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Original Research

Open Access Special Issue

Qualitative and quantitative analysis of emergency department cardiac arrest publications

  • Hsiang-Yun Lo1,2
  • Shang-Kai Hung1
  • Chip-Jin Ng1
  • Shou-Yen Chen1,3

1Department of Emergency Medicine, Chang Gung Memorial Hospital and Chang Gung University, 333 Taoyuan, Taiwan

2Institute of health policy and management, National Taiwan University, 106 Taipei, Taiwan

3Graduate Institute of Clinical Medical Sciences. Division of Medical Education, College of Medicine, Chang Gung University, 333 Taoyuan, Taiwan

DOI: 10.22514/sv.2021.209 Vol.18,Issue 2,March 2022 pp.78-87

Submitted: 06 July 2021 Accepted: 10 August 2021

Published: 08 March 2022

(This article belongs to the Special Issue Emergency Department Cardiac Arrest (EDCA))

*Corresponding Author(s): Shou-Yen Chen E-mail:


Cardiac arrest is a medical emergency with a poor prognosis. Patient characteristics and outcomes are associated with location and are traditionally categorized into out-of-hospital cardiac arrest (OHCA) or in-hospital cardiac arrest (IHCA). Increasing evidence has revealed that cardiac arrest occurring in the emergency department is distinct from OHCA or IHCA in other locations in hospitals, but most academic publications combine these populations and apply the knowledge arising from OHCA or IHCA to patients with emergency department cardiac arrest (EDCA). The aim of this study was to identify the research direction of EDCA in the past 20 years and to analyze the characteristics and content of academic publications. We searched the MEDLINE and EMBASE databases for eligible articles until May 30, 2021. Two independent reviewers extracted data by using a customized form to record crucial information, and any conflicts between the two reviewers were resolved through discussion with another independent reviewer. The aggregated data underwent a scoping review and analyzed qualitatively and quantitatively. In total, 52 original articles investigating EDCA were included; only 15 articles simply focused on EDCA, while other articles involved OHCA or IHCA simultaneously. There were 3 articles discussing the relationship of overcrowdedness and EDCA, 12 articles for prediction and risk factors associated with EDCA, 15 articles for epidemiology and prognosis, and 22 articles for specific diagnostic or resuscitation skills with regard to EDCA. Studies focusing on EDCA are increasing but still scarce. Applying the knowledge arising from OHCA or IHCA to EDCA is questionable, and research focused on EDCA is necessary. ED overcrowdedness-associated EDCA and prediction models for EDCA are essential topics that need further investigation.


Emergency department cardiac arrest; Resuscitation; In-hospital cardiac arrest; Overcrowdedness

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Hsiang-Yun Lo,Shang-Kai Hung,Chip-Jin Ng,Shou-Yen Chen. Qualitative and quantitative analysis of emergency department cardiac arrest publications. Signa Vitae. 2022. 18(2);78-87.


[1] Jacobs I, Nadkarni V, Bahr J, Berg RA, Billi JE, Bossaert L, et al. Cardiac arrest and cardiopulmonary resuscitation outcome reports: update and simplification of the Utstein templates for resuscitation registries: a statement for healthcare professionals from a task force of the Interna-tional Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian Resuscitation Council, New Zealand Resuscitation Council, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Councils of Southern Africa). Circulation. 2004; 110: 3385–3397.

[2] Høybye M, Stankovic N, Holmberg M, Christensen HC, Granfeldt A, Andersen LW. In-Hospital vs. Out-of-Hospital Cardiac Arrest: Patient Characteristics and Survival. Resuscitation. 2021; 158: 157–165.

[3] Moskowitz A, Holmberg MJ, Donnino MW, Berg KM. In-hospital cardiac arrest: are we overlooking a key distinction? Current Opinion in Critical Care. 2018; 24: 151–157.

