Course of quantitative ventricular fibrillation waveform measure and outcome following out-of-hospital cardiac arrest

An acoustic method (Spectral Flux) to analyze ECG signals for optimizing timing for defibrillation in a porcine model of ventricular fibrillation

Aim

Spectral Flux (SF), which is based on common algorithms in the audio processing field, was applied to quantitatively analyze ECG signals to optimize the timing of defibrillation. With the aim of proving the performance in optimizing the timing of defibrillation, SF was compared with Amplitude Spectrum Area (AMSA) in a porcine model of ventricular fibrillation (VF) in a retrospective analysis experiment.

Methods

A total of 56 male domestic pigs, weighing 40 ± 5 kg, were induced to undergo VF. Animals were then left untreated for 10 min, and after 6 min of cardiopulmonary resuscitation (CPR) defibrillation was performed. The respective SF and AMSA values were calculated every minute during VF and CPR. Comparisons were made through receiver operating characteristic (ROC) curves, one-way analyses of variance (one-way ANOVA), and scatterplots for the successful initial defibrillation sample (positive samples, Group R) and the failed initial defibrillation sample (negative samples, Group N) to illustrate the performance in optimizing the timing of defibrillation for the AMSA and SF methods.

Result

Values of SF and AMSA gradually decreased during the 10 min VF period and increased in during the 6 min CPR period. The scatterplots showed that both metrics had the ability to distinguish positive and negative samples (p < .001). Meanwhile, ROC curves showed that SF (area under the curve, AUC = 0.798, p < .001) had the same ability as AMSA (AUC = 0.737, p < .001) to predict the successful defibrillation (Z = 1.35, p = 0.177). Moreover, when comparing the values for AMSA and SF between the successful initial defibrillation samples (Group R) and the failed initial defibrillation samples (Group N), the results showed that the values of both AMSA and SF in Group R were significantly higher than those in Group N (p < .001).

Conclusion

In the present study, SF method had the same ability as AMSA to predict successful defibrillation with significantly higher values in cases of successful defibrillation than the instances in which defibrillation failed. Additionally, SF method might be more stable than AMSA for filtering out the higher frequency interference signals due to the narrower frequency range and had higher specificity and predictive accuracy than AMSA. So SF method had high clinical potential to optimize the timing of defibrillation. Nevertheless, further animal and clinical studies are still needed to confirm the effectiveness and practicality of SF as a predictive module for defibrillators in clinical practice.

Amplitude spectral area of ventricular fibrillation can discriminate survival of patients with out-of-hospital cardiac arrest

Background

Evidence of the association between AMplitude Spectral Area (AMSA) of ventricular fibrillation and outcome after out-of-hospital cardiac arrest (OHCA) is limited to short-term follow-up. In this study, we assess whether AMSA can stratify the risk of death or poor neurological outcome at 30 days and 1 year after OHCA in patients with an initial shockable rhythm or with an initial non-shockable rhythm converted to a shockable one.

Methods

This is a multicentre retrospective study of prospectively collected data in two European Utstein-based OHCA registries. We included all cases of OHCAs with at least one manual defibrillation. AMSA values were calculated after data extraction from the monitors/defibrillators used in the field by using a 2-s pre-shock electrocardiogram interval. The first detected AMSA value, the maximum value, the average value, and the minimum value were computed, and their outcome prediction accuracy was compared. Multivariable Cox regression models were run for both 30-day and 1-year deaths or poor neurological outcomes. Neurological cerebral performance category 1-2 was considered a good neurological outcome.

Results

Out of the 578 patients included, 494 (85%) died and 10 (2%) had a poor neurological outcome at 30 days. All the AMSA values considered (first value, maximum, average, and minimum) were significantly higher in survivors with good neurological outcome at 30 days. The average AMSA showed the highest area under the receiver operating characteristic curve (0.778, 95% CI: 0.7-0.8, p < 0.001). After correction for confounders, the highest tertiles of average AMSA (T3 and T2) were significantly associated with a lower risk of death or poor neurological outcome compared with T1 both at 30 days (T2: HR 0.6, 95% CI: 0.4-0.9, p = 0.01; T3: HR 0.6, 95% CI: 0.4-0.9, p = 0.02) and at 1 year (T2: HR 0.6, 95% CI: 0.4-0.9, p = 0.01; T3: HR 0.6, 95% CI: 0.4-0.9, p = 0.01). Among survivors at 30 days, a higher AMSA was associated with a lower risk of mortality or poor neurological outcome at 1 year (T3: HR 0.03, 95% CI: 0-0.3, p = 0.02).

Discussion

Lower AMSA values were significantly and independently associated with the risk of death or poor neurological outcome at 30 days and at 1 year in OHCA patients with either an initial shockable rhythm or a conversion rhythm from non-shockable to shockable. The average AMSA value had the strongest association with prognosis.

