Ventricular Fibrillation Waveform Research Articles

In-hospital and long term neurological outcome prognostication using an algorithm based on the analysis of ventricular fibrillation from the EKG of patients with sudden cardiac death (AWAKE)

Abstract Background Sudden cardiac death (SCD) is a leading cause of mortality and carries a dismal neurological outcome (NO) among patients admitted alive to acute cardiac care units (ACCU). A big share of SCD are found in ventricular fibrillation (VF). VF waveform is dynamic and it can be characterized by spectral analysis at the very time of ACCU admission. Purpose To validate in a multicenter cohort an algorithm based on spectral variables of VF waveform to early predict neurological outcome at hospital discharge and follow-up, and to compare its performance with currently established prognostic tools. Methods Multicenter study in which 5 hospitals recruited patients, resuscitated from SCD due to VF whose EKG was available, and admitted to ACCU with expertise in SCD. EKG tracings were scanned and analyzed with a custom MATLAB script delivering spectral variables to be entered in an algorithm to predict NO. NO was assessed by Cerebral Performance Category (CPC) during admission and after a minimum 6-month follow-up period. The primary endpoint was favorable neurological outcome (FNO, defined as CPC 1 and 2) during hospital admission, and the secondary endpoint was FNO at long-term follow-up. Statistical data were analyzed with SPSS and EPIDAT. Results We included 168 patients out of 678 screened in participant hospitals among admissions from 2007 to 2022. The main cause of exclusion was a rhythm different from VF at the time of medical assistance (n=329, 48%) or VF EKG unavailable (n=151, 22%). Ninety-five patients (57%) were admitted in comatose status (Glasgow <9). Baseline characteristics are depicted in Picture 1. FNO during admission was documented in 136 patients (81%). Twenty-nine patients (17%) died during hospitalization. A spectral-based model using VF tracings early predicted FNO with an area under the curve (AUC) value of 0.85 (CI95% 0.77-0.93). There was overlap with the predictive performance obtained with the peak value of neuron-specific enolase (NSE) (0.85; CI95% 0.74-0.95) (Picture 2, A). Other prognostic tools showed similar or lower diagnostic yield and results were obtained at a later time after admission (Picture 2, B,D). Long-term follow-up was obtained for 166 cases (2 foreign patients were impossible to retrieve). Patients discharged alive were followed for 17 (± standard deviation 21) months. FNO was present in 136 (82%). The spectral-based model predicted FNO with AUC 0.83 (CI95% 0.74-0.92), similar to NSE (AUC 0.87, CI95% 0.77-0.97) (Picture 2, C). Other tools performed in follow-up similar to during admission (Picture 2, D). CONCLUSION A model based on VF spectral analysis predicts neurological outcome after SCD with an accuracy similar to current validated tools, but with no requirement for drug withdrawal or waiting period in comatose patients. This model estimates outcomes at the earliest moment of hospital admission and its predictions remain valid in the long run.Baseline characteristicsDiagnostic yield of model vs other tools

Instantaneous amplitude: Association of ventricular fibrillation waveform measures at time of shock with outcome in out-of-hospital cardiac arrest.

Prompt defibrillation is key to successful resuscitation from ventricular fibrillation out-of-hospital cardiac arrest (VF-OHCA). Preliminary evidence suggests that the timing of shock relative to the amplitude of the VF ECG waveform may affect the likelihood of resuscitation. We investigated whether the VF waveform amplitude at the time of shock (instantaneous amplitude) predicts outcome independent of other validated waveform measures. We conducted a retrospective study of VF-OHCA patients ≥18 old. We evaluated three VF waveform measures for each shock: instantaneous amplitude at the time of shock, and maximum amplitude and amplitude spectrum area (AMSA) over a 3-s window preceding the shock. Linear mixed-effects modeling was used to determine whether instantaneous amplitude was associated with shock-specific return of organized rhythm (ROR) or return of spontaneous circulation (ROSC) independent of maximum amplitude or AMSA. The 566 eligible patients received 1513 shocks, resulting in ROR of 62.0% (938/1513) and ROSC of 22.3% (337/1513). In unadjusted regression, an interquartile increase in instantaneous amplitude was associated with ROR (Odds ratio [OR][95% confidence interval]=1.27 [1.11-1.45]) and ROSC (OR=1.27 [1.14-1.42]). However, instantaneous amplitude was not associated with ROR (OR=1.13 [0.97-1.30]) after accounting for maximum amplitude, nor with ROR (OR=1.00 [0.87-1.15]) or ROSC (OR=1.05 [0.93-1.18]) after accounting for AMSA. By contrast, AMSA and maximum amplitude remained independently associated with ROR and ROSC. We did not observe an independent association between instantaneous amplitude and shock-specific outcomes. Efforts to time shock to the maximal amplitude of the VF waveform are unlikely to affect resuscitation outcome.

