Ventricular Fibrillation Waveform Research Articles

Prediction of Defibrillation Outcome by Ventricular Fibrillation Waveform Analysis: A Clinical Review

The most frequent initial rhythm in out-of-hospital witnessed cardiac arrest is ventricular fibrillation (VF) and electrical defibrillation is still the only effective therapy for the termination of this life-threatening cardiac arrhythmia. Even though earlier defibrillation is greatly emphasized during cardiopulmonary resuscitation (CPR), unnecessary or repetitive high energy defibrillations are associated with decreased post-resuscitation myocardial function. Optimizing the timing of defibrillation is of great importance in order to discriminate patients should receive immediate defibrillation versus alternate therapies such as CPR. Since characteristics of VF waveform changes over time and with CPR, which exhibit predictable ability of defibrillation success, quantitative analysis of VF waveform has the potential to guide defibrillation. This article reviewed methods developed for VF waveform analysis (including time domain, frequency domain, time-frequency domain, nonlinear analysis, and combination analysis techniques) and their performances for the prediction of defibrillation outcomes in clinical settings. The retrospective meta-analysis confirmed that VF waveform could predict the return of organized electrical activity, restoration of spontaneous circulation, and survival reliably. Additionally, predictors based on time-frequency and nonlinear methods were superior to other methods on the whole. However, no prospective studies have been performed to identify the optimal time of defibrillation utilizing VF waveform analysis until now. Therefore, the value of VF waveform analysis to guide clinical countershock management still needs further investigation.

Abstract 271: Detection of Ventricular Fibrillation with Continuing Chest Compression Using Adaptive Least Mean-Square Estimation of Cardiopulmonary Resuscitation Artifacts and Removal of the Artifacts

Introduction: Adequate perfusion during cardiopulmonary resuscitation (CPR) is crucial for successful defibrillation after ventricular fibrillation (VF), but the effects of chest compressions (CC) are compromised from interruptions due to rhythm analysis. A novel algorithm utilizing corrupted ECG signal to detect VF during continuing CC is developed. Method: VF waveforms were retrieved from automatic external defibrillator (AED) records of adult non-traumatic cardiac arrests. Ten CC artifacts were retrieved from arrests with asystole. Corrupted ECG was constructed by adding a scaled CC artifact to a VF with strength determined by signal-to-interference ratio (SIR). Using empirical mode decomposition, the fluctuation and frequency of CC were retrieved. VF was detected by subtracting the CC artifacts, modeled by least mean-square (LMS) algorithm, from ECG signals. Amplitude spectrum analysis (AMSA) was used to quantify the component with frequency range 0.7[[Unable to Display Character: ‐]]30 Hz. Result: One-hundred and fifty VF (36 successful and 114 unsuccessful defibrillation, p<0.01) were included. After removing CC artifacts with the algorithm, successful vs unsuccessful cases remained distinguished (p<0.05) in SIR ranged 0 to -9dB. The detected and the original VFs had similar AMSA with correlation of 0.98, 0.95, 0.93 and 0.86 when SIR were 0, -3, -6 and -9dB (p<0.001). The mean of difference between the detected VFs and the original VFs were 0.19, 0.56, 1.36 and 3.33 for SIR of 0, -3, -6 and -9dB, respectively, in Bland-Altman plot. Conclusions: The proposed adaptive algorithm using LMS could model CPR artifact without external signals, and effectively detect VF with continuing CC.

Abstract 303: End-Tidal CO2 Reactivity Is Associated with Defibrillation Success

Background: High quality cardiopulmonary resuscitation (CPR) appears to modulate ventricular fibrillation (VF) morphology and increase defibrillation success. However, VF waveform analysis has not proven to be clinically effective in guiding resuscitation. End-tidal CO2 (PetCO2) correlates well with cardiac perfusion during CPR. Objective: To evaluate PetCO2 as a predictor of defibrillation success. Methods: This was a prospective, observational study conducted at two urban academic hospitals with a combined total of 500 beds. Data regarding defibrillation attempts, CPR process, and PetCO2 were abstracted from monitor/defibrillators. All arrest victims with primary VF or ventricular tachycardia (VT) for whom CPR and PetCO2 data were available were included in this analysis. Patients with return of spontaneous circulation (ROSC) were compared to those without ROSC with regard to the change in PetCO2, mean and final compression rate and depth, and chest compression fraction (CCF). Results: A total of 32 patients were included in this analysis; 19 of these (59%) had ROSC. PetCO2 values prior to the final defibrillation attempt were 121% higher than initial PetCO2 values in patients with ROSC versus only 19% higher in patients without ROSC (p<0.01). Patients with and without ROSC were similar with regard to mean compression depth (2.5 vs. 2.7 inches, p=NS), final compression depth (2.7 vs. 2.8 inches, p=NS), mean compression rate (119/min for both, p=NS), final compression rate (121/min vs. 123/min, p=NS), mean CCF (83% vs. 85%, p=NS), and final CCF (83% vs. 91%). Conclusions: An increase in PetCO2 was associated with ROSC among inpatients with VF/VT arrest. No differences with regard to overall or final CPR process measures were observed. The “PetCO2 responsiveness” to CPR may reflect an underlying physiology that predicts defibrillation success.