Real-time amplitude spectrum area estimation during chest compression from the ECG waveform using a 1D convolutional neural network

Introduction: Amplitude spectrum area (AMSA) is a well-established measure than can predict defibrillation outcome and guiding individualized resuscitation of ventricular fibrillation (VF) patients. However, accurate AMSA can only be calculated during cardiopulmonary resuscitation (CPR) pause due to artifacts produced by chest compression (CC). In this study, we developed a real-time AMSA estimation algorithm using a convolutional neural network (CNN). Methods: Data were collected from 698 patients, and the AMSA calculated from the uncorrupted signals served as the true value for both uncorrupted and the adjacent corrupted signals. An architecture consisting of a 6-layer 1D CNN and 3 fully connected layers was developed for AMSA estimation. A 5-fold cross-validation procedure was used to train, validate and optimize the algorithm. An independent testing set comprised of simulated data, real-life CC corrupted data, and preshock data was used to evaluate the performance. Results: The mean absolute error, root mean square error, percentage root mean square difference and correlation coefficient were 2.182/1.951 mVHz, 2.957/2.574 mVHz, 22.887/28.649% and 0.804/0.888 for simulated and real-life testing data, respectively. The area under the receiver operating characteristic curve regarding predicting defibrillation success was 0.835, which was comparable to that of 0.849 using the true value of the AMSA. Conclusions: AMSA can be accurately estimated during uninterrupted CPR using the proposed method.

Continuous Assessment of Ventricular Fibrillation Prognostic Status during CPR: Implications for Resuscitation

BACKGROUND

Ventricular fibrillation (VF) waveform measures reflect myocardial physiologic status. Continuous assessment of VF prognosis using such measures could guide resuscitation, but has not been possible due to CPR artifact in the ECG. A recently-validated VF measure (termed VitalityScore), which estimates the probability (0-100%) of return-of-rhythm (ROR) after shock, can assess VF during CPR, suggesting potential for continuous application during resuscitation.

OBJECTIVE

We evaluated VF using VitalityScore to characterize VF prognostic status continuously during resuscitation.

METHODS

We characterized VF using VitalityScore during 60 seconds of CPR and 10 seconds of subsequent pre-shock CPR interruption in patients with out-of-hospital VF arrest. VitalityScore utility was quantified using area under the receiver operating characteristic curve (AUC). VitalityScore trends over time were estimated using mixed-effects models, and associations between trends and ROR were evaluated using logistic models. A sensitivity analysis characterized VF during protracted (100-second) periods of CPR.

RESULTS

We evaluated 724 VF episodes among 434 patients. After an initial decline from 0-8 seconds following VF onset, VitalityScore increased slightly during CPR from 8-60 seconds (slope: 0.18 %/min). During the first 10 seconds of subsequent pre-shock CPR interruption, VitalityScore declined (slope: -14 %/min). VitalityScore predicted ROR throughout CPR with AUCs 0.73-0.75. Individual VitalityScore trends during 8-60 seconds of CPR were marginally associated with subsequent ROR (adjusted odds ratio for interquartile slope change (OR)=1.10, p=0.21), and became significant with protracted (≥100 seconds) CPR duration (OR=1.28, p=0.006).

CONCLUSION

VF prognostic status can be continuously evaluated during resuscitation, a development that could translate to patient-specific resuscitation strategies.

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

Background

The 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 Results

We 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.

Conclusions

Bystander 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

INTRODUCTION

In 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.

METHODS

Multi-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.

RESULTS

We 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.

CONCLUSION

This 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.

Effect of Amplitude Spectral Area on Termination of Fibrillation and Outcomes in Pediatric Cardiac Arrest

Background

Amplitude spectral area (AMSA) predicts termination of fibrillation (TOF) with return of spontaneous circulation (ROSC) and survival in adults but has not been studied in pediatric cardiac arrest. We characterized AMSA during pediatric cardiac arrest from a Pediatric Resuscitation Quality Collaborative and hypothesized that AMSA would be associated with TOF and ROSC.

Methods and Results

Children aged <18 years with cardiac arrest and ventricular fibrillation were studied. AMSA was calculated for 2 seconds before shock and averaged for each subject (AMSA‐avg). TOF was defined as termination of ventricular fibrillation 10 seconds after defibrillation to any non‐ventricular fibrillation rhythm. ROSC was defined as >20 minutes without chest compressions. Univariate and multivariable logistic regression analyses controlling for weight, current, and illness category were performed. Primary end points were TOF and ROSC. Secondary end points were 24‐hour survival and survival to discharge. Between 2015 and 2019, 50 children from 14 hospitals with 111 shocks were identified. In univariate analyses AMSA was not associated with TOF and AMS‐Aavg was not associated with ROSC. Multivariable logistic regression showed no association between AMSA and TOF but controlling for defibrillation average current and illness category, there was a trend to significant association between AMSA‐avg and ROSC (odds ratio, 1.10 [1.00‒1.22] P=0.058). There was no significant association between AMSA‐avg and 24‐hour survival or survival to hospital discharge.

Conclusions

In pediatric patients, AMSA was not associated with TOF, whereas AMSA‐avg had a trend to significance for association in ROSC, but not 24‐hour survival or survival to hospital discharge.

Registration

URL: https://www.clinicaltrials.gov; Unique identifier: NCT02708134.

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.

