Literature DB >> 18326131

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

G Ristagno1, A Gullo, G Berlot, U Lucangelo, E Geheb, J Bisera.   

Abstract

In the present study we sought to examine the efficacy of an electrocardiographic parameter, 'amplitude spectrum area' (AMSA), to predict the likelihood that any one electrical shock would restore a perfusing rhythm during cardiopulmonary resuscitation in human victims of out-of-hospital cardiac arrest. AMSA analysis is not invalidated by artefacts produced by chest compression and thus it can be performed during CPR, avoiding detrimental interruptions of chest compression and ventilation. We hypothesised that a threshold value of AMSA could be identified as an indicator of successful defibrillation in human victims of cardiac arrest. Analysis was performed on a database of electrocardiographic records, representing lead 2 equivalent recordings from automated external defibrillators including 210 defibrillation attempts from 90 victims of out-of-hospital cardiac arrest. A 4.1 second interval of ventricular fibrillation or ventricular tachycardia, recorded immediately preceding the delivery of the shock, was analysed using the AMSA algorithm. AMSA represents a numerical value based on the sum of the magnitude of the weighted frequency spectrum between two and 48 Hz. AMSA values were significantly greater in successful defibrillation (restoration of a perfusing rhythm), compared to unsuccessful defibrillation (P < 0.0001). An AMSA value of 12 mV-Hz was able to predict the success of each defibrillation attempt with a sensitivity of 0.91 and a specificity of 0.97. In conclusion, AMSA analysis represents a clinically applicable method, which provides a real-time prediction of the success of defibrillation attempts. AMSA may minimise the delivery of futile and detrimental electrical shocks, reducing thereby post-resuscitation myocardial injury.

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Year:  2008        PMID: 18326131     DOI: 10.1177/0310057X0803600108

Source DB:  PubMed          Journal:  Anaesth Intensive Care        ISSN: 0310-057X            Impact factor:   1.669


  6 in total

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

Authors:  Beatriz Chicote; Elisabete Aramendi; Unai Irusta; Pamela Owens; Mohamud Daya; Ahamed Idris
Journal:  Resuscitation       Date:  2019-03-02       Impact factor: 5.262

2.  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

3.  Repeated epinephrine doses during prolonged cardiopulmonary resuscitation have limited effects on myocardial blood flow: a randomized porcine study.

Authors:  Henrik Wagner; Michael Götberg; Bjarne Madsen Hardig; Malin Rundgren; Jonas Carlson; Matthias Götberg; David Zughaft; David Erlinge; Göran K Olivecrona
Journal:  BMC Cardiovasc Disord       Date:  2014-12-20       Impact factor: 2.298

4.  Predict Defibrillation Outcome Using Stepping Increment of Poincare Plot for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest.

Authors:  Yushun Gong; Yubao Lu; Lei Zhang; Hehua Zhang; Yongqin Li
Journal:  Biomed Res Int       Date:  2015-09-02       Impact factor: 3.411

5.  Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes.

Authors:  Sharad Shandilya; Michael C Kurz; Kevin R Ward; Kayvan Najarian
Journal:  PLoS One       Date:  2016-01-07       Impact factor: 3.240

6.  Correlation of end tidal carbon dioxide, amplitude spectrum area, and coronary perfusion pressure in a porcine model of cardiac arrest.

Authors:  Nicolas Segal; Anja K Metzger; Johanna C Moore; Laura India; Michael C Lick; Paul S Berger; Wanchun Tang; David G Benditt; Keith G Lurie
Journal:  Physiol Rep       Date:  2017-09
  6 in total

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