Ventricular fibrillation waveform properties influenced by thoracic impedance guided chest compressions in a porcine model

Background and objectiveQuantitative measures extracted from ventricular fibrillation (VF) waveform reflect the metabolic state of the myocardium and are associated with survival outcome. The quality of delivered chest compressions during cardiopulmonary resuscitation are also linked with survival. The aim of this research is to explore the viability and effectiveness of a thoracic impedance (TI) based chest compression (CC) guidance system to control CC depth within individual subjects and influence VF waveform properties. MethodsThis porcine investigation includes an analysis of two protocols. CC were delivered in 2 min episodes at a constant rate of 110 CC min−1. Subject-specific CC depth was controlled using a TI-thresholding system where CC were performed according to the amplitude (ZRMS, 0.125 to 1.250 Ω) of a band-passed TI signal (ZCC). Protocol A was a retrospective analysis of a 12-porcine study to characterise the response of two VF waveform metrics: amplitude spectrum area (AMSA) and mean slope (MS), to varying CC quality. Protocol B was a prospective 12-porcine study to determine if changes in VF waveform metrics, due to CC quality, were associated with defibrillation outcome. ResultsProtocol A: A directly proportional relationship was observed between ZRMS and CC depth applied within each subject (r = 0.90; p <0.001). A positive relationship was observed between ZRMS and both AMSA (p <0.001) and MS (p <0.001), where greater TI thresholds were associated with greater waveform metrics. Protocol B: MS was associated with return of circulation following defibrillation (odds ratio = 2.657; p = 0.043). ConclusionTI-thresholding was an effective way to control CC depth within-subjects. Compressions applied according to higher TI thresholds evoked an increase in AMSA and MS. The response in MS due to deeper CC resulted in a greater incidence of ROSC compared to shallow chest compressions.

Age, sex, and survival following ventricular fibrillation cardiac arrest: A mechanistic evaluation of the ECG waveform

BackgroundStudies of outcome differences by sex in out-of-hospital cardiac arrest (OHCA) have produced mixed results that may depend on age, a potential surrogate for menopausal status. ObjectiveWe used quantitative measures of ventricular fibrillation (VF) waveforms – indicators of the myocardium’s physiology – to assess whether survival differences according to sex and age group may be mediated via a biologic mechanism. MethodsWe conducted a cohort study of VF-OHCA in a metropolitan EMS system. We used multivariable logistic regression to assess the association of survival to hospital discharge with sex and age group (<55, ≥55 years). We determined the proportion of outcome difference mediated by VF waveform measures: VitalityScore and amplitude spectrum area (AMSA). ResultsAmong 1526 VF-OHCA patients, the average age was 62 years, and 29% were female. Overall, younger women were more likely to survive than younger men (survival 67% vs 54%, p = 0.02), while survival among older women and older men did not differ (40% vs 44%, p = 0.3). Adjusting for Utstein characteristics, women <55 compared to men <55 had greater odds of survival to hospital discharge (OR = 1.93, 95% CI 1.23–3.09), an association not observed between the ≥55 groups. Waveform measures were more favorable among women and mediated some of the beneficial association between female sex and survival among those <55 years: 47% for VitalityScore and 25% for AMSA. ConclusionsWomen <55 years were more likely to survive than men <55 years following VF-OHCA. The biologic mechanism represented by VF waveform mediated some, though not all, of the outcome difference.

C6 VENTRICULAR FIBRILLATION SPECTRAL AREA (AMSA) AND LOW–ENERGY SHOCK SUCCESS PREDICTION IN PATIENTS WITH OUT–OF–HOSPITAL CARDIAC ARREST