[4] Nolan JP, Soar J, Smith GB, Gwinnutt C, Parrott F, Power S, et al. Incidence and outcome of in-hospital cardiac arrest in the United Kingdom National Cardiac Arrest Audit. Resuscitation. 2014; 85: 987–992.

[5] Kayser RG, Ornato JP, Peberdy MA; American Heart Association National Registry of Cardiopulmonary Resuscitation. Cardiac arrest in the Emergency Department: a report from the National Registry of Cardiopulmonary Resuscitation. Resuscitation. 2008; 78: 151–160.

[6] Gräsner JT, Herlitz J, Tjelmeland IBM, Wnent J, Masterson S, Lilja G, et al. European Resuscitation Council Guidelines 2021: Epidemiology of cardiac arrest in Europe. Resuscitation. 2021; 161: 61–79.

[7] Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Annals of Internal Medicine. 2018; 169: 467–473.

[8] Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology. 2018; 18: 143.

[9] Valderrama AL, Fang J, Merritt RK, Hong Y. Cardiac arrest patients in the emergency department-National Hospital Ambulatory Medical Care Survey, 2001–2007. Resuscitation. 2011; 82: 1298–1301.

[10] Tan SC, Leong BS. Cardiac arrests within the emergency department: an Utstein style report, causation and survival factors. European Journal of Emergency Medicine. 2018; 25: 12–17.

[11] Mitchell OJL, Edelson DP, Abella BS. Predicting cardiac arrest in the emergency department. Journal of the American College of Emergency Physicians Open. 2020; 1: 321–326.

[12] Lee MJ, Ryu JH, Min MK, Lee DS, Yeom SR, Bae BK, et al. Predictors of survival and good neurological outcomes after in-hospital cardiac arrest. Signa Vitae. 2021; 17; 67–76.

[13] Chang Y, Shih H, Chen C, Chen W, Huang F, Muo C. Association of sudden in-hospital cardiac arrest with emergency department crowding. Resuscitation. 2019; 138: 106–109.

[14] Ye S, Liu J, He Y, Cao Y. Emergency department crowding might not strongly associated with higher incidence of in-hospital cardiac arrest. Resuscitation. 2019; 140: 72–73.

[15] Kim JS, Bae HJ, Sohn CH, Cho SE, Hwang J, Kim WY, et al. Maximum emergency department overcrowding is correlated with occurrence of unexpected cardiac arrest. Critical Care. 2020; 24: 305.

[16] Tsai L, Chien W, Chen C, Tsai S, Chaou C, Weng Y, et al. Association of patient-to-emergency department staff ratio with the incidence of cardiac arrest: A retrospective cohort study. Signa Vitae. 2021; 17; 118–124.

[17] Hwang U, McCarthy ML, Aronsky D, Asplin B, Crane PW, Craven CK, et al. Measures of crowding in the emergency department: a systematic review. Academic Emergency Medicine. 2011; 18: 527–538.

[18] Asplin BR, Magid DJ, Rhodes KV, Solberg LI, Lurie N, Camargo CA Jr. A conceptual model of emergency department crowding. Annals of Emergency Medicine. 2003; 42: 173–180.

[19] Jones P, Wells S, Ameratunga S. Towards a best measure of emergency department crowding: Lessons from current Australasian practice. Emergency Medicine Australasia. 2018; 30: 214–221.

[20] Wang A, Fang C, Chen S, Tsai S, Kao W. Periarrest Modified Early Warning Score (MEWS) predicts the outcome of in-hospital cardiac arrest. Journal of the Formosan Medical Association. 2016; 115: 76–82.

[21] Kim I, Song H, Kim HJ, Park KN, Kim SH, Oh SH, et al. Use of the National Early Warning Score for predicting in-hospital mortality in older adults admitted to the emergency department. Clinical and Experimental Emergency Medicine. 2020; 7: 61–66.

