from cardiac arrest.

Niel Chen, Clifton W Callaway, Francis X Guyette, Jon C Rittenberger, Ankur A Doshi, Cameron Dezfulian, Jonathan Elmer, Pittsburgh Post-Cardiac Arrest Service

Registry-linked trialAbstract read
In one paragraph

Article in Resuscitation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07002294 (The Effect of SPecialty cAre on Recovery From Cardiac Arrest Trial), which is not on this map. Cited by 65 papers.

0numbers the graph read from it
0cells of the map it votes in
65citing 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.

NCT07002294 narecruitingnot on this mapstarted 2026, after this paper: background citation

The Effect of SPecialty cAre on Recovery From Cardiac Arrest Trial (the SPARC Trial)

TypeinterventionalSponsorUniversity of PittsburghRan2026 to 2032Enrolled1,618ConditionsCardiac Arrest (CA), Heart Arrest, Heart Arrest, Out-Of-HospitalArmsTransfer to specialty care, Usual care
3 · Its place in the literature

Who cites it

65 citing papers in PubMed.

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5 more citing papers are in PubMed but not listed here.

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

8 authors.

Niel ChenUniversity of Pittsburgh School of Medicine, United States.
Clifton W CallawayDepartment of Emergency Medicine, University of Pittsburgh School of Medicine, United States.
Francis X GuyetteDepartment of Emergency Medicine, University of Pittsburgh School of Medicine, United States.
Jon C RittenbergerDepartment of Emergency Medicine, University of Pittsburgh School of Medicine, United States.
Ankur A DoshiDepartment of Emergency Medicine, University of Pittsburgh School of Medicine, United States.
Cameron DezfulianDepartment of Critical Care Medicine, University of Pittsburgh School of Medicine, United States.
Jonathan ElmerDepartment of Emergency Medicine, University of Pittsburgh School of Medicine, United States; Department of Critical Care Medicine, University of Pittsburgh School of Medicine, United States. Electronic address: elmerjp@upmc.edu.
Pittsburgh Post-Cardiac Arrest Service

Funding

University of Pittsburgh Clinical and Translational Science InstituteUL1TR001857 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2016 to 2025
$129.3M
Pittsburgh Career Development Program in Emergency Medicine ResearchK12HL109068 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CALLAWAY, CLIFTON W., YEALY, DONALD M · 2011 to 2015
$3.1M
Quantitative electroencephalography after cardiac arrestK23NS097629 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI ELMER, JONATHAN · 2017 to 2021
$945k
NCATS NIH HHS UL1 TR001857NHLBI NIH HHS K12 HL109068NINDS NIH HHS K23 NS097629
6 · The paper itself

Abstract

introductionCardiac arrest etiology is often assigned according to the Utstein template, which differentiates medical (formerly "presumed cardiac") from other causes. These categories are poorly defined, contain within them many clinically distinct etiologies, and are rarely based on diagnostic testing. Optimal clinical care and research require more rigorous characterization of arrest etiology.

methodsWe developed a novel system to classify arrest etiology using a structured chart review of consecutive patients treated at a single center after in- or out-of-hospital cardiac arrest over four years. Two reviewers independently reviewed a random subset of 20% of cases to calculate inter-rater reliability. We used X

resultsWe identified 14 principal arrest etiologies, and developed objective diagnostic criteria for each. Inter-rater reliability was high (kappa = 0.80). Median age of 986 included patients was 60 years, 43% were female and 71% arrested out-of-hospital. The most common etiology was respiratory failure (148 (15%)). A minority (255 (26%)) arrested due to cardiac causes. Only nine (1%) underwent a diagnostic workup that was unrevealing of etiology. Rates of awakening and survival to hospital discharge both differed across arrest etiologies, with survival ranging from 6% to 60% (both P < 0.001), and rates of favorable outcome ranging from 0% to 40% (P < 0.001). Timing and mechanism of death (e.g. multisystem organ failure or brain death) also differed significantly across etiologies.

conclusionsArrest etiology was identifiable in the majority cases via systematic chart review. "Cardiac" etiologies may be less common than previously thought. Substantial clinical heterogeneity exists across etiologies, suggesting previous classification systems may be insufficient.

Indexed as

Heart DiseasesRespiratory InsufficiencyCardiopulmonary ResuscitationCause of DeathClassificationDiagnostic Screening ProgramsFemaleHeart ArrestHumansMaleMiddle AgedPatient DischargePatient SelectionRandom AllocationReproducibility of ResultsUnited StatesCardiac arrestEpidemiologyEtiologyOutcomesPost-arrest

Identifiers

PMID29940296
PMCPMC6092216

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Registered trials

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.