Abstract Introduction In case of cardiac arrest due to ventricular fibrillation (VF) or pulseless ventricular tachycardia (pVT), the optimal energy level for defibrillation is that which achieves defibrillation and minimize the current–induced myocardial damage. Therefore, it would be reasonable to reduce the energy level as well as the number of shocks. ECG–based VF waveform analysis features such as amplitude spectral area (AMSA) have been recently introduced as predictors of shock success but their predictivity for shock success with low energy level is not known. We aimed to assess whether AMSA of VF is able to predict the efficacy of low energy level for defibrillation in out–of–hospital cardiac arrest (OHCA) patients. Methods All the OHCAs with at least one shockable rhythm occurred from January 2015 to December 2020 in the province of Pavia, Italy, were considered. AMSA values were calculated by retrospectively analyzing the data collected by the Corpuls 3 monitors/defibrillators and by using a 2–second–pre–shock ECG interval. Results Among 4619 OHCA, AMSA values and energy for defibrillation were documented in 791 shocks, of which 45% received a shock at low energy (&amp;lt;= 150J) and 55% at high energy (&amp;gt;150J). The rate of efficacy between the two groups did not differ significantly (44% vs 38%, p=0.102), however in patients efficaciously treated with low energy, AMSA was higher compared to those efficaciously treated with high energy [13.2 mV·Hz (12.5–14.2) vs 10.8 (10.1–11.5), p&amp;lt;0.001]. Moreover, AMSA was found to be different even when comparing ineffective shock at low energy with effective shock at high energy [(6.6 (4.6–10) vs 10.8 (8.1–13.8), p&amp;lt;0.001] and similar when comparing ineffective shock at low and at high energy [6.6 (4.6–10) vs 6.3 (4.5–8.7), p=0.21]. By dividing AMSA values into three tertiles the rate of shock success at low energy was found to be different: [T1 (0.7–6.2) 4.2%; T2 (6.2–10.8) 13%; T3 (10.8–63.2) 42%, Chi squared p&amp;lt;0.001 and p for trend &amp;lt;0.001]. After correction for age, sex, amiodarone use, call to shock time, AMSA values corresponding to the third and second tertile were associated with higher probability of shock success compared with the values in the lowest tertile [T3 OR 15 (95%CI 7–30), p&amp;lt; 0.001; T2 OR 3 (95%CI 1–7), p= 0.002]. Conclusion Amplitude spectral area of VF is a predictor of shock success at low energy. This could be useful to optimize the choice of energy limiting the current related myocardial injury.

1105 VENTRICULAR FIBRILLATION SPECTRAL AREA (AMSA) AND LOW-ENERGY SHOCK SUCCESS PREDICTION IN PATIENTS WITH OUT-OF-HOSPITAL CARDIAC ARREST

Abstract Introduction In case of cardiac arrest due to ventricular fibrillation (VF) or pulseless ventricular tachycardia (pVT), the optimal energy level for defibrillation is that which achieves defibrillation and minimize the current-induced myocardial damage. Therefore, it would be reasonable to reduce the energy level as well as the number of shocks. ECG-based VF waveform analysis features such as amplitude spectral area (AMSA) have been recently introduced as predictors of shock success but their predictivity for shock success with low energy level is not known. We aimed to assess whether AMSA of VF is able to predict the efficacy of low energy level for defibrillation in out-of-hospital cardiac arrest (OHCA) patients. Methods All the OHCAs with at least one shockable rhythm occurred from January 2015 to December 2020 in the province of Pavia, Italy, were considered. AMSA values were calculated by retrospectively analyzing the data collected by the Corpuls 3 monitors/defibrillators and by using a 2-second-pre-shock ECG interval. Results Among 4619 OHCA, AMSA values and energy for defibrillation were documented in 791 shocks, of which 45% received a shock at low energy (&amp;lt;= 150J) and 55% at high energy (&amp;gt;150J). The rate of efficacy between the two groups did not differ significantly (44% vs 38%, p=0.102), however in patients efficaciously treated with low energy, AMSA was higher compared to those efficaciously treated with high energy [13.2 mV·Hz (12.5-14.2) vs 10.8 (10.1-11.5), p&amp;lt;0.001]. Moreover, AMSA was found to be different even when comparing ineffective shock at low energy with effective shock at high energy [(6.6 (4.6-10) vs 10.8 (8.1-13.8), p&amp;lt;0.001] and similar when comparing ineffective shock at low and at high energy [6.6 (4.6-10) vs 6.3 (4.5-8.7), p=0.21]. By dividing AMSA values into three tertiles the rate of shock success at low energy was found to be different: [T1 (0.7-6.2) 4.2%; T2 (6.2-10.8) 13%; T3 (10.8-63.2) 42%, Chi squared p&amp;lt;0.001 and p for trend &amp;lt;0.001]. After correction for age, sex, amiodarone use, call to shock time, AMSA values corresponding to the third and second tertile were associated with higher probability of shock success compared with the values in the lowest tertile [T3 OR 15 (95%CI 7-30), p&amp;lt; 0.001; T2 OR 3 (95%CI 1-7), p= 0.002]. Conclusion Amplitude spectral area (AMSA) of VF is a predictor of shock success at low energy. This could be useful to optimize the choice of energy limiting the current related myocardial injury.