Estimating the amplitude spectrum area of ventricular fibrillation during cardiopulmonary resuscitation using only ECG waveform

Background

Amplitude spectrum area (AMSA) calculated from ventricular fibrillation (VF) can be used to monitor the effectiveness of chest compression (CC) and optimize the timing of defibrillation. However, reliable AMSA can only be obtained during CC pause because of artifacts. In this study, we sought to develop a method for estimating AMSA during cardiopulmonary resuscitation (CPR) using only the electrocardiogram (ECG) waveform.

Methods

Intervals of 8 seconds ECG and CC-related references, including 4 seconds during CC and an adjacent 4 seconds without CC, were collected before 1,008 defibrillation shocks from 512 out-of-hospital cardiac arrest patients. Signal quality was analyzed based on the irregularity of autocorrelation of VF. If signal quality index (SQI) was high, AMSA would be calculated from the original signal. Otherwise, CC-related artifacts would be constructed and suppressed using the least mean square filter from VF before calculation of AMSA. The algorithm was optimized using 480 training shocks and evaluated using 528 independent testing shocks.

Results

Overall, CC resulted in lower SQI [0.15 (0.04-0.61) with CC vs. 0.75 (0.61-0.83) without CC, P<0.01] and higher AMSA [11.2 (7.7-16.2) with CC vs. 7.2 (4.9-10.6) mVHz without CC, P<0.01] values. The predictive accuracy (49.2% vs. 66.5%, P<0.01) and area under the receiver operating characteristic curve (AUC) (0.647 vs. 0.734, P<0.01) were significantly decreased during CC. Using the proposed method, the estimated AMSA was 7.1 (5.0-15.2) mVHz, the predictive accuracy was 67.0% and the AUC was 0.713, which were all comparable with those calculated without CC.

Conclusions

Using the signal quality-based artifact suppression method, AMSA can be reliably estimated and continuously monitored during CPR.

A method to predict ventricular fibrillation shock outcome during chest compressions

BACKGROUND

Out-of-hospital ventricular fibrillation (VF) cardiac arrest is a leading cause of death. Quantitative analysis of the VF electrocardiogram (ECG) can predict patient outcomes and could potentially enable a patient-specific, guided approach to resuscitation. However, VF analysis during resuscitation is confounded by cardiopulmonary resuscitation (CPR) artifact in the ECG, challenging continuous application to guide therapy throughout resuscitation. We therefore sought to design a method to predict VF shock outcomes during CPR.

METHODS

Study data included 4577 5-s VF segments collected during and without CPR prior to defibrillation attempts in N = 1151 arrest patients. Using training data (460 patients), an algorithm was designed to predict the VF shock outcomes of defibrillation success (return of organized ventricular rhythm) and functional survival (Cerebral Performance Category 1-2). The algorithm was designed with variable-frequency notch filters to reduce CPR artifact in the ECG based on real-time chest compression rate. Ten ECG features and three dichotomous patient characteristics were developed to predict outcomes. These variables were combined using support vector machines and logistic regression. Algorithm performance was evaluated by area under the receiver operating characteristic curve (AUC) to predict outcomes in validation data (691 patients).

RESULTS

AUC (95% Confidence Interval) for predicting defibrillation success was 0.74 (0.71-0.77) during CPR and 0.77 (0.74-0.79) without CPR. AUC for predicting functional survival was 0.75 (0.72-0.78) during CPR and 0.76 (0.74-0.79) without CPR.

CONCLUSION

A novel algorithm predicted defibrillation success and functional survival during ongoing CPR following VF arrest, providing a potential proof-of-concept towards real-time guidance of resuscitation therapy.

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.

Closed-loop machine-controlled CPR system optimises haemodynamics during prolonged CPR

Objectives

We evaluated the feasibility of optimising coronary perfusion pressure (CPP) during cardiopulmonary resuscitation (CPR) with a closed-loop, machine-controlled CPR system (MC-CPR) that sends real-time haemodynamic feedback to a set of machine learning and control algorithms which determine compression/decompression characteristics over time.

Background

American Heart Association CPR guidelines (AHA-CPR) and standard mechanical devices employ a “one-size-fits-all” approach to CPR that fails to adjust compressions over time or individualise therapy, thus leading to deterioration of CPR effectiveness as duration exceeds 15–20 ​min.

Methods

CPR was administered for 30 ​min in a validated porcine model of cardiac arrest. Intubated anaesthetised pigs were randomly assigned to receive MC-CPR (6), mechanical CPR conducted according to AHA-CPR (6), or human-controlled CPR (HC-CPR) (10). MC-CPR directly controlled the CPR piston’s amplitude of compression and decompression to maximise CPP over time. In HC-CPR a physician controlled the piston amplitudes to maximise CPP without any algorithmic feedback, while AHA-CPR had one compression depth without adaptation.

Results

MC-CPR significantly improved CPP throughout the 30-min resuscitation period compared to both AHA-CPR and HC-CPR. CPP and carotid blood flow (CBF) remained stable or improved with MC-CPR but deteriorated with AHA-CPR. HC-CPR showed initial but transient improvement that dissipated over time.

Conclusion

Machine learning implemented in a closed-loop system successfully controlled CPR for 30 ​min in our preclinical model. MC-CPR significantly improved CPP and CBF compared to AHA-CPR and ameliorated the temporal haemodynamic deterioration that occurs with standard approaches.

