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In Journal of the American Heart Association ; h5-index 70.0

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<0.001 versus AMSA lead II. Model C did not improve this further: 0.74 (95% CI, 0.67-0.80), 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.

Thannhauser Jos, Nas Joris, Rebergen Dennis J, Westra Sjoerd W, Smeets Joep L R M, Van Royen Niels, Bonnes Judith L, Brouwer Marc A


amplitude spectrum area, cardiac arrest, machine learning, myocardial infarction, ventricular fibrillation