Abstract 15563: Analysis of the Ventricular Fibrillation Waveform Can Guide Resuscitation in Cardiac Arrest by Identifying the Precipitating Cause and Estimating the Underlying Hemodynamics

Background: Patients presenting with out of hospital cardiac arrest (OHCA) and prolonged (&gt;30 min) cardiopulmonary resuscitation (CPR) face nearly 100% mortality. In patients presenting with ventricular fibrillation (VF), coronary ischemia is thought to be the cause in up to 60%. As conventional CPR does not reverse a coronary ischemia, CPR often must be done until a patient can be brought to a cardiac catheterization laboratory (CCL) where the underlying cause (acute ischemia) can be treated. In a porcine model of cardiac arrest, we have previously demonstrated that machine learning (ML) targeting invasive hemodynamic monitoring, could significantly improve CPR quality. To advance the clinical relevance of the machine-controlled CPR methods, we developed ML algorithms that can diagnose coronary ischemia during VF, and infer the underlying hemodynamics while performing CPR. Methods: In a porcine model of cardiac arrest, VF was induced via high-grade coronary occlusion (ischemia) or electrical stimulation (dysrhythmia). The animals (n = 72) were left in VF for 3 minutes prior to intervention and the ECG waveform was collected. The VF signal was converted to the frequency domain using a fast-fourier transformation and an ensemble classifier was developed to detect underlying ischemia. CPR was then initiated and the VF waveform was continuously monitored along with the coronary perfusion pressure (CPP). We then developed a 20-layer convolutional neural network (CNN) to non-invasively infer the CPP from a 10-second VF waveform. Results: Animal studies were split into training and test sets. When using the developed ML technique to detect ischemia from the VF waveform, evaluation of the test set yielded an area under the curve (AUC) of 0.81 on a ROC. When we used the CNN to infer the underlying hemodynamics, we demonstrated a high correlation between measured and predicted CPP in the test set. The AUC for the ROC when predicting a CPP of &gt;15 mmHg was 0.85. Conclusion: Survival for patients with OHCA secondary to coronary ischemia will improve with rapid diagnosis and high quality CPR. Our results demonstrate the proof-of-concept that the VF waveform contains features that can guide resuscitation for cause identification and hemodynamic improvement.

Abstract 258: Age, Sex And Survival Of Ventricular Fibrillation Out-of-Hospital Cardiac Arrest: The Influence Of Electrocardiogram Waveform Measures

Background: Studies of sex-based outcome in out-of-hospital cardiac arrest (OHCA) have produced mixed results that may depend on age. Menopause produces a range of effects that may influence resuscitation. We used quantitative measures of the presenting OHCA ventricular fibrillation (VF) waveform - a surrogate of the myocardium’s acute physiology - to assess whether outcome differences according to sex and age group may be mediated via a biologic mechanism. Methods: We conducted a retrospective cohort study of adults with non-traumatic VF-OHCA in a metropolitan Emergency Medical Services system between 2008-2020. We used multivariable logistic regression to assess the association of outcome (survival to hospital discharge, favorable neurological outcome (Cerebral Performance Category (CPC) 1 or 2), with sex and age group (&lt;55, &gt; 55 yrs). We then determined the proportion of the outcome difference mediated by the waveform measure by incorporating previously described quantitative VF waveform measures - an estimated probability of survival (P Survival ) and amplitude spectrum area (AMSA - into the model individually. Results: Of 1526 VF-OHCA patients, average age was 62 years, and 29% were female. Overall, younger women were more likely to survive than younger men (survival 67% vs 54%, p=0.02 with CPC 1-2 61% vs 51%, p=0.07), while older women and older men had similar worse outcomes. After adjusting for Utstein characteristics, women &lt;55 compared to men &lt;55 was associated with greater odds of survival to hospital discharge (OR 1.93, 95% CI 1.23-3.09, p=0.005) and CPC 1-2 survival (OR 1.72, 95% CI 1.10-2.71, p=0.02), whereas no associated sex-based differences in these outcomes were observed between the older groups. Each waveform measure mediated some of the beneficial association between sex and age group with survival: 47% for P Survival and 25% AMSA. Conclusions: Women &lt;55 years were more likely to survive to hospital discharge and with favorable neurological status compared to men &lt;55 years, a sex-associated outcome difference not observed in the older age group. The biologic mechanism represented by VF waveform measures mediated some (47% at most), though not all, of the outcome differences.

