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A Simplified and Structured Teaching Tool for the Evaluation and Management of Pulseless Electrical Activity

Open access
Littmann, Laszlo, Bustin, Devin J., Haley, Michael W.·2014·Medical Principles and Practice

Abstract

Cardiac arrest victims who present with pulseless electrical activity (PEA) usually have a grave prognosis. Several conditions, however, have cause-specific treatments which, if applied immediately, can lead to quick and sustained recovery. Current teaching focuses on recollection of numerous conditions that start with the letters H or T as potential causes of PEA. This teaching method is too complex, difficult to recall during resuscitation, and does not provide guidance to the most effective initial interventions. This review proposes a structured algorithm that is based on the differentiation of the PEA rhythm into narrow- or wide-complex subcategories, which simplifies the working differential and initial treatment approach. This, in conjunction with bedside ultrasound, can quickly point towards the most likely cause of PEA and thus guide resuscitation.

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Metadata

Authors
Littmann, L., Bustin, D. J., & Haley, M. W.
Venue
Medical Principles and Practice
Publisher
S. Karger AG
Published
2013-08-13
Volume
23
Issue
1
Pages
1-6
ISSN
  • 1011-7571 (ISSN-L)
  • 1423-0151
OpenAlex
W2051212900
Cited by
738
References
0
FWCI
68.00
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: A Simplified and Structured Teaching Tool for the Evaluation and Management of Pulseless Electrical Activity. https://app.cochat.ai/papers/10.1159/000354195
The paper
Littmann, L., Bustin, D. J., & Haley, M. W. (2014). A Simplified and Structured Teaching Tool for the Evaluation and Management of Pulseless Electrical Activity. Medical Principles and Practice. https://doi.org/10.1159/000354195

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.