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

Background

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.

Methods

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.

Results

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).

Conclusions

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.

Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest

BACKGROUND

Quantitative measures of the ventricular fibrillation (VF) ECG waveform can assess myocardial physiology and predict cardiac arrest outcomes, making these measures a candidate to help guide resuscitation. Chest compressions are typically paused for waveform measure calculation because compressions cause ECG artifact. However, such pauses contradict resuscitation guideline recommendations to minimize cardiopulmonary resuscitation interruptions. We evaluated a comprehensive group of VF measures with and without ongoing compressions to determine their performance under both conditions for predicting functionally-intact survival, the study's primary outcome.

METHODS

Five-second VF ECG segments were collected with and without chest compressions before 2755 defibrillation shocks from 1151 out-of-hospital cardiac arrest patients. Twenty-four individual measures and 3 combination measures were implemented. Measures were optimized to predict functionally-intact survival (Cerebral Performance Category score ≤2) using 460 training cases, and their performance evaluated using 691 independent test cases.

RESULTS

Measures predicted functionally-intact survival on test data with an area under the receiver operating characteristic curve ranging from 0.56 to 0.75 (median, 0.73) without chest compressions and from 0.53 to 0.75 (median, 0.69) with compressions ( P<0.001 for difference). Of all measures evaluated, the support vector machine model ranked highest both without chest compressions (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.73-0.78) and with compressions (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.72-0.78; P=0.75 for difference).

CONCLUSIONS

VF waveform measures predict functionally-intact survival when calculated during chest compressions, but prognostic performance is generally reduced compared with compression-free analysis. However, support vector machine models exhibited similar performance with and without compressions while also achieving the highest area under the receiver operating characteristic curve. Such machine learning models may, therefore, offer means to guide resuscitation during uninterrupted cardiopulmonary resuscitation.

Early prognostic value of an Algorithm based on spectral Variables of Ventricular fibrillAtion from the EKG of patients with suddEn cardiac death: A multicentre observational study (AWAKE)

OBJECTIVE

Ventricular fibrillation (VF)-related sudden cardiac death (SCD) is a leading cause of mortality and morbidity. Current biological and imaging parameters show significant limitations on predicting cerebral performance at hospital admission. The AWAKE study (NCT03248557) is a multicentre observational study to validate a model based on spectral ECG analysis to early predict cerebral performance and survival in resuscitated comatose survivors.

METHODS

Data from VF ECG tracings of patients resuscitated from SCD will be collected using an electronic Case Report Form. Patients can be either comatose (Glasgow Coma Scale - GCS - ≤8) survivors undergoing temperature control after return of spontaneous circulation (RoSC), or those who regain consciousness (GCS=15) after RoSC; all admitted to Intensive Cardiac Care Units in 4 major university hospitals. VF tracings prior to the first direct current shock will be digitized and analyzed to derive spectral data and feed a predictive model to estimate favorable neurological performance (FNP). The results of the model will be compared to the actual prognosis.

RESULTS

The primary clinical outcome is FNP during hospitalization. Patients will be categorized into 4 subsets of neurological prognosis according to the risk score obtained from the predictive model. The secondary clinical outcomes are survival to hospital discharge, and FNP and survival after 6 months of follow-up. The model-derived categorisation will be also compared with clinical variables to assess model sensitivity, specificity, and accuracy.

CONCLUSIONS

A model based on spectral analysis of VF tracings is a promising tool to obtain early prognostic data after SCD.

Effects of intra-resuscitation antiarrhythmic administration on rearrest occurrence and intra-resuscitation ECG characteristics in the ROC ALPS trial

BACKGROUND

Intra-resuscitation antiarrhythmic drugs may improve resuscitation outcomes, in part by avoiding rearrest, a condition associated with poor out-of-hospital cardiac arrest (OHCA) outcomes. However, antiarrhythmics may also alter defibrillation threshold. The objective of this study was to investigate the relationship between rearrest and intra-resuscitation antiarrhythmic drugs in the context of the Resuscitation Outcomes Consortium (ROC) amiodarone, lidocaine, and placebo (ALPS) trial.

HYPOTHESIS

Rearrest rates would be lower in cases treated with amiodarone or lidocaine, versus saline placebo, prior to first return of spontaneous circulation (ROSC). We also hypothesized antiarrhythmic effects would be quantifiable through analysis of the prehospital electrocardiogram.

METHODS

We conducted a secondary analysis of the ROC ALPS trial. Cases that first achieved prehospital ROSC after randomized administration of study drug were included in the analysis. Rearrest, defined as loss of pulses following ROSC, was ascertained from emergency medical services records. Rearrest rate was calculated overall, as well as by ALPS treatment group. Multivariable logistic regression models were constructed to assess the association between treatment group and rearrest, as well as rearrest and both survival to hospital discharge and survival with neurologic function. Amplitude spectrum area, median slope, and centroid frequency of the ventricular fibrillation (VF) ECG were calculated and compared across treatment groups.