Ventricular fibrillation amplitude spectral area as a guide to deliver the optimal energy level for defibrillation

Abstract Introduction In case of cardiac arrest due to ventricular fibrillation (VF) or pulseless ventricular tachycardia (pVT), the optimal energy should be the lowest energy effective to achieve defibrillation minimizing the current-induced myocardial damage. Therefore, it would be ideal to minimize the energy level as well as the number of shocks during resuscitation. ECG-based VF waveform analysis features such as amplitude spectral area have been recently introduced as predictors of shock success, but their predictivity for shock success with low energy level is not known. Purpose To assess whether amplitude spectral area (AMSA) of VF is able to predict the efficacy of low energy level defibrillation in out-of-hospital cardiac arrest (OHCA) patients. Methods All the OHCAs with at least one shockable rhythm that occurred from January 2015 to December 2020 were considered. AMSA values were calculated by retrospectively analyzing the data collected by the Corpuls 3 monitors/defibrillators and by using a 2-second-pre-shock ECG interval. Results Among 4619 OHCAs, resuscitation was attempted in 2982 (64%) and at least one shock was delivered in 697 (15%). AMSA values and defibrillation energy were available for 791 shocks, of which 45% received shock at low energy (&amp;gt;150J) and 55% at high energy (&amp;gt;150J). The rate of efficacy between the two groups was similar (44% vs 38%, p=0.102). However, in patients efficaciously treated with shock at low energy, AMSA was higher compared to those treated with shock at high energy [13.2 mV Hz (IQR 10.2–17) vs 10.8 mV Hz (IQR 8–13.8), p&amp;lt;0.001]. Moreover, AMSA values were significantly different when comparing ineffective shock at low energy with effective shock at high energy [6.6 mV Hz (IQR 4.6–10) vs 10.8 mV Hz (IQR 8.1–13.8), p&amp;lt;0.001] and similar when comparing ineffective shock at low and high energy [6.6 mV Hz (IQR 4.6–10) vs 6.3 mV Hz (IQR 4.5–8.7), p=0.21]. By dividing AMSA values into three tertiles, the rate of shock success at low energy was statistically different: [T1 (0.7–6.2 mV Hz) 4.2%; T2 (6.2–10.8 mV Hz) 13%; T3 (10.8–63.2 mV Hz) 42%; Chi-squared p&amp;lt;0.001 and p for trend &amp;lt;0.001]. After correction for age, sex, amiodarone use and call to shock time, AMSA values corresponding to the third and second tertile were associated with higher probability of shock success at low energy compared to the lowest tertile [T3 OR 15 (95% CI 7–30), p&amp;lt;0.001; T2 OR 3 (95% CI 1–7), p=0.002]. Conclusion Ventricular fibrillation amplitude spectral area is a predictor of shock success at a low energy level. This could be useful to optimize both time and dose-energy to patients, yielding the highest chance for successful defibrillation while reducing the number of futile shocks and thus limiting the total current myocardial energy as well as CPR interruptions. Funding Acknowledgement Type of funding sources: None.

Insights From the Ventricular Fibrillation Waveform Into the Mechanism of Survival Benefit From Bystander Cardiopulmonary Resuscitation.

BackgroundThe mechanism by which bystander cardiopulmonary resuscitation (CPR) improves survival following out‐of‐hospital cardiac arrest is unclear. We hypothesized that ventricular fibrillation (VF) waveform measures, as surrogates of myocardial physiology, mediate the relationship between bystander CPR and survival.Methods and ResultsWe performed a retrospective cohort study of adult, bystander‐witnessed patients with out‐of‐hospital cardiac arrest with an initial rhythm of VF who were treated by a metropolitan emergency medical services system from 2005 to 2018. Patient, resuscitation, and outcome variables were extracted from emergency medical services and hospital records. A total of 3 VF waveform measures (amplitude spectrum area, peak frequency, and median peak amplitude) were computed from a 3‐second ECG segment before the initial shock. Multivariable logistic regression estimated the association between bystander CPR and survival to hospital discharge adjusted for Utstein elements. Causal mediation analysis quantified the proportion of survival benefit that was mediated by each VF waveform measure. Of 1069 patients, survival to hospital discharge was significantly higher among the 814 patients who received bystander CPR than those who did not (0.52 versus 0.43, respectively; P<0.01). The multivariable‐adjusted odds ratio for bystander CPR and survival was 1.6 (95% CI, 1.2, 2.1), and each VF waveform measure attenuated this association. Depending on the specific waveform measure, the proportion of mediation varied: 53% for amplitude spectrum area, 31% for peak frequency, and 29% for median peak amplitude.ConclusionsBystander CPR correlated with more robust initial VF waveform measures, which in turn mediated up to one‐half of the survival benefit associated with bystander CPR. These results provide insight into the biological mechanism of bystander CPR in VF out‐of‐hospital cardiac arrest.