Abstract 304: Comparing a Novel Stochastic Integrative Machine Learning Model with AMSA for Predicting Defibrillation Success

Introduction A computational model of defibrillation success, based on physiologic signals, would be highly useful in guiding therapy during cardiac arrest. The Amplitude Spectrum Area (AMSA) measure has been explored in a number of studies as a predictor of countershock success for ventricular fibrillation (VF). We have developed a novel non-linear stochastic method for a unified predictive model of Return Of Spontaneous Circulation (ROSC) and compare this model to AMSA. Methods Ninety sample pre-hospital VF Electrocardiogram (ECG) signals including defibrillation were classified as successful (34) or unsuccessful (56) through 10-fold cross validation. A novel integrative model was developed with Machine Learning and features extracted from non-linear stochastic characterization of the ECG signal. This method was subsequently compared with the AMSA method. We replicated the procedure published by AMSA creators and attempted to discern a threshold. The performance of both methods in predicting post-countershock ROSC was measured. Accuracy is calculated as the correctly predicted countershock outcomes divided by all samples in the database. ROC AUC is a measure of predictive model robustness. Results The novel integrative model performs real-time, short-term (7.8 second) analysis of the ECGs with prediction Accuracy and Receiver Operator Characteristic (ROC) Area Under the Curve (AUC) of 82.2% and 85%, respectively. Sensitivity was 85.3% and specificity was 80.3%. Using the AMSA methodology, no clear threshold for shock success could be identified for comparison. Employing information-gain based decision stump (or one-rule) for AMSA values yielded 64.6% accuracy and 60.9% ROC AUC. Sensitivity was 44.1% and Specificity was 77.2%. Conclusion We report multiple performance metrics for a novel integrative model as well as the previously described, Fourier Transform based, AMSA model for predicting defibrillation success. A stochastic and non-linear basis for signal characterization proves to be more adept in dealing with the known dynamic and non-stationary characteristics/morphology of the VF waveform. It significantly improves performance in analyzing the VF signal to predict countershock success.

Abstract 212: Can Return of Spontaneous Circulation Be Achieved Faster in a Resuscitation Algorithm that Directs the Duration of Postshock Chest Compressions According to the Preshock Value of the Amplitude-Spectral Area? A Study of Ventricular Fibrillation Cardiac Arrest in Normal Swine

Background: It has been previously demonstrated in a swine model of cardiac arrest due to ventricular fibrillation (VF) that a return of spontaneous circulation (ROSC) following a shock is predicted by the amplitude spectral area (AMSA), a quantitative measure of the VF waveform. These prior studies were a retrospective analysis of the VF waveform prior to shocks. We hypothesize that AMSA can be utilized in a prospective fashion as part of a resuscitation algorithm to direct the duration of post-shock chest compressions (CC). Specifically, we have tested whether such an AMSA-directed algorithm will shorten the time needed to achieve ROSC in normal swine. Methods: VF was induced electrically in normal swine and untreated for 10 minutes. Resuscitation was commenced with 1 minute of CC, resumption of mechanical ventilation, followed by the first shock and administration of epinephrine. Subsequently, animals were randomized to a traditional or waveform guided group. In the traditional group, all shocks were followed by 2 minutes of post-shock CC. In the waveform guided group, AMSA was measured prior to each shock and if AMSA>20 mV-Hz then post shock CC duration was shortened to 1 minute. ROSC was defined as a systolic aortic pressure greater than 50 mm Hg and pulse pressure > 20 mm Hg lasting at least one minute. Time to ROSC was compared with a Kruskal-Wallis rank test. AMSA was compared between groups using regression analysis with random effects to account for multiple shocks within the same animal. Results: A total of N=8 animals were assigned to the traditional group with 22 shocks delivered, and N=8 animals to the waveform guided group with 21 shocks delivered. Time to ROSC (from induction of VF) was 14.5 ±1.2 minutes in the traditional group and 13.5± 1.2 minutes in the waveform guided group, (P=0.2). There was a trend for higher AMSA values in the waveform guided group: 48 ± 15 versus 39 ± 16 mV-Hz (P=0.1). Conclusions: A resuscitation algorithm that tailors the duration of post-shock chest compressions to the value of AMSA is feasible. In this study of normal swine we found small, but non-significant, reductions in the times to achieve ROSC and higher values of AMSA in swine treated with a waveform guided approach.

Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning

BackgroundVentricular Fibrillation (VF) is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. We developed a unique approach of computational VF waveform analysis, with and without addition of the signal of end-tidal carbon dioxide (PetCO2), using advanced machine learning algorithms. We compare these results with those obtained using the Amplitude Spectral Area (AMSA) technique.MethodsA total of 90 pre-countershock ECG signals were analyzed form an accessible preshosptial cardiac arrest database. A unified predictive model, based on signal processing and machine learning, was developed with time-series and dual-tree complex wavelet transform features. Upon selection of correlated variables, a parametrically optimized support vector machine (SVM) model was trained for predicting outcomes on the test sets. Training and testing was performed with nested 10-fold cross validation and 6–10 features for each test fold.ResultsThe integrative model performs real-time, short-term (7.8 second) analysis of the Electrocardiogram (ECG). For a total of 90 signals, 34 successful and 56 unsuccessful defibrillations were classified with an average Accuracy and Receiver Operator Characteristic (ROC) Area Under the Curve (AUC) of 82.2% and 85%, respectively. Incorporation of the end-tidal carbon dioxide signal boosted Accuracy and ROC AUC to 83.3% and 93.8%, respectively, for a smaller dataset containing 48 signals. VF analysis using AMSA resulted in accuracy and ROC AUC of 64.6% and 60.9%, respectively.ConclusionWe report the development and first-use of a nontraditional non-linear method of analyzing the VF ECG signal, yielding high predictive accuracies of defibrillation success. Furthermore, incorporation of features from the PetCO2 signal noticeably increased model robustness. These predictive capabilities should further improve with the availability of a larger database.

Correlation between coronary perfusion pressure and quantitative ECG waveform measures during resuscitation of prolonged ventricular fibrillation

IntroductionThe ventricular fibrillation (VF) waveform is dynamic and predicts defibrillation success. Quantitative waveform measures (QWMs) quantify these changes. Coronary perfusion pressure (CPP), a surrogate for myocardial perfusion, also predicts defibrillation success. The relationship between QWM and CPP has been preliminarily explored. We sought to further delineate this relationship in our porcine model and to determine if it is different between animals with/without ROSC (return of spontaneous circulation). HypothesisA relationship exists between QWM and CPP that is different between animals with/without ROSC. MethodsUtilizing a prior experiment in our porcine model of prolonged out-of-hospital VF cardiac arrest, we calculated mean CPP, cumulative dose CPP, and percent recovery of three QWM during resuscitation before the first defibrillation: amplitude spectrum area (AMSA), median slope (MS), and logarithm of the absolute correlations (LAC). A random effects linear regression model with an interaction term CPP*ROSC investigated the association between CPP and percent recovery QWM and how this relationship changes with/without ROSC. ResultsFor 12 animals, CPP and QWM measures (except LAC) improved during resuscitation. A linear relationship existed between CPP and percent recovery AMSA (coefficient 0.27; 95%CI 0.23, 0.31; p<0.001) and percent recovery MS (coefficient 0.80; 95%CI 0.70, 0.90; p<0.001). A linear relationship existed between cumulative dose CPP and percent recovery AMSA (coefficient 2.29; 95%CI 2.0, 2.56; p<0.001) and percent recovery MS (coefficient 6.68; 95%CI 6.09, 7.26; p<0.001). Animals with ROSC had a significantly “steeper” dose–response relationship. ConclusionsThere is a linear relationship between QWM and CPP during chest compressions in our porcine cardiac arrest model that is different between animals with/without ROSC.

