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.

data. We compared the performance of our method with the methods of Berntson and Clifford on the same data. We identified 257,458 R-peak detections, of which 235,644 (91.5%) were true detections and 21,814 (8.5%) arose from artifacts. Our method showed superior performance for detecting artifacts with sensitivity 100%, specificity 99%, precision 99%, positive likelihood ratio of 100 and negative likelihood ratio <0.001 compared to Berntson's and Clifford's method with a sensitivity, specificity, precision and positive and negative likelihood ratio of 99%, 78%, 82%, 4.5, 0.013 for Berntson's method and 55%, 98%, 96%, 27.5, 0.460 for Clifford's method, respectively. A novel algorithm using a patient-independent threshold derived from the distribution of adRRI values in ICU ECG data identifies artifacts accurately, and outperforms two other methods in common use. Furthermore, the threshold was calculated based on real data from critically ill patients and the algorithm is easy to implement.

Introduction: Currently, “smart” automated external defibrillators (AEDs) are under investigation that use the ventricular fibrillation (VF) waveform to guide shock timing. Recent publications show that AED electrode placement varies greatly in clinical practice, which may affect the registered values of VF waveform characteristics. Therefore, we investigated whether recording direction influences the observed value of the most commonly assessed VF-waveform characteristic: the amplitude spectrum area (AMSA). Methods: Prospective cohort of patients who underwent defibrillation testing after implantation of an implantable cardioverter defibrillator (ICD) (2010-2013). Four-second segments of induced VF were selected prior to the ICD-shock. AMSA was calculated for bipolar and unipolar ECG-leads (I, II, V3 and V6) and reported as means ± standard deviations. Pairwise comparisons between leads were performed using paired samples t-tests. Results: We studied 180 patients, mean age 63 ± 13 years and 134/180 (74%) males. The mean AMSA was the highest in lead V3 (20.1 ± 9.3 mVHz) and the lowest in lead I (7.9 ± 3.5 mVHz). Significant differences in AMSA between ECG-leads were found between lead I and lead II, between lead I and leads V3,V6 (all p<.001) and between lead V3 and leads II,V6 (both p<.001) (Figure). No other differences were found. Conclusion: VF-waveform characteristics vary greatly with the recording direction of ECG electrodes. In an era where “smart” defibrillators are under investigation using specific cut-off values of the AMSA, these findings are of great importance. Future studies on VF-guided shock delivery should use standardized paddle placement.

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