The effect of the localisation of an underlying ST-elevation myocardial infarction on the VF-waveform: A multi-centre cardiac arrest study

IntroductionIn cardiac arrest, ventricular fibrillation (VF) waveform characteristics such as amplitude spectrum area (AMSA) are studied to identify an underlying myocardial infarction (MI). Observational studies report lower AMSA-values in patients with than without underlying MI. Moreover, experimental studies with 12-lead ECG-recordings show lowest VF-characteristics when the MI-localisation matches the ECG-recording direction. However, out-of-hospital cardiac arrest (OHCA)-studies with defibrillator-derived VF-recordings are lacking. MethodsMulti-centre (Amsterdam/Nijmegen, the Netherlands) cohort-study on the association between AMSA, ST-elevation MI (STEMI) and its localisation. AMSA was calculated from defibrillator pad-ECG recordings (proxy for lead II, inferior vantage point); STEMI-localisation was determined using ECG/angiography/autopsy findings. ResultsWe studied AMSA-values in 754 OHCA-patients. There were statistically significant differences between no STEMI, anterior STEMI and inferior STEMI (Nijmegen: no STEMI 13.0mVHz [7.9–18.6], anterior STEMI 7.5mVHz [5.6–13.8], inferior STEMI 7.5mVHz [5.4–11.8], p = 0.006. Amsterdam: 11.7mVHz [5.0–21.9], 9.6mVHz [4.6–17.2], and 6.9mVHz [3.2–16.0], respectively, p = 0.001). Univariate analyses showed significantly lower AMSA-values in inferior STEMI vs. no STEMI; there was no significant difference between anterior and no STEMI. After correction for confounders, adjusted absolute AMSA-values were numerically lowest for inferior STEMI in both cohorts, and the relative differences in AMSA between inferior and no STEMI was 1.4–1.7 times larger than between anterior and no STEMI. ConclusionThis multi-centre VF-waveform OHCA-study showed significantly lower AMSA in case of underlying STEMI, with a more pronounced difference for inferior than for anterior STEMI. Confirmative studies on the impact of STEMI-localisation on the VF-waveform are warranted, and might contribute to earlier diagnosis of STEMI during VF.

Towards individualised treatment of out-of-hospital cardiac arrest patients: an update on technical innovations in the prehospital chain of survival.

Out-of-hospital cardiac arrest (OHCA) is a major healthcare problem, with approximately 200 weekly cases in the Netherlands. Its critical, time-dependent nature makes it a unique medical situation, of which outcomes strongly rely on infrastructural factors and on-scene care by emergency medical services (EMS). Survival to hospital discharge is poor, although it has substantially improved, to roughly 25% over the last years. Recognised key factors, such as bystander resuscitation and automated external defibrillator use at the scene, have been markedly optimised with the introduction of technological innovations. In an era with ubiquitous smartphone use, the Dutch digital text message alert platform HartslagNu (www.hartslagnu.nl) increasingly contributes to timely care for OHCA victims. Guidelines emphasise the role of cardiac arrest recognition and early high-quality bystander resuscitation, which calls for education and improved registration at HartslagNu. As for EMS care, new technological developments with future potential are the selective use of mechanical chest compression devices and extracorporeal life support. As a future innovation, ‘smart’ defibrillators are under investigation, guiding resuscitative interventions based on ventricular fibrillation waveform characteristics. Taken together, optimisation of available prehospital technologies is crucial to further improve OHCA outcomes, with particular focus on more available trained volunteers in the first phase and additional research on advanced EMS care in the second phase.

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

AimIn 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. MethodsProspective 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. ResultsIn 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. ConclusionsECG-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.