Abstract 240: AMSA Predicts Shock Success in Cardiac Arrest Patients with Different Transthoracic Impedance

Introduction: Amplitude spectrum area (AMSA), which is calculated from the Fourier transformation of ventricular fibrillation (VF) waveforms, has shown its ability in predicting the likelihood of successful defibrillation and monitoring the effectiveness of chest compression [1-3]. In the present study, we retrospectively evaluated the reliability of AMSA in predicting shock success over entire transthoracic impedance (TTI) range in pre-hospital cardiac arrest victims. Methods ECG recordings, along with TTI measurements between two shocking pads, were collected from multiple emergency medical services (EMS) in the USA through a regular field case submission program sponsored by ZOLL Medical Corporation. All the EMSs in this study use ZOLL AED which employs current-based impedance compensation technique. AMSA was calculated based on a 1024 point ECG window ending at 0.5 sec before each shock attempt. Shock success was defined as an organized rhythm that was present for a minimum of 30 seconds, started within 60 seconds after the shock, and had a rate of 40 beats per minute or greater. Results: A total of 599 patients with witnessed rhythm of VF were included in the analyses. Only the first shock (120J) was analyzed. As shown in Figure 1, AMSA was significantly higher for successful shocks as compared with failed shocks in different TTI ranges. With an AMSA threshold of 8 mVHz, a large amount of unnecessary shocks (79.3%, 92.4% and 98.1%) were avoided with accuracy of 61.5%, 65.4% and 62.2% when TTI was low, normal and high, respectively. Logistic regression indicated that both AMSA (O.R. 1.19) and TTI (O.R. 0.99) were independent predictors for shock success, though, AMSA was a positive predictor and TTI was a negative predictor for shock success. Conclusion: In this patient population, AMSA was confirmed to be significantly higher in the presence of successful defibrillation over the entire TTI range. AMSA therefore can reliably predict shock outcome at different TTI ranges.

Abstract 202: Predicting Successful Defibrillation with a New Algorithm Based on the Magnitude and Dominant Frequency of Ventricular Fibrillation Waveform

After prolonged ventricular fibrillation (VF), defibrillation after every 2 minutes of CPR, as currently recommended, not only often fails to return spontaneous circulation but also causes significant myocardial injury. This study has developed a new algorithm to predict the optimal time of defibrillation. We hypothesized that the product of the total absolute magnitudes and dominant frequency (PMD) will provide an equal or improved prediction of amplitude spectrum area (AMSA) in a pig model of CPR. Forty-eight domestic pigs weighing between 35 to 48 kg were utilized. The durations of untreated ventricular fibrillation (VF) were from 4 to 15 minutes. The electrical shock was delivered after 2 to 5 minutes of chest compression. Only the results of the first electrical shock were reported. The 5-sec strip of VF waveform prior to each electrical shock was captured to calculate PMD and AMSA. The formula calculating PMD is PMD= (|A 1 |+|A 2 |+¼+|A i |+¼+|A n |) * DF/K, where |A i | is the absolute amplitude of the i th time-sampled data. DF is the dominant frequency. K is a constant factor. For PMD calculation, the frequency range from 5 Hz to 25 Hz was selected to filter the signal, for AMSA calculation, the frequently-used frequency range is from 4 Hz to 48Hz. K =100 was used to calculate PMD. The perfusing rhythm was restored in 20 pigs after the electrical shock, PMD was 22.95±5.01 mV·Hz in resuscitated pigs and was 10.21±3.55 mV·Hz when electrical shock failed to restore circulation (p&lt;.001). AMSA values were 30.97±5.35 mV·Hz and 18.77±4.57 mV·Hz in resuscitated and non-resuscitated pigs, respectively (p&lt;.001). The differences in both of PMD and AMSA between resuscitated and non-resuscitated pigs were highly significant (p&lt;.001). For PMD, the calculated threshold was 16 mV·Hz when the sensitivity and specificity were 100% and 89%, respectively. For AMSA, the calculated threshold was 23 mV·Hz when the sensitivity and specificity were 100% and 86%, respectively. We concluded that the product of the total absolute magnitudes and dominant frequency (PMD) predicts the successful defibrillation during CPR as effectively as amplitude spectrum area (AMSA).

Beta-blockade causes a reduction in the frequency spectrum of VF but improves resuscitation outcome: A potential limitation of quantitative waveform measures

