Ventricular fibrillation waveform characteristics differ according to the presence of a previous myocardial infarction: A surface ECG study in ICD-patients.
BACKGROUND
Characteristics of the ventricular fibrillation (VF) waveform reflect arrest duration and have been incorporated in studies on algorithms to guide resuscitative interventions. Findings in animals indicate that VF characteristics are also affected by the presence of a previous myocardial infarction (MI). As studies in humans are scarce, we assessed the impact of a previous MI on VF characteristics in ICD-patients.


METHODS
Prospective cohort of ICD-patients (n=190) with defibrillation testing at the Radboudumc (2010-2013). VF characteristics of the 12-lead surface ECG were compared between three groups: patients without a history of MI (n=88), with a previous anterior (n=47) and a previous inferior MI (n=55).


RESULTS
As compared to each of the other groups, the mean amplitude and amplitude spectrum area were lower, for an anterior MI in lead V3 and for an inferior MI in leads II and aVF. Across the three groups, the bandwidth was broader in the leads corresponding with the infarct localisation. In contrast, the dominant and median frequencies only differed between previous anterior MI and no history of MI, being lower in the former.


CONCLUSIONS
The VF waveform is affected by the presence of a previous MI. Amplitude-related measures were lower and VF was less organised in the ECG-lead(s) adjacent to the area of infarction. Although VF characteristics of the surface ECG have so far primarily been considered a proxy for arrest duration and metabolic state, our findings question this paradigm and may provide additional insights into the future potential of VF-guided resuscitative interventions.
Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: judithbonnes@gmail.com.
Judith L
JL
Bonnes
Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands.
Jos
J
Thannhauser
Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands.
Mathilde C
MC
Hermans
Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands.
Sjoerd W
SW
Westra
Department of Cognitive Neuroscience, Donders Center for Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands.
Thom F
TF
Oostendorp
Faculty of Electrical Engineering, Mathematics and Computer Sciences, Department of Applied Mathematics, University of Twente, Enschede, The Netherlands.
Gjerrit
G
Meinsma
Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands.
Menko Jan
MJ
de Boer
Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands.
Marc A
MA
Brouwer
Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands.
Joep L R M
JL
Smeets
D000368:Aged; D000465:Algorithms; D017147:Defibrillators, Implantable; D004576:Electromyography / Q000379:methods*; D005260:Female; D005500:Follow-Up Studies; D006323:Heart Arrest / Q000453:epidemiology / Q000209:etiology* / Q000628:therapy; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000150:complications* / Q000503:physiopathology; D009426:Netherlands / Q000453:epidemiology; D011379:Prognosis; D011446:Prospective Studies; D015996:Survival Rate / Q000639:trends; D014693:Ventricular Fibrillation / Q000150:complications / Q000503:physiopathology* / Q000628:therapy
Electrocardiography; Fourier analysis; Implantable cardioverter defibrillator; Myocardial infarction; Ventricular fibrillation