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      Effect of Prehospital Epinephrine on Outcomes of Out-of-Hospital Cardiac Arrest: A Bayesian Network Approach

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          Abstract

          Background

          The benefit of prehospital epinephrine in out-of-hospital cardiac arrest (OHCA) was shown in a recent large placebo-controlled trial. However, placebo-controlled studies cannot identify the nonpharmacologic influences on concurrent or downstream events that might modify the main effect positively or negatively. We sought to identify the real-world effect of epinephrine from a clinical registry using Bayesian network with time-sequence constraints.

          Methods

          We analyzed a prospective regional registry of OHCA where a prehospital advanced life support (ALS) protocol named “Smart ALS (SALS)” was gradually implemented from July 2015 to December 2016. Using Bayesian network, a causal structure was estimated. The effect of epinephrine and SALS program was modelled based on the structure using extended Cox-regression and logistic regression, respectively.

          Results

          Among 4324 patients, SALS was applied to 2351 (54.4%) and epinephrine was administered in 1644 (38.0%). Epinephrine was associated with faster ROSC rate in nonshockable rhythm (HR: 2.02, 6.94, and 7.43; 95% CI: 1.08–3.78, 4.15–11.61, and 2.92–18.91, respectively, for 1–10, 11–20, and >20 minutes) while it was associated with slower rate up to 20 minutes in shockable rhythm (HR: 0.40, 0.50, and 2.20; 95% CI: 0.21–0.76, 0.32–0.77, and 0.76–6.33). SALS was associated with increased prehospital ROSC and neurologic recovery in noncardiac etiology (HR: 5.36 and 2.05; 95% CI: 3.48–8.24 and 1.40–3.01, respectively, for nonshockable and shockable rhythm).

          Conclusions

          Epinephrine was associated with faster ROSC rate in nonshockable rhythm but slower rate in shockable rhythm up to 20 minutes. SALS was associated with improved prehospital ROSC and neurologic recovery in noncardiac etiology.

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          Most cited references17

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          European Resuscitation Council Guidelines for Resuscitation 2015: Section 3. Adult advanced life support.

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            Reducing bias through directed acyclic graphs

            Background The objective of most biomedical research is to determine an unbiased estimate of effect for an exposure on an outcome, i.e. to make causal inferences about the exposure. Recent developments in epidemiology have shown that traditional methods of identifying confounding and adjusting for confounding may be inadequate. Discussion The traditional methods of adjusting for "potential confounders" may introduce conditional associations and bias rather than minimize it. Although previous published articles have discussed the role of the causal directed acyclic graph approach (DAGs) with respect to confounding, many clinical problems require complicated DAGs and therefore investigators may continue to use traditional practices because they do not have the tools necessary to properly use the DAG approach. The purpose of this manuscript is to demonstrate a simple 6-step approach to the use of DAGs, and also to explain why the method works from a conceptual point of view. Summary Using the simple 6-step DAG approach to confounding and selection bias discussed is likely to reduce the degree of bias for the effect estimate in the chosen statistical model.
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              A Randomized Trial of Epinephrine in Out-of-Hospital Cardiac Arrest

              Concern about the use of epinephrine as a treatment for out-of-hospital cardiac arrest led the International Liaison Committee on Resuscitation to call for a placebo-controlled trial to determine whether the use of epinephrine is safe and effective in such patients.
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                Author and article information

                Contributors
                Journal
                Emerg Med Int
                Emerg Med Int
                EMI
                Emergency Medicine International
                Hindawi
                2090-2840
                2090-2859
                2020
                1 August 2020
                : 2020
                : 8057106
                Affiliations
                1Department of Emergency Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil Bundang-gu, Seongnam-si Gyeonggi-do, Seongnam 13620, Republic of Korea
                2Department of Emergency Medicine, Soonchunhyang University Bucheon Hospital, 170, Jomaru-ro Bucheon-si Gyeonggi-do, Bucheon 14584, Republic of Korea
                3Department of Emergency Medicine, Dankook University Hospital, 201, Manghyang-ro Dongnam-gu, Cheonan-si Chungcheongnam-do, Cheonan 31116, Republic of Korea
                4Department of Emergency Medicine, Soonchunhyang University Hospital, 44, Suncheonhyang 4-gil Dongnam-gu, Cheonan-si Chungcheongnam-do, Asan 31151, Republic of Korea
                Author notes

                Academic Editor: Jeffrey R. Avner

                Author information
                https://orcid.org/0000-0003-2720-7442
                Article
                10.1155/2020/8057106
                7416253
                32802513
                92bf0388-3d3d-4c24-a81f-352656a070bd
                Copyright © 2020 Joonghee Kim et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 April 2020
                : 29 June 2020
                : 8 July 2020
                Funding
                Funded by: Soonchunhyang University
                Funded by: Ministry of Health and Welfare
                Award ID: 090-091-2800-2832-309
                Funded by: Seoul National University
                Award ID: 02-2013-060
                Categories
                Research Article

                Emergency medicine & Trauma
                Emergency medicine & Trauma

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