[22] Srivilaithon W, Amnuaypattanapon K, Limjindaporn C, Imsuwan I, Daorattanachai K, Dasanadeba I, et al. Predictors of in‐hospital cardiac arrest within 24 h after emergency department triage: a case-control study in urban Thailand. Emergency Medicine Australasia. 2019; 31: 843–850.

[23] Lee SB, Kim DH, Kim T, Kang C, Lee SH, Jeong JH, et al. Emergency Department Triage Early Warning Score (TREWS) predicts in-hospital mortality in the emergency department. American Journal of Emergency Medicine. 2020; 38: 203–210.

[24] Awan SE, Sohel F, Sanfilippo FM, Bennamoun M, Dwivedi G. Machine learning in heart failure: ready for prime time. Current Opinion in Cardiology. 2018; 33: 190–195.

[25] Delahanty RJ, Alvarez J, Flynn LM, Sherwin RL, Jones SS. Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis. Annals of Emergency Medicine. 2019; 73: 334–344.

[26] Than MP, Pickering JW, Sandoval Y, Shah ASV, Tsanas A, Apple FS, et al. Machine Learning to Predict the Likelihood of Acute Myocardial Infarction. Circulation. 2019; 140: 899–909

[27] Wu C, Hsu W, Islam MM, Poly TN, Yang H, Nguyen PA, et al. An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain. Computer Methods and Programs in Biomedicine. 2019; 173: 109–117.

[28] Lee S, Mohr NM, Street WN, Nadkarni P. Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview. The Western Journal of Emergency Medicine. 2019; 20: 219–227.

[29] Ong MEH, Lee Ng CH, Goh K, Liu N, Koh ZX, Shahidah N, et al. Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score. Critical Care. 2012; 16: R108.

[30] Liu N, Koh ZX, Goh J, Lin Z, Haaland B, Ting BP, et al. Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection. BMC Medical Informatics and Decision Making. 2014; 14: 75.

[31] Liu T, Lin Z, Ong MEH, Koh ZX, Pek PP, Yeo YK, et al. Manifold ranking based scoring system with its application to cardiac arrest prediction: a retrospective study in emergency department patients. Computers in Biology and Medicine. 2015; 67: 74–82.

[32] Hong S, Lee S, Lee J, Cha WC, Kim K. Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study. JMIR Medical Informatics. 2020; 8: e15932.

[33] Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. The New England Journal of Medicine. 2019; 380: 1347–1358.

[34] Liao SF, Chen PJ, Chaou CH, Lee CH. Top-cited publications on point-of-care ultrasound: The evolution of research trends. American Journal of Emergency Medicine. 2018; 36: 1429–1438.

[35] Mikati N, Callaway CW, Coppler PJ, Elmer J. Data-driven classification of arrest location for emergency department cardiac arrests. Resuscitation. 2020; 154: 26–30.

[36] Ravindran R, Kwok CS, Wong CW, Siller-Matula JM, Parwani P, Velagapudi P, et al. Cardiac arrest and related mortality in emergency departments in the United States: Analysis of the nationwide emergency department sample. Resuscitation. 2020; 157: 166–173.

[37] April MD, Arana A, Reynolds JC, Carlson JN, Davis WT, Schauer SG, et al. Peri-intubation cardiac arrest in the Emergency Department: a National Emergency Airway Registry (NEAR) study. Resuscitation. 2021; 162: 403–411.

[38] Kim WY, Kwak MK, Ko BS, Yoon JC, Sohn CH, Lim KS, et al. Factors associated with the occurrence of cardiac arrest after emergency tracheal intubation in the emergency department. PLoS ONE. 2014; 9: e112779.

[39] Hoehn EF, Dean P, Lautz AJ, Frey M, Cabrera-Thurman MK, Geis GL, et al. Peri-Intubation Cardiac Arrest in the Pediatric Emergency Department: A Novel System of Care. Pediatric Quality & Safety. 2020; 5: e365.

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