RESULTS

A total of 1144 (40.4%) cases with study drug prior to first ROSC were included. Rearrest rate was 44.0% overall; 42.9% for placebo, 45.7% for lidocaine, and 43.0% for amiodarone. In multivariable logistic regression models, ALPS treatment group was not associated with rearrest, though rearrest was associated with poor survival and neurologic outcomes. AMSA and median slope measures of the first available VF were associated with rearrest case status, while median slope and centroid frequency were associated with ALPS treatment group.

CONCLUSION

Rearrest rates did not differ between antiarrhythmic and placebo treatment groups. ECG waveform characteristics were correlated with treatment group and rearrest. Rearrest was inversely associated with survival and neurologic outcomes.

Predictive value of amplitude spectrum area of ventricular fibrillation waveform in patients with acute or previous myocardial infarction in out-of-hospital cardiac arrest

BACKGROUND

Amplitude spectrum area (AMSA) of ventricular fibrillation (VF) has been associated with survival from out-of-hospital cardiac arrest (OHCA). Ischemic heart disease has been shown to change AMSA. We studied whether the association between AMSA and survival changes with acute ST-elevation myocardial infarction (STEMI) as cause of the OHCA and/or previous MI.

METHODS

Multivariate logistic regression with log-transformed AMSA of first artifact-free VF segment was used to assess the association between AMSA and survival, according to presence of STEMI or previous MI, adjusting for resuscitation characteristics, medication use and comorbidities.

RESULTS

Of 716 VF-patients included from an OHCA-registry in the Netherlands, 328 (46%) had STEMI as cause of OHCA. Previous MI was present in 186 (26%) patients. Survival was 66%; neither previous MI (P=0.11) nor STEMI (P=0.78) altered survival. AMSA was a predictor of survival (ORadj: 1.52, 95%-CI: 1.28-1.82). STEMI was associated with lower AMSA (8.4mV-Hz [3.7-16.5] vs. 12.3mV-Hz [5.6-23.0]; P<0.001), but previous MI was not (9.5mV-Hz [3.9-18.0] vs 10.6mV-Hz [4.6-19.3]; P=0.27). When predicting survival, there was no interaction between previous MI and AMSA (P=0.14). STEMI and AMSA had a significant interaction (P=0.002), whereby AMSA was no longer a predictor of survival (ORadj: 1.03, 95%-CI: 0.77-1.37) in STEMI-patients. In patients without STEMI, higher AMSA was associated with higher survival rates (ORadj: 1.80, 95%-CI: 1.39-2.35).

CONCLUSIONS

The prognostic value of AMSA is altered by the presence of STEMI: while AMSA has strong predictive value in patients without STEMI, AMSA is not a predictor of survival in STEMI-patients.

Ventricular fibrillation waveform measures combined with prior shock outcome predict defibrillation success during cardiopulmonary resuscitation

AIM

Amplitude Spectrum Area (AMSA) and Median Slope (MS) are ventricular fibrillation (VF) waveform measures that predict defibrillation shock success. Cardiopulmonary resuscitation (CPR) obscures electrocardiograms and must be paused for analysis. Studies suggest waveform measures better predict subsequent shock success when combined with prior shock success. We determined whether this relationship applies during CPR.

METHODS

AMSA and MS were calculated from 5-second pre-shock segments with and without CPR, and compared to logistic models combining each measure with prior return of organized rhythm (ROR).

RESULTS

VF segments from 692 patients were analyzed during CPR before 1372 shocks and without CPR before 1283 shocks. Combining waveform measures with prior ROR increased areas under receiver operating characteristic curves for AMSA/MS with CPR (0.66/0.68 to 0.73/0.74, p<0.001) and without CPR (0.71/0.72 to 0.76/0.76, p<0.001).

CONCLUSIONS

Prior ROR improves prediction of shock success during CPR, and may enable waveform measure calculation without chest compression pauses.

Novel CPR system that predicts return of spontaneous circulation from amplitude spectral area before electric shock in ventricular fibrillation

AIM

Amplitude spectral area (AMSA), an index for analysing ventricular fibrillation (VF) waveforms, is thought to predict the return of spontaneous circulation (ROSC) after electric shocks, but its validity is unconfirmed. We developed an equation to predict ROSC, where the change in AMSA (ΔAMSA) is added to AMSA measured immediately before the first shock (AMSA1). We examine the validity of this equation by comparing it with the conventional AMSA1-only equation.

METHOD

We retrospectively investigated 285 VF patients given prehospital electric shocks by emergency medical services. ΔAMSA was calculated by subtracting AMSA1 from last AMSA immediately before the last prehospital electric shock. Multivariate logistic regression analysis was performed using post-shock ROSC as a dependent variable.

RESULTS

Analysis data were subjected to receiver operating characteristic curve analysis, goodness-of-fit testing using a likelihood ratio test, and the bootstrap method. AMSA1 (odds ratio (OR) 1.151, 95% confidence interval (CI) 1.086-1.220) and ΔAMSA (OR 1.289, 95% CI 1.156-1.438) were independent factors influencing ROSC induction by electric shock. Area under the curve (AUC) for predicting ROSC was 0.851 for AMSA1-only and 0.891 for AMSA1+ΔAMSA. Compared with the AMSA1-only equation, the AMSA1+ΔAMSA equation had significantly better goodness-of-fit (likelihood ratio test P<0.001) and showed good fit in the bootstrap method.

