Characteristics of ventricular fibrillation in relation to cardiac aetiology and shock success: A waveform analysis study in ICD-patients

Electrocardiographic recording direction impacts ventricular fibrillation waveform measurements: A potential pitfall for VF-waveform guided defibrillation protocols

Aim

In cardiac arrest, ventricular fibrillation (VF) waveform analysis has identified the amplitude spectrum area (AMSA) as a key predictor of defibrillation success and favorable neurologic survival. New resuscitation protocols are under investigation, where prompt defibrillation is restricted to cases with a high AMSA. Appreciating the variability of in-field pad placement, we aimed to assess the impact of recording direction on AMSA-values, and the inherent defibrillation advice.

Methods

Prospective VF-waveform study on 12-lead surface electrocardiograms (ECGs) obtained during defibrillation testing in ICD-recipients (2010–2017). AMSA-values (mVHz) of simultaneous VF-recordings were calculated and compared between all limb leads, with lead II as reference (proxy for in-field pad position). AMSA-differences between leads I and II were quantified using Bland-Altman analysis. Moreover, we investigated differences between these adjacent leads regarding classification into high (≥15.5), intermediate (6.5–15.5) or low (≤6.5) AMSA-values.

Results

In this cohort (n = 243), AMSA-values in lead II (10.2 ± 4.8) differed significantly from the other limb leads (I: 8.0 ± 3.4; III: 12.9 ± 5.6, both p < 0.001). The AMSA-value in lead I was, on average, 2.24 ± 4.3 lower than in lead II. Of the subjects with high AMSA-values in lead II, only 15% were classified as high if based on assessments of lead I. For intermediate and low AMSA-values, concordances were 66% and 72% respectively.

Conclusions

ECG-recording direction markedly affects the result of VF-waveform analysis, with 20–30% lower AMSA-values in lead I than in lead II. Our data suggest that electrode positioning may significantly impact shock guidance by ‘smart defibrillators’, especially affecting the advice for prompt defibrillation.

Computerized Analysis of the Ventricular Fibrillation Waveform Allows Identification of Myocardial Infarction: A Proof-of-Concept Study for Smart Defibrillator Applications in Cardiac Arrest

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.