Evidence map›Paper›PMID 40596889›Full record

ArticleBMC emergency medicine2025

Comparison of return of spontaneous circulation prediction scores in patients with cardiac arrest during ambulance transport.

Neslihan Ergün Süzer, Gülbin Aydoğdu Umaç, Süleyman Alpar, Sarper Yılmaz

Abstract readComparative Study
In one paragraph

Article in BMC emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Neslihan Ergün SüzerDepartment of Emergency Medicine, Kocaeli Darıca Farabi Training and Research Hospital, Kocaeli, Turkey. drergunsuzer@gmail.com.ORCID https://orcid.org/0000-0003-4839-8110
Gülbin Aydoğdu UmaçDepartment of Emergency Medicine, İzmir City Hospital, Izmir, Turkey.ORCID https://orcid.org/0000-0002-9874-0343
Süleyman AlparFaculty of Medicine, İstanbul Beykent University, Istanbul, Turkey.ORCID http://orcid.org/0000-0002-8509-0660
Sarper YılmazDepartment of Emergency Medicine, Kartal Dr. Lutfi Kirdar City Hospital, University of Health Sciences, Istanbul, Turkey.ORCID https://orcid.org/0000-0001-8166-659X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCardiac arrest during ambulance transport is a complex situation that has features of both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) but lacks a clear classification. This study aimed to evaluate the diagnostic performance of prehospital and in-hospital ROSC prediction tools in patients experiencing cardiac arrest during ambulance transport.

methodsA retrospective study was conducted with patients experiencing cardiac arrest during transport. Demographic, clinical, and treatment data were collected, including pre-arrest consciousness, arrest rhythm, and cardiopulmonary resuscitation duration. Four ROSC prediction scores (Prehospital-ROSC, the ROSC after cardiac arrest, Utstein-Based ROSC, and The Cardiac Arrest Survival Post-Resuscitation In-Hospital scores) were used to assess the patients. The AUROCs of the scores were compared to evaluate their diagnostic accuracy.

resultsPatients were categorized into two groups based on ROSC: No-ROSC (n = 248, 75.2%) and ROSC (n = 82, 24.8%). The ROSC group had significantly more shockable rhythms (81.7% vs. 22.2%, p < 0.001) and a higher proportion of cardiac etiology (p = 0.015) compared to the No-ROSC group. The time for the ambulance to reach the patient did not significantly differ between groups (p = 0.140), but the time spent in the ambulance before arrest was significantly shorter in the ROSC group (p = 0.026). The prehospital-ROSC score had the highest diagnostic performance (AUROC 0.791), followed by The ROSC after cardiac arrest score (0.754) and The Utstein-Based ROSC score (0.716). The Cardiac Arrest Survival Post-Resuscitation In-Hospital score had the lowest performance (0.658). Prehospital-ROSC score outperformed the Utstein-Based ROSC score (p = 0.005), and the Cardiac Arrest Survival Post-Resuscitation In-Hospital score was significantly lower than both ROSC after cardiac arrest score (p = 0.031) and the prehospital-ROSC score (p < 0.001).

conclusionPrehospital-ROSC score was the most accurate predictor of ROSC in patients experiencing cardiac arrest during ambulance transport, while the Cardiac Arrest Survival Post-Resuscitation In-Hospital score demonstrated lower predictive accuracy.

Indexed as

AmbulancesEmergency Medical ServicesHeart ArrestOut-of-Hospital Cardiac ArrestReturn of Spontaneous CirculationAgedCardiopulmonary ResuscitationFemaleHumansMaleMiddle AgedRetrospective StudiesCardiac arrest prediction modelsEmergency medical servicesOut-of-hospital cardiac arrestReturn of spontaneous circulation

Identifiers

PMID40596889
PMCPMC12220659

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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.