CONCLUSIONS

Post-shock ROSC was accurately predicted by adding ΔAMSA to AMSA1. AMSA-based ROSC prediction enables application of electric shock to only those patients with high probability of ROSC, instead of interrupting chest compressions and delivering unnecessary shocks to patients with low probability of ROSC.

Ventricular fibrillation waveform measures and the etiology of cardiac arrest

BACKGROUND

Early determination of the acute etiology of cardiac arrest could help guide resuscitation or post-resuscitation care. In experimental studies, quantitative measures of the ventricular fibrillation waveform distinguish ischemic from non-ischemic etiology.

METHODS

We investigated whether waveform measures distinguished arrest etiology among adults treated by EMS for out-of-hospital ventricular fibrillation between January 1, 2006-December 31, 2014. Etiology was classified using hospital information into three exclusive groups: acute coronary syndrome (ACS) with ST elevation myocardial infarction (STEMI), ACS without ST elevation (non-STEMI), or non-ischemic arrest. Waveform measures included amplitude spectrum area (AMSA), centroid frequency (CF), mean frequency (MF), and median slope (MS) assessed during CPR-free epochs immediately prior to the initial and second shock. Waveform measures prior to the initial shock and the changes between first and second shock were compared by etiology group. We a priori chose a significance level of 0.01 due to multiple comparisons.

RESULTS

Of the 430 patients, 35% (n=150) were classified as STEMI, 29% (n=123) as non-STEMI, and 37% (n=157) with non-ischemic arrest. We did not observe differences by etiology in any of the waveform measures prior to shock 1 (Kruskal-Wallis Test) (p=0.28 for AMSA, p=0.07 for CF, p=0.63 for MF, and p=0.39 for MS). We also did not observe differences for change in waveform between shock 1 and 2, or when the two acute ischemia groups (STEMI and non-STEMI) were combined and compared to the non-ischemic group.

CONCLUSION

This clinical investigation suggests that waveform measures may not be useful in distinguishing cardiac arrest etiology.

Short ECG segments predict defibrillation outcome using quantitative waveform measures

AIM

Quantitative waveform measures of the ventricular fibrillation (VF) electrocardiogram (ECG) predict defibrillation outcome. Calculation requires an ECG epoch without chest compression artifact. However, pauses in CPR can adversely affect survival. Thus the potential use of waveform measures is limited by the need to pause CPR. We sought to characterize the relationship between the length of the CPR-free epoch and the ability to predict outcome.

METHODS

We conducted a retrospective investigation using the CPR-free ECG prior to first shock among out-of-hospital VF cardiac arrest patients in a large metropolitan region (n=442). Amplitude Spectrum Area (AMSA) and Median Slope (MS) were calculated using ECG epochs ranging from 5s to 0.2s. The relative ability of the measures to predict return of organized rhythm (ROR) and neurologically-intact survival was evaluated at different epoch lengths by calculating the area under the receiver operating characteristic curve (AUC) using the 5-s epoch as the referent group.

RESULTS

Compared to the 5-s epoch, AMSA performance declined significantly only after reducing epoch length to 0.2s for ROR (AUC 0.77-0.74, p=0.03) and with epochs of ≤0.6s for neurologically-intact survival (AUC 0.72-0.70, p=0.04). MS performance declined significantly with epochs of ≤0.8s for ROR (AUC 0.78-0.77, p=0.04) and with epochs ≤1.6s for neurologically-intact survival (AUC 0.72-0.71, p=0.04).

CONCLUSION

Waveform measures predict defibrillation outcome using very brief ECG epochs, a quality that may enable their use in current resuscitation algorithms designed to limit CPR interruption.

Combining Amplitude Spectrum Area with Previous Shock Information Using Neural Networks Improves Prediction Performance of Defibrillation Outcome for Subsequent Shocks in Out-Of-Hospital Cardiac Arrest Patients

Objective

Quantitative ventricular fibrillation (VF) waveform analysis is a potentially powerful tool to optimize defibrillation. However, whether combining VF features with additional attributes that related to the previous shock could enhance the prediction performance for subsequent shocks is still uncertain.

Methods

A total of 528 defibrillation shocks from 199 patients experienced out-of-hospital cardiac arrest were analyzed in this study. VF waveform was quantified using amplitude spectrum area (AMSA) from defibrillator's ECG recordings prior to each shock. Combinations of AMSA with previous shock index (PSI) or/and change of AMSA (ΔAMSA) between successive shocks were exercised through a training dataset including 255shocks from 99patientswith neural networks. Performance of the combination methods were compared with AMSA based single feature prediction by area under receiver operating characteristic curve(AUC), sensitivity, positive predictive value (PPV), negative predictive value (NPV) and prediction accuracy (PA) through a validation dataset that was consisted of 273 shocks from 100patients.

Results

A total of61 (61.0%) patients required subsequent shocks (N = 173) in the validation dataset. Combining AMSA with PSI and ΔAMSA obtained highest AUC (0.904 vs. 0.819, p<0.001) among different combination approaches for subsequent shocks. Sensitivity (76.5% vs. 35.3%, p<0.001), NPV (90.2% vs. 76.9%, p = 0.007) and PA (86.1% vs. 74.0%, p = 0.005)were greatly improved compared with AMSA based single feature prediction with a threshold of 90% specificity.

