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Published in: Journal of Clinical Monitoring and Computing 5/2019

01-10-2019 | Original Research

Identification of return of spontaneous circulation during cardiopulmonary resuscitation via pulse oximetry in a porcine animal cardiac arrest model

Authors: Chen Li, Jun Xu, Fei Han, Joseph Walline, Liangliang Zheng, Yangyang Fu, Huadong Zhu, Yanfen Chai, Xuezhong Yu

Published in: Journal of Clinical Monitoring and Computing | Issue 5/2019

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Abstract

In this prospective study we investigated whether the pulse oximetry plethysmographic waveform (POP) could be used to identify return of spontaneous circulation (ROSC) during cardio-pulmonary resuscitation (CPR). Tweleve pigs (28 ± 2 kg) were randomly assigned to two groups: Group I (non-arrested with compressions) (n = 6); Group II (arrested with CPR and defibrillation) (n = 6). Hemodynamic parameters and POP were collected and analyzed. POP was analyzed using both a time domain method and a frequency domain method. In Group I, when compressions were carried out on subjects with a spontaneous circulation, a hybrid fluctuation or “envelope” phenomenon appeared in the time domain method and a “double” or “fusion” peak appeared in the frequency domain method. In Group II, after the period of ventricular fibrillation was induced, the POP waveform disappeared. With compressions, POP showed a regular compression wave. After defibrillation, ROSC, and continued compressions, a hybrid fluctuation or “envelope” phenomenon appeared in the time domain method and a “double” or “fusion” peak appeared in the frequency domain method, similar to Group I. Analysis of POP using the time and frequency domain methods could be used to identify ROSC during CPR.
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Metadata
Title
Identification of return of spontaneous circulation during cardiopulmonary resuscitation via pulse oximetry in a porcine animal cardiac arrest model
Authors
Chen Li
Jun Xu
Fei Han
Joseph Walline
Liangliang Zheng
Yangyang Fu
Huadong Zhu
Yanfen Chai
Xuezhong Yu
Publication date
01-10-2019
Publisher
Springer Netherlands
Published in
Journal of Clinical Monitoring and Computing / Issue 5/2019
Print ISSN: 1387-1307
Electronic ISSN: 1573-2614
DOI
https://doi.org/10.1007/s10877-018-0230-4

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