Optimizing defibrillation during cardiac arrest

Current cardiac arrest guidelines are based on a fixed, time-based defibrillation strategy. Rhythm analysis and shock delivery (if indicated) are repeated every 2 min requiring cyclical interruptions of chest compressions. This approach has several downsides, such as the need to temporarily stop cardiopulmonary resuscitation (CPR) for a variable amount of time, thus reducing myocardial perfusion and decreasing the chance of successful defibrillation. A tailored defibrillation strategy should identify treatment priority for each patient, that is chest compressions (CCS) or defibrillation, minimize CCs interruptions, speed up the delivery of early effective defibrillation and reduce the number of ineffective shocks. Real-time ECG analysis (using adaptive filters, new algorithms robust to chest compressions artifacts and shock-advisory algorithms) is an effective strategy to correctly identify heart rhythm during CPR and reduce the hands-off time preceding a shock. Similarly, ventricular fibrillation waveform analysis, that is amplitude spectrum area (AMSA) represents a well established approach to reserve defibrillation in patients with high chance of shock success and postpone it when ventricular fibrillation termination is unlikely. Both approaches demonstrated valuable results in improving cardiac arrest outcomes in experimental and observational study. Real-time ECG analysis and AMSA have the potential to predict ventricular fibrillation termination, return of spontaneous circulation and even survival, with discretely high confidence. Prospective studies are now necessary to validate these new approaches in the clinical scenario.

Recent advances in external cardiac defibrillation techniques

As an important medical electronic equipment for the cardioversion of malignant arrhythmia such as ventricular fibrillation and ventricular tachycardia, cardiac external defibrillators have been widely used in the clinics. However, the resuscitation success rate for these patients is still unsatisfied. In this paper, the recent advances of cardiac external defibrillation technologies is reviewed. The potential mechanism of defibrillation, the development of novel defibrillation waveform, the factors that may affect defibrillation outcome, the interaction between defibrillation waveform and ventricular fibrillation waveform, and the individualized patient-specific external defibrillation protocol are analyzed and summarized. We hope that this review can provide helpful reference for the optimization of external defibrillator design and the individualization of clinical application.

MLWAVE: A novel algorithm to classify primary versus secondary asphyxia-associated ventricular fibrillation

Introduction/HypothesisThe outcome of cardiopulmonary resuscitation (CPR) depends on timely recognition of the underlying cause of cardiac arrest. Ventricular fibrillation (VF) waveform analysis to differentiate primary VF from secondary asphyxia-associated VF may allow tailoring of therapies to improve cardiac arrest outcomes. Therefore, the primary goal of this investigation was to develop a novel technique utilizing wavelet synchrosqueezed transform (WSST) and decision-tree classifier that was specifically adapted to discriminate between these two incidents of VF. MethodsSecondary analytical investigation of electrocardiography (ECG) data obtained from swine models of either primary VF (n=18) or secondary asphyxia-associated VF (7min of asphyxia prior to VF induction; n=12). In the primary analysis, WSST technique was applied to the first 35s of the VF ECG signal to identify the most differentiating characteristics of the signal for use as features to develop a machine learning algorithm to classify the arrest as either primary VF vs. secondary asphyxia-associated VF. The performance of this new interactive Machine Learning algorithm with Wavelet Energy features of ECG (MLWAVE) was assessed using both classification accuracy and area under the receiver operating characteristic curve (AUCROC). To evaluate the validity of the new technique, the amplitude spectrum area (AMSA)-based technique, a well-established defibrillation classification method, was also applied to the same ECG signals. The classification accuracy and AUCROC were then compared between the two techniques. ResultsFor the primary analysis evaluating the first 35s of the VF waveform, the MLWAVE technique classified the type of VF with high accuracy (28/28 [100%], AUCROC: 1.00). The MLWAVE technique performed better than the AMSA technique across all comparisons, but given the small sample sizes, differences were not statistically significant (accuracy: 100% vs. 85.7%; p=0.24; AUCROC: 1.00 vs. 0.82; p=0.24). ConclusionThis analytical investigation illustrates the advantages of the MLWAVE signal processing method which was associated with 100% accuracy in classifying the type of VF waveform: primary vs. asphyxia-associated. Such classification could lead to personalized tailoring of resuscitation (e.g., immediate defibrillation vs. continued CPR and treatment of reversible cardiac arrest causes before defibrillation) to improve outcomes for cardiac arrest.

Abstract 253: Trajectory of Ventricular Fibrillation Prognostic Status During Cardiopulmonary Resuscitation