Conclusion

In this retrospective study, combining AMSA with previous shock information using neural networks greatly improves prediction performance of defibrillation outcome for subsequent shocks.

Feasibility of Biosignal-guided Chest Compression During Cardiopulmonary Resuscitation: A Proof of Concept

OBJECTIVES

Cardiac arrest is one of the leading causes of death in the United States and is treated by cardiopulmonary resuscitation (CPR). CPR involves both chest compressions and positive pressure ventilations when given by medical providers. Mechanical chest compression devices automate chest compressions and are beginning to be adopted by emergency medical services with the intent of providing high-quality, consistent chest compressions that are not limited by human providers who can become fatigued. Biosignals acquired from cardiac arrest patients have been characterized in their ability to track the effect of CPR on the patient. The authors investigated the feasibility and appropriate response of a biosignal-guided mechanical chest compression device in a swine model of cardiac arrest.

METHODS

After a custom signal-guided chest compression device was engineered, its ability to respond to biosignal changes in a swine model of cardiac arrest was tested. In a preliminary series of six swine, two biosignals were used: mean arterial pressure (MAP) and a mathematical derivative of the electrocardiogram waveform, median slope (MS). How these biosignals changed was observed when chest compression rate and depth were adjusted by the signal-guided chest compression device, independent of the user. Chest compression rate and depth were adjusted by the signal-guided chest compression device according to a preset threshold algorithm until either of the biosignals improved to satisfy a set "threshold" or until the chest compression rate and depth achieved maximum values. Defibrillation was attempted at the end of each resuscitation in an effort to achieve return of spontaneous circulation (ROSC).

RESULTS

The signal-guided chest compression device responded appropriately to biosignals by changing its rate and depth. All animals exhibited positive improvements in their biosignals. During the course of the resuscitation, three of the six animals improved their MS biosignal to reach the MS threshold, while two of the six animals improved their MAP biosignal to reach the MAP threshold. In the six experiments conducted, defibrillation was attempted on five animals, and two animals achieved ROSC.

CONCLUSIONS

In this proof-of-concept study, a signal-guided chest compression device was demonstrated to be capable of responding to biosignal input and delivering chest compressions with a broad range of rates and depths.

A Novel Approach to Improve Time to First Shock in Prehospital STEMI Complicated by Ventricular Fibrillation

Lethal cardiac arrhythmias such as ventricular fibrillation and pulseless ventricular tachycardia (VF/pVT) complicate up to 6% of all out-of-hospital STEMIs. Typically, paramedics respond to this by applying defibrillation pads and delivering a shock as soon as possible. A recently introduced "pads-on" protocol directed paramedics to apply defibrillation pads to all STEMI patients (regardless of clinical stability) with the aim of decreasing time to first shock. In this article we present two cases of prehospital STEMI complicated by VF to illustrate times to first shock for the two different protocols. One case each of a STEMI complicated by VF before implementation of the pads-on protocol and after the implementation of the protocol is presented. An important difference in the time to first shock is noted between the two patients with STEMI complicated by VF. While it took 2 min 43 s for the pads-off patient to be defibrillated, only 27 s elapsed before the pads-on patient was defibrillated. These two cases demonstrate that the application of defibrillation pads immediately following the diagnosis of prehospital STEMI has the potential to decrease the time to shock in patients suffering VF/pVT.

Spectral analysis-based risk score enables early prediction of mortality and cerebral performance in patients undergoing therapeutic hypothermia for ventricular fibrillation and comatose status

BACKGROUND

Early prognosis in comatose survivors after cardiac arrest due to ventricular fibrillation (VF) is unreliable, especially in patients undergoing mild hypothermia. We aimed at developing a reliable risk-score to enable early prediction of cerebral performance and survival.

METHODS

Sixty-one out of 239 consecutive patients undergoing mild hypothermia after cardiac arrest, with eventual return of spontaneous circulation (ROSC), and comatose status on admission fulfilled the inclusion criteria. Background clinical variables, VF time and frequency domain fundamental variables were considered. The primary and secondary outcomes were a favorable neurological performance (FNP) during hospitalization and survival to hospital discharge, respectively. The predictive model was developed in a retrospective cohort (n = 32; September 2006-September 2011, 48.5 ± 10.5 months of follow-up) and further validated in a prospective cohort (n = 29; October 2011-July 2013, 5 ± 1.8 months of follow-up).

RESULTS

FNP was present in 16 (50.0%) and 21 patients (72.4%) in the retrospective and prospective cohorts, respectively. Seventeen (53.1%) and 21 patients (72.4%), respectively, survived to hospital discharge. Both outcomes were significantly associated (p < 0.001). Retrospective multivariate analysis provided a prediction model (sensitivity = 0.94, specificity = 1) that included spectral dominant frequency, derived power density and peak ratios between high and low frequency bands, and the number of shocks delivered before ROSC. Validation on the prospective cohort showed sensitivity = 0.88 and specificity = 0.91. A model-derived risk-score properly predicted 93% of FNP. Testing the model on follow-up showed a c-statistic ≥ 0.89.

