Literature DB >> 32673732

Data-driven classification of arrest location for emergency department cardiac arrests.

Nancy Mikati1, Clifton W Callaway1, Patrick J Coppler1, Jonathan Elmer2.   

Abstract

BACKGROUND: Resuscitation research is inconsistent in how emergency department (ED) arrests are classified. We tested whether clinical features of ED arrests more closely resembled out-of-hospital cardiac arrest (OHCA) or in-hospital cardiac arrest (IHCA).
METHODS: We performed a retrospective study including all patients resuscitated from cardiac arrest at a single academic medical center from January 2010 to December 2019. We abstracted clinical information from our prospective registry. We used unsupervised learning (k-prototypes) to identify clusters within the OHCA and IHCA cohorts. We determined the number of subgroups using scree plots. We assigned individual ED arrest patients the nearest OHCA or IHCA cluster based on the shortest Gower distance from that patient to the nearest cluster center. In our secondary analysis, we determined the optimal number of clusters in each of the 3 arrest cohorts, and then calculated the mean Gower distances with the standard deviation (SD) between cluster centers (ED-IHCA, ED-OHCA, IHCA-OHCA).
RESULTS: We included 2723 patients: 372 (14%) ED arrests, 1709 (63%) OHCA, and 642 (23%) IHCA. We identified 3 clusters of OHCA patients, and 4 clusters of IHCA patients. Of ED arrest cases, 292 (78%) most closely resembled an IHCA cluster and 80 (22%) most closely resembled an OHCA cluster. Mean (SD) Gower distance between ED arrest and IHCA centers was 0.33 (0.2). Mean Gower distances between ED arrest-OHCA centers and between IHCA-OHCA centers were 0.41 (0.11).
CONCLUSION: Across multiple aggregated measures, ED arrests resemble IHCA more than OHCA.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Cardiac arrest; Clustering; Unsupervised learning

Mesh:

Year:  2020        PMID: 32673732      PMCID: PMC7484116          DOI: 10.1016/j.resuscitation.2020.07.004

Source DB:  PubMed          Journal:  Resuscitation        ISSN: 0300-9572            Impact factor:   5.262


  13 in total

1.  The Scree Test For The Number Of Factors.

Authors:  R B Cattell
Journal:  Multivariate Behav Res       Date:  1966-04-01       Impact factor: 5.923

2.  An early, novel illness severity score to predict outcome after cardiac arrest.

Authors:  Jon C Rittenberger; Samuel A Tisherman; Margo B Holm; Francis X Guyette; Clifton W Callaway
Journal:  Resuscitation       Date:  2011-07-05       Impact factor: 5.262

3.  Demographic, social, economic and geographic factors associated with long-term outcomes in a cohort of cardiac arrest survivors.

Authors:  Patrick J Coppler; Jonathan Elmer; Jon C Rittenberger; Clifton W Callaway; David J Wallace
Journal:  Resuscitation       Date:  2018-04-26       Impact factor: 5.262

4.  Validation of the Pittsburgh Cardiac Arrest Category illness severity score.

Authors:  Patrick J Coppler; Jonathan Elmer; Luis Calderon; Alexa Sabedra; Ankur A Doshi; Clifton W Callaway; Jon C Rittenberger; Cameron Dezfulian
Journal:  Resuscitation       Date:  2015-01-28       Impact factor: 5.262

5.  Strategies for improving survival after in-hospital cardiac arrest in the United States: 2013 consensus recommendations: a consensus statement from the American Heart Association.

Authors:  Laurie J Morrison; Robert W Neumar; Janice L Zimmerman; Mark S Link; L Kristin Newby; Paul W McMullan; Terry Vanden Hoek; Colleen C Halverson; Lynn Doering; Mary Ann Peberdy; Dana P Edelson
Journal:  Circulation       Date:  2013-03-11       Impact factor: 29.690

6.  Arrest etiology among patients resuscitated from cardiac arrest.

Authors:  Niel Chen; Clifton W Callaway; Francis X Guyette; Jon C Rittenberger; Ankur A Doshi; Cameron Dezfulian; Jonathan Elmer
Journal:  Resuscitation       Date:  2018-06-22       Impact factor: 5.262

7.  Cardiac arrests within the emergency department: an Utstein style report, causation and survival factors.

Authors:  Sing C Tan; Benjamin Sieu-Hon Leong
Journal:  Eur J Emerg Med       Date:  2018-02       Impact factor: 2.799

Review 8.  In-hospital cardiac arrest: are we overlooking a key distinction?

Authors:  Ari Moskowitz; Mathias J Holmberg; Michael W Donnino; Katherine M Berg
Journal:  Curr Opin Crit Care       Date:  2018-06       Impact factor: 3.687

9.  Association between chest compression rates and clinical outcomes following in-hospital cardiac arrest at an academic tertiary hospital.

Authors:  J Hope Kilgannon; Michael Kirchhoff; Lisa Pierce; Nicholas Aunchman; Stephen Trzeciak; Brian W Roberts
Journal:  Resuscitation       Date:  2016-09-22       Impact factor: 5.262

10.  Location of In-Hospital Cardiac Arrest in the United States-Variability in Event Rate and Outcomes.

Authors:  Sarah M Perman; Emily Stanton; Jasmeet Soar; Robert A Berg; Michael W Donnino; Mark E Mikkelsen; Dana P Edelson; Matthew M Churpek; Lin Yang; Raina M Merchant
Journal:  J Am Heart Assoc       Date:  2016-09-29       Impact factor: 5.501

View more
  2 in total

1.  Bayesian Outcome Prediction After Resuscitation From Cardiac Arrest.

Authors:  Jonathan Elmer; Patrick J Coppler; Bobby L Jones; Daniel S Nagin; Clifton W Callaway
Journal:  Neurology       Date:  2022-07-05       Impact factor: 11.800

2.  Development and Validation of a Novel Triage Tool for Predicting Cardiac Arrest in the Emergency Department.

Authors:  Chu-Lin Tsai; Tsung-Chien Lu; Cheng-Chung Fang; Chih-Hung Wang; Jia-You Lin; Wen-Jone Chen; Chien-Hua Huang
Journal:  West J Emerg Med       Date:  2022-02-23
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.