Background: Resuscitation from out-of-hospital cardiac arrest (OHCA) due to ventricular fibrillation (VF) typically involves continuous CPR cycles interrupted every 2 minutes for rhythm analysis and potential defibrillation. Quantitative measures of the VF ECG waveform have been proposed to guide therapy for VF arrest because they are associated with myocardial energetics, are dynamic over the course of resuscitation, and predict outcome. However, while VF waveform measures have until recently have required CPR interruption to accurately gauge prognostic status, CPR interruptions are associated with a lower chance of survival. We used a novel waveform measure previously-validated during active CPR to estimate the course of VF status through the 2-minute CPR cycle between consecutive shocks. Methods: We conducted an observational study of patients with VF OHCA who experienced recurrent VF for at least 90 seconds following initial shock. We used the continuous defibrillator ECG to calculate the VF waveform measure as a function of predicted probability of survival-with-intact-neurologic-status at 1-s intervals over the course of resuscitation between shocks. Results: We collected 499 VF ECG segments (≥90 seconds) during CPR from 313 patients. The trajectory of the average prognostic VF measure had a 3-phase time-dependent pattern (Fig. 1). During CPR, the slope of the measure decreased during the initial 25 s of VF (slope = -12%/min) and was relatively flat during the subsequent 65-s interval of VF (slope = +1%/min). Furthermore, slope decreased sharply following the cessation of CPR for rhythm analysis, charge, and shock (slope = -23%/min). Conclusion: On average, a novel VF waveform measure assessed during the scheduled cycle of CPR and rhythm analysis between consecutive shocks was characterized by a period of decline, stabilization, and then decline. Whether these changes in VF status can be used to improve care for individual patients is uncertain.

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

BackgroundIn 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 ResultsWe 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.ConclusionsThis 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.

Ventricular fibrillation waveform characteristics in out-of-hospital cardiac arrest and cardiovascular medication use

BackgroundVentricular fibrillation (VF) waveform analyses are considered a reliable proxy for OHCA characteristics in out-of-hospital cardiac arrest (OHCA), but patient characteristics such as cardiovascular medication use might also be associated with changes in VF waveform measures. ObjectivesTo assess associations between cardiovascular medication use and amplitude spectrum area (AMSA) of VF, while correcting for the presence of cardiovascular disease (CVD), CVD risk factors, and OHCA characteristics. MethodsWe included 990 VF patients from an OHCA registry in the Netherlands, with available information on medical history and cardiovascular medication use. Associations between cardiovascular medication use and AMSA were tested in a multivariate linear regression model, adjusting for CVD, CVD risk factors, and OHCA characteristics. Model performance was shown using R-square and R-change. We also calculated whether medication use was associated with faster dissolution of AMSA to lower values with increasing time delay. ResultsIn the multivariate analysis, when corrected for CVD, CVD risk factors and OHCA characteristics, only potassium-sparing agents were associated with a lower AMSA when compared to patients using other cardiovascular medications (OR 0.46 [95% CI 0.10–0.81]; P < 0.012). The decrease in AMSA with increasing EMS-call-to-ECG delay was the same for patients with and without cardiovascular medication use (all P > 0.05). Only a small part of the variance in AMSA could be explained by medication use (R-square 0.003– 0.026). Adding OHCA characteristics to the model resulted in the largest R square change (0.09–0.15). ConclusionsIt is unlikely that there is a strong and clinically relevant independent pharmacologic effect of cardiovascular medication use on AMSA. In OHCA, AMSA might be used as patient management tool without considering cardiovascular medication use.

Validation of spectral energy for the quantitative analysis of ventricular fibrillation waveform to guide defibrillation in a porcine model of cardiac arrest and resuscitation.

The amplitude spectrum area (AMSA), a frequency-domain ventricular fibrillation (VF) waveform metric, can predict successful defibrillation and the return of spontaneous circulation (ROSC) after defibrillation attempts. We aimed to investigate the validation of Spectral Energy for the quantitative analysis of the VF waveform to guide defibrillation in a porcine model of cardiac arrest and compare it with the AMSA metric. In addition, we sought to determine the effects of epinephrine and cardiopulmonary resuscitation (CPR) on AMSA and Spectral Energy. Sixty male domestic pigs weighing 35 to 45 kg were involved in this study. VF was initially untreated for 10 min followed by 6 min of CPR. Epinephrine was administered to the animals after 2 min of CPR. After the CPR, a single 120-J biphasic shock was applied to the animals. AMSA and Spectral Energy values were measured every minute from the electrocardiogram (ECG) to defibrillation. Receiver operating characteristic (ROC) curves were calculated for both the Spectral Energy and AMSA methods. Spectral Energy and AMSA values gradually decayed during untreated VF in all the animals. However, after the application of CPR and epinephrine, Spectral Energy and AMSA values were significantly increased in animals which were later successfully defibrillated, but did not increase in animals in which defibrillation was unsuccessful. The ROC curves showed that the Spectral Energy and AMSA methods possessed similar levels of sensitivity and specificity in predicting defibrillation success (P<0.001). Both the Spectral Energy and AMSA methods accurately predict successful defibrillation. Moreover, increases in the value of either Spectral Energy or AMSA after application of CPR and epinephrine may also predict successful defibrillation.