Literature DB >> 30836170

Value of capnography to predict defibrillation success in out-of-hospital cardiac arrest.

Beatriz Chicote1, Elisabete Aramendi2, Unai Irusta2, Pamela Owens3, Mohamud Daya4, Ahamed Idris3.   

Abstract

BACKGROUND AND AIM: Unsuccessful defibrillation shocks adversely affect survival from out-of-hospital cardiac arrest (OHCA). Ventricular fibrillation (VF) waveform analysis is the tool-of-choice for the non-invasive prediction of shock success, but surrogate markers of perfusion like end-tidal CO2 (EtCO2) could improve the prediction. The aim of this study was to evaluate EtCO2 as predictor of shock success, both individually and in combination with VF-waveform analysis.
MATERIALS AND METHODS: In total 514 shocks from 214 OHCA patients (75 first shocks) were analysed. For each shock three predictors of defibrillation success were automatically calculated from the device files: two VF-waveform features, amplitude spectrum area (AMSA) and fuzzy entropy (FuzzyEn), and the median EtCO2 (MEtCO2) in the minute before the shock. Sensitivity, specificity, receiver operating characteristic (ROC) curves and area under the curve (AUC) were calculated, for each predictor individually and for the combination of MEtCO2 and VF-waveform predictors. Separate analyses were done for first shocks and all shocks.
RESULTS: MEtCO2 in first shocks was significantly higher for successful than for unsuccessful shocks (31mmHg/25mmHg, p<0.05), but differences were not significant for all shocks (32mmHg/29mmHg, p>0.05). MEtCO2 predicted shock success with an AUC of 0.66 for first shocks, but was not a predictor for all shocks (AUC 0.54). AMSA and FuzzyEn presented AUCs of 0.76 and 0.77 for first shocks, and 0.75 and 0.75 for all shocks. For first shocks, adding MEtCO2 improved the AUC of AMSA and FuzzyEn to 0.79 and 0.83, respectively.
CONCLUSIONS: MEtCO2 predicted defibrillation success only for first shocks. Adding MEtCO2 to VF-waveform analysis in first shocks improved prediction of shock success. VF-waveform features and MEtCO2 were automatically calculated from the device files, so these methods could be introduced in current defibrillators adding only new software.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Amplitude spectrum area (AMSA); End-tidal CO(2) (EtCO(2)); Fuzzy entropy; Out-of-hospital cardiac arrest; Shock outcome prediction; Ventricular fibrillation

Mesh:

Substances:

Year:  2019        PMID: 30836170      PMCID: PMC6504568          DOI: 10.1016/j.resuscitation.2019.02.028

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


  41 in total

Review 1.  Mechanisms of defibrillation.

Authors:  Derek J Dosdall; Vladimir G Fast; Raymond E Ideker
Journal:  Annu Rev Biomed Eng       Date:  2010-08-15       Impact factor: 9.590

2.  Definition of successful defibrillation.

Authors:  Rudolph W Koster; Robert G Walker; Anouk P van Alem
Journal:  Crit Care Med       Date:  2006-12       Impact factor: 7.598

3.  Prediction of successful defibrillation in human victims of out-of-hospital cardiac arrest: a retrospective electrocardiographic analysis.

Authors:  G Ristagno; A Gullo; G Berlot; U Lucangelo; E Geheb; J Bisera
Journal:  Anaesth Intensive Care       Date:  2008-01       Impact factor: 1.669

4.  Optimizing timing of ventricular defibrillation.

Authors:  A Marn-Pernat; M H Weil; W Tang; A Pernat; J Bisera
Journal:  Crit Care Med       Date:  2001-12       Impact factor: 7.598

5.  Abrupt rise of end tidal carbon dioxide level was a specific but non-sensitive marker of return of spontaneous circulation in patient with out-of-hospital cardiac arrest.

Authors:  Chun Tat Lui; Kin Ming Poon; Kwok Leung Tsui
Journal:  Resuscitation       Date:  2016-05-06       Impact factor: 5.262

6.  Predicting the success of defibrillation by electrocardiographic analysis.

Authors:  Heitor P Povoas; Max Harry Weil; Wanchun Tang; Joe Bisera; Kada Klouche; Ann Barbatsis
Journal:  Resuscitation       Date:  2002-04       Impact factor: 5.262

7.  End tidal carbon dioxide as an haemodynamic determinant of cardiopulmonary resuscitation in the rat.

Authors:  M von Planta; I von Planta; M H Weil; S Bruno; J Bisera; E C Rackow
Journal:  Cardiovasc Res       Date:  1989-04       Impact factor: 10.787

8.  Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning.

Authors:  Sharad Shandilya; Kevin Ward; Michael Kurz; Kayvan Najarian
Journal:  BMC Med Inform Decis Mak       Date:  2012-10-15       Impact factor: 2.796

9.  Detrended fluctuation analysis predicts successful defibrillation for out-of-hospital ventricular fibrillation cardiac arrest.

Authors:  Lian-Yu Lin; Men-Tzung Lo; Patrick Chow-In Ko; Chen Lin; Wen-Chu Chiang; Yen-Bin Liu; Kun Hu; Jiunn-Lee Lin; Wen-Jone Chen; Matthew Huei-Ming Ma
Journal:  Resuscitation       Date:  2010-01-13       Impact factor: 5.262

10.  End-tidal carbon dioxide monitoring may be associated with a higher possibility of return of spontaneous circulation during out-of-hospital cardiac arrest: a population-based study.

Authors:  Jiun-Jia Chen; Yi-Kung Lee; Sheng-Wen Hou; Ming-Yuan Huang; Chen-Yang Hsu; Yung-Cheng Su
Journal:  Scand J Trauma Resusc Emerg Med       Date:  2015-11-24       Impact factor: 2.953

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  1 in total

1.  Continuous flow insufflation of oxygen compared with manual ventilation during out-of-hospital cardiac arrest: A survey of the paramedics.

Authors:  Mathieu Groulx; Alexandra Nadeau; Marcel Émond; Jessica Harrisson; Pierre-Gilles Blanchard; Douglas Eramian; Eric Mercier
Journal:  SAGE Open Med       Date:  2021-06-30
  1 in total

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