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      Association of Timing of Epinephrine Administration With Outcomes in Adults With Out-of-Hospital Cardiac Arrest

      research-article
      , MD, MS 1 , , , PhD 2 , , MD, PhD 1 , , MD, DrPH 3
      JAMA Network Open
      American Medical Association

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          Abstract

          This cohort study evaluates survival and functional status outcomes associated with timing of epinephrine administration in adults with out-of-hospital cardiac arrest with or without initial shockable cardiac rhythms.

          Key Points

          Question

          Is timing of epinephrine administration associated with outcomes in adults with out-of-hospital cardiac arrest?

          Findings

          In this cohort study with time-dependent propensity score and risk-set matching analysis of 41 079 adult patients from a large out-of-hospital cardiac arrest registry in the United States and Canada, survival to hospital discharge and favorable functional status at hospital discharge were statistically significant and differed according to the timing of epinephrine administration, and the risk ratios for survival and favorable functional status decreased with delayed administration of epinephrine.

          Meaning

          Findings of this study suggest that early epinephrine administration is associated with better survival outcomes in adult patients with shockable and nonshockable out-of-hospital cardiac arrest.

          Abstract

          Importance

          Administration of epinephrine has been found to be associated with an increased chance of survival after out-of-hospital cardiac arrest (OHCA), but the optimal timing of administration has not been fully investigated.

          Objective

          To ascertain whether there is an association between timing of epinephrine administration and patient outcomes after OHCA.

          Design, Setting, and Participants

          This cohort study included adults 18 years or older with OHCA treated by emergency medical services (EMS) personnel from April 1, 2011, to June 30, 2015. Initial cardiac rhythm was stratified as either initially shockable (ventricular defibrillation or pulseless ventricular tachycardia) or nonshockable (pulseless electrical activity or asystole). Eligible individuals were identified from among publicly available, deidentified patient-level data from the Resuscitation Outcomes Consortium Cardiac Epidemiologic Registry, a prospective registry of adults with EMS-treated, nontraumatic OHCA with 10 sites in North America. Data analysis was conducted from May 2019 to April 2021.

          Exposures

          Interval between advanced life support (ALS)–trained EMS personnel arrival at the scene and the first prehospital intravenous or intraosseous administration of epinephrine.

          Main Outcomes and Measures

          The primary outcome was survival to hospital discharge. In each cohort of initial cardiac rhythms, patients who received epinephrine at any period (minutes) after EMS arrival at the scene were matched with patients who were at risk of receiving epinephrine within the same period using time-dependent propensity scores calculated from patient demographic characteristics, arrest characteristics, and EMS interventions.

          Results

          Of 41 079 eligible individuals (median [interquartile range] age, 67 [55-79] years), 26 579 (64.7%) were men. A total of 10 088 individuals (24.6%) initially had shockable cardiac rhythms, and 30 991 (75.4%) had nonshockable rhythms. Those who received epinephrine included 8223 patients (81.5%) with shockable cardiac rhythms and 27 901 (90.0%) with nonshockable rhythms. In the shockable cardiac rhythm cohort, the risk ratio (RR) for receipt of epinephrine with survival to hospital discharge was highest between 0 and 5 minutes after EMS arrival (1.12; 95% CI, 0.99-1.26) across the categorized timing of the administration of epinephrine by 5-minute intervals after EMS arrival; however, that finding was not statistically significant. Treating the timing of epinephrine administration as a continuous variable, the RR for survival to hospital discharge decreased 5.5% (95% CI, 3.4%-7.5%; P < .001 for the interaction between epinephrine administration and time to matching) per minute after EMS arrival. In the nonshockable cardiac rhythm cohort, the RR for the association of receipt of epinephrine with survival to hospital discharge was the highest between 0 and 5 minutes (1.28; 95% CI, 0.95-1.72), although not statistically significant, and decreased 4.4% (95% CI, 0.8%-7.9%; P for interaction = .02) per minute after EMS arrival.

          Conclusions and Relevance

          Among adults with OHCA, survival to hospital discharge differed across the timing of epinephrine administration and decreased with delayed administration for both shockable and nonshockable rhythms.

          Related collections

          Most cited references33

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          Heart Disease and Stroke Statistics—2020 Update

          Circulation
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            An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

            The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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              Matching methods for causal inference: A review and a look forward.

              When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970's, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine, and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods-or developing methods related to matching-do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                10 August 2021
                August 2021
                10 August 2021
                : 4
                : 8
                : e2120176
                Affiliations
                [1 ]Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
                [2 ]Division of Biomedical Statistics, Department of Integrated Medicine, Osaka University Graduate School of Medicine, Suita, Japan
                [3 ]Department of Internal Medicine, Okinawa Prefectural Yaeyama Hospital, Okinawa, Japan
                Author notes
                Article Information
                Accepted for Publication: May 23, 2021.
                Published: August 10, 2021. doi:10.1001/jamanetworkopen.2021.20176
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Okubo M et al. JAMA Network Open.
                Corresponding Author: Masashi Okubo, MD, MS, Department of Emergency Medicine, University of Pittsburgh School of Medicine, 3600 Forbes Ave, Iroquois Building 400A, Pittsburgh, PA 15260 ( okubom@ 123456upmc.edu ).
                Author Contributions: Drs Okubo and Komukai had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Okubo, Komukai, Izawa.
                Acquisition, analysis, or interpretation of data: Okubo, Callaway, Izawa.
                Drafting of the manuscript: Okubo, Komukai, Callaway.
                Critical revision of the manuscript for important intellectual content: Okubo, Callaway, Izawa.
                Statistical analysis: Komukai, Callaway, Izawa.
                Administrative, technical, or material support: Callaway.
                Supervision: Callaway, Izawa.
                Conflict of Interest Disclosures: Dr Callaway reported receiving grants from the National Institutes of Health to study emergency care and cardiac arrest outside the submitted work and previous work in the development of resuscitation guidelines. No other disclosures were reported.
                Additional Contributions: We wish to acknowledge and thank all of the participating emergency medical services personnel, agencies, and medical directors as well as the hospitals that collected and contributed data for the Resuscitation Outcomes Consortium. We thank our colleagues from Osaka University Center of Medical Data Science and the Advanced Clinical Epidemiology Investigator’s Research Project for providing insight and expertise for our research; they were not compensated for their contributions.
                Article
                zoi210597
                10.1001/jamanetworkopen.2021.20176
                8356068
                34374770
                01fada6f-5433-4127-91c1-ecbb138023ad
                Copyright 2021 Okubo M et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 17 September 2020
                : 23 May 2021
                Categories
                Research
                Original Investigation
                Online Only
                Emergency Medicine

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