1. Computerized Analysis of the Ventricular Fibrillation Waveform Allows Identification of Myocardial Infarction: A Proof-of-Concept Study for Smart Defibrillator Applications in Cardiac Arrest
- Author
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Jos Thannhauser, Dennis J. Rebergen, Sjoerd W. Westra, Niels van Royen, Judith L. Bonnes, Joep L.R.M. Smeets, Marc A. Brouwer, and Joris Nas
- Subjects
Male ,medicine.medical_specialty ,Defibrillation ,medicine.medical_treatment ,Vascular damage Radboud Institute for Health Sciences [Radboudumc 16] ,Electric Countershock ,Myocardial Infarction ,cardiac arrest ,Diagnostic Testing ,030204 cardiovascular system & hematology ,Proof of Concept Study ,Resuscitation Science ,03 medical and health sciences ,Electrocardiography ,0302 clinical medicine ,All institutes and research themes of the Radboud University Medical Center ,Internal medicine ,medicine ,Image Processing, Computer-Assisted ,Waveform ,Humans ,Myocardial infarction ,Registries ,Aged ,Netherlands ,Original Research ,Cardiopulmonary Resuscitation and Emergency Cardiac Care ,business.industry ,Computerized analysis ,amplitude spectrum area ,Vascular damage Radboud Institute for Molecular Life Sciences [Radboudumc 16] ,030208 emergency & critical care medicine ,Middle Aged ,medicine.disease ,Implantable cardioverter-defibrillator ,Prognosis ,ventricular fibrillation ,Frequency spectrum ,Cardiopulmonary Resuscitation ,Defibrillators, Implantable ,Heart Arrest ,Electrophysiology ,machine learning ,Waveform analysis ,Ventricular fibrillation ,Cardiology ,Female ,Cardiology and Cardiovascular Medicine ,business - Abstract
Background In cardiac arrest, computerized analysis of the ventricular fibrillation (VF) waveform provides prognostic information, while its diagnostic potential is subject of study. Animal studies suggest that VF morphology is affected by prior myocardial infarction (MI), and even more by acute MI. This experimental in‐human study reports on the discriminative value of VF waveform analysis to identify a prior MI. Outcomes may provide support for in‐field studies on acute MI. Methods and Results We conducted a prospective registry of implantable cardioverter defibrillator recipients with defibrillation testing (2010–2014). From 12‐lead surface ECG VF recordings, we calculated 10 VF waveform characteristics. First, we studied detection of prior MI with lead II, using one key VF characteristic (amplitude spectrum area [AMSA]). Subsequently, we constructed diagnostic machine learning models: model A, lead II, all VF characteristics; model B, 12‐lead, AMSA only; and model C, 12‐lead, all VF characteristics. Prior MI was present in 58% (119/206) of patients. The approach using the AMSA of lead II demonstrated a C‐statistic of 0.61 (95% CI, 0.54–0.68). Model A performance was not significantly better: 0.66 (95% CI, 0.59–0.73), P =0.09 versus AMSA lead II. Model B yielded a higher C‐statistic: 0.75 (95% CI, 0.68–0.81), P P =0.66 versus model B. Conclusions This proof‐of‐concept study provides the first in‐human evidence that MI detection seems feasible using VF waveform analysis. Information from multiple ECG leads rather than from multiple VF characteristics may improve diagnostic accuracy. These results require additional experimental studies and may serve as pilot data for in‐field smart defibrillator studies, to try and identify acute MI in the earliest stages of cardiac arrest.
- Published
- 2020