CONCLUSIONS

A spectral analysis-based model reliably correlates time-dependent VF spectral changes with acute cerebral injury in comatose survivors undergoing mild hypothermia after cardiac arrest.

Amplitude Spectrum Area to Guide Defibrillation

Background—

This study sought to validate the ability of amplitude spectrum area (AMSA) to predict defibrillation success and long-term survival in a large population of out-of-hospital cardiac arrests.

Methods and Results—

ECGs recorded by automated external defibrillators from different manufacturers were obtained from patients with cardiac arrests occurring in 8 city areas. A database, including 2447 defibrillations from 1050 patients, was used as the derivation group, and an additional database, including 1381 defibrillations from 567 patients, served as validation. A 2-second ECG window before defibrillation was analyzed, and AMSA was calculated. Univariable and multivariable regression analyses and area under the receiver operating characteristic curve were used for associations between AMSA and study end points: defibrillation success, sustained return of spontaneous circulation, and long-term survival. Among the 2447 defibrillations of the derivation database, 26.2% were successful. AMSA was significantly higher before a successful defibrillation than a failing one (13±5 versus 6.8±3.5 mV-Hz) and was an independent predictor of defibrillation success (odds ratio, 1.33; 95% confidence interval, 1.20–1.37) and sustained return of spontaneous circulation (odds ratio, 1.22; 95% confidence interval, 1.17–1.26). Area under the receiver operating characteristic curve for defibrillation success prediction was 0.86 (95% confidence interval, 0.85–0.88). AMSA was also significantly associated with long-term survival. The following AMSA thresholds were identified: 15.5 mV-Hz for defibrillation success and 6.5 mV-Hz for defibrillation failure. In the validation database, AMSA ≥15.5 mV-Hz had a positive predictive value of 84%, whereas AMSA ≤6.5 mV-Hz had a negative predictive value of 98%.

Conclusions—

In this large derivation-validation study, AMSA was validated as an accurate predictor of defibrillation success. AMSA also appeared as a predictor of long-term survival.

Towards cardiopulmonary resuscitation without vasoactive drugs

PURPOSE OF REVIEW

Whereas there is clear evidence for improved survival with cardiopulmonary resuscitation (CPR) and defibrillation during cardiac arrest management, there is today lacking evidence that any of the recommended and used drugs lead to any long-term benefit for the patients. In this review, we try to discuss our current view on why advanced life support (ALS) today can be performed without the use of drugs, and instead gain all focus on improving the tasks we know improve survival: CPR and defibrillation.

RECENT FINDINGS

Previous and recent cardiac arrest drug studies have been reviewed. These are mostly consisting of retrospective register data, some experimental data and a few new randomized trials. The alternative drug-free ALS concept is also discussed with relevant studies.

SUMMARY

There is currently no evidence to support any specific drugs during cardiac arrest. Good-quality CPR, early defibrillation and goal-directed postresuscitation care is more important. Healthcare systems should not prioritize implementation of unproven drugs before good quality of care can be documented. More drug studies are indeed required, and future research needs to incorporate better diagnostic tools to test more specific and tailored therapies that account for underlying causes and individual responsiveness.

Association of amplitude spectral area of the ventricular fibrillation waveform with survival of out-of-hospital ventricular fibrillation cardiac arrest

BACKGROUND

Previous investigations of out-of-hospital cardiac arrest (OHCA) have shown that the waveform characteristic amplitude spectral area (AMSA) can predict successful defibrillation and return of spontaneous circulation (ROSC) but has not been studied previously for survival.

OBJECTIVES

To determine whether AMSA computed from the ventricular fibrillation (VF) waveform is associated with pre-hospital ROSC, hospital admission, and hospital discharge.

METHODS

Adults with witnessed OHCA and an initial rhythm of VF from an Utstein style database were studied. AMSA was measured prior to each shock and averaged for each subject (AMSA-avg). Factors such as age, sex, number of shocks, time from dispatch to monitor/defibrillator application, first shock AMSA, and AMSA-avg that could predict pre-hospital ROSC, hospital admission, and hospital discharge were analyzed by logistic regression.

RESULTS

Eighty-nine subjects (mean age 62 ± 15 years) with a total of 286 shocks were analyzed. AMSA-avg was associated with pre-hospital ROSC (p = 0.003); a threshold of 20.9 mV-Hz had a 95% sensitivity and a 43.4% specificity. Additionally, AMSA-avg was associated with hospital admission (p < 0.001); a threshold of 21 mV-Hz had a 95% sensitivity and a 54% specificity and with hospital discharge (p < 0.001); a threshold of 25.6 mV-Hz had a 95% sensitivity and a 53% specificity. First-shock AMSA was also predictive of pre-hospital ROSC, hospital admission, and discharge. Time from dispatch to monitor/defibrillator application was associated with hospital admission (p = 0.034) but not pre-hospital ROSC or hospital discharge.

CONCLUSIONS

AMSA is highly associated with pre-hospital ROSC, survival to hospital admission, and hospital discharge in witnessed VF OHCA. Future studies are needed to determine whether AMSA computed during resuscitation can identify patients for whom continuing current resuscitation efforts would likely be futile.