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

29.11.2018 | Original Research

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

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

Erschienen in: Journal of Clinical Monitoring and Computing | Ausgabe 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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Metadaten
Titel
Identification of return of spontaneous circulation during cardiopulmonary resuscitation via pulse oximetry in a porcine animal cardiac arrest model
verfasst von
Chen Li
Jun Xu
Fei Han
Joseph Walline
Liangliang Zheng
Yangyang Fu
Huadong Zhu
Yanfen Chai
Xuezhong Yu
Publikationsdatum
29.11.2018
Verlag
Springer Netherlands
Erschienen in
Journal of Clinical Monitoring and Computing / Ausgabe 5/2019
Print ISSN: 1387-1307
Elektronische ISSN: 1573-2614
DOI
https://doi.org/10.1007/s10877-018-0230-4

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