IntroductionMethods to identify appropriate treatments for the various stages of ventricular fibrillation (VF) involve differentiating groups of subjects who will respond to defibrillation with return of spontaneous circulation (ROSC) and those who require other therapies (e.g., CPR, drugs) prior to defibrillation. The use of quantitative waveform measures (QWM) which measure the frequency and fractal dimension of the VF electrocardiogram have shown success in predicting response to defibrillatory shock in animal models. Patients in cardiac arrest are often taking medications affecting adrenergic activity such as the beta blocker metoprolol and the combined alpha and beta blocker, labetalol. How this exposure might alter the QWM and ROSC rates is not known. HyothesisWe sought to determine how pretreatment with adrenergic agents alters two QWM measures, the amplitude spectrum area (AMSA) and the detrended fluctuation analysis (DFA). We also examined how these medications alter the probability of ROSC after shock. MethodsA swine model of ischemically induced VF cardiac arrest was used in which metoprolol and labetalol were administered prior to VF onset. 30 swine were randomly assigned to three groups of 10; control, metoprolol and labetalol. They were anesthetized, intubated and given the appropriate study drug. A balloon catheter was placed in the LAD coronary artery and inflated until VF occurred. ECG was recorded at 1000Hz for 7min of untreated VF. Closed chest compressions were then begun and after 1min a 200J shock was delivered. Resuscitation was continued with repeat defibrillation shocks as indicated for 15min or until ROSC was achieved (defined: systolic BP>60 for 10min). The Fourier frequency spectra, AMSA and DFA measures from VF onset to 7min were calculated using custom MATLAB routines. The QWM were compared over the electrical and circulatory phases of VF using generalized estimating equations. The rates of ROSC in the three groups were compared using relative risk measures. ResultsAll 10 control animals fibrillated after coronary occlusion, 8 metoprolol and 7 labetalol animals fibrillated.The frequency spectrum in metoprolol treated animals demonstrated a reduction in mean frequencies from 1 to 3min (electrical phase) and from 3 to 7min (circulatory phase). Labetalol produced an even greater reduction in frequencies in these intervals. The decline in AMSA was similar in all three groups over the first 3min. From 3 to 7min the metoprolol group was significantly lower than the control group (p<0.001) and the labetalol group was lower still (p<0.001). The DFA demonstrated little difference between the control and metoprolol groups, but showed a linear increase over 7min in the labetalol group (p<0.001 vs compared to control and metoprolol groups).ROSC was noted in 2/10 in the control group, 7/8 in metoprolol group and 2/7 in the labetalol group. The frequentist analysis of ROSC showed a relative risk (RR) of ROSC of 4.4 when comparing control to metoprolol animals and 1.4 comparing control to labetalol animals. DiscussionMetoprolol results in a reduction in frequencies in the Fourier spectrum of VF as compared with controls. There is a further decrease in frequencies with labetalol. The AMSA reflects this reduction in frequencies with lower AMSA values from 3 to 7min of VF. The DFA demonstrates consistent changes with labetalol treated animals over the 7min, but the metoprolol treated animals do not differ from the controls. The marked improvement in ROSC seen with metoprolol (RR 4.4) is unexpected and is not seen in labetalol treated animals. Adrenergic blockade prior to VF induction affects quantitative measures of the VF waveform and may limit the ability of such measures to predict downtime or defibrillation outcome.

Toward the nature of ventricular fibrillation from electrophysiological point of view

In 1997 H. U. Strohmenger et al. have published a paper in Chest journal that documented the predictive property of Fast Fourier Transform (FFT) parameters including Median Frequency (MF), Dominant Frequency (DF), Edge Frequency (EF), and amplitude derived from Ventricular Fibrillation (VF) waveform [1]. One of the important aspects of their work was the measurement of FFT parameters for different lengths of VF Electrocardiogram (ECG). Although the deviation of long length ECG waveform FFT parameters from the short ones – here referred to as Fourier window deviation (FWD) – was not compared statistically between successful and unsuccessful groups, the study of this herein proposed index may yield appealing information about VF behaviour.

Utility of the Ventricular Fibrillation Waveform to Predict a Return of Spontaneous Circulation and Distinguish Acute From Post Myocardial Infarction or Normal Swine in Ventricular Fibrillation Cardiac Arrest

In cardiac arrest, the ventricular fibrillation (VF) waveform, particularly amplitude spectral area (AMSA) and slope, predicts the return of spontaneous circulation (ROSC), but it is unknown whether the predictive utility differs in an acute myocardial infarction (MI), prior MI, or normal myocardium and if the waveform can distinguish the underlying myocardial state. We hypothesized that in a swine model of VF cardiac arrest, AMSA and slope predict ROSC after a shock independent of substrate and distinguish an acute from nonacute MI state. MI was induced by occlusion of the left anterior descending artery. Post MI swine recovered for a 2-week period before induction of VF. VF was untreated for 8 minutes in 10 acute MI, 10 post MI, and 10 control swine. AMSA and slope predicted ROSC after a shock independent of myocardial state. For AMSA >31 mV-Hz, the odds ratio was 62 (P≤0.001) compared with AMSA <19 mV-Hz. For slope >3.1 mV/s, odds ratio was 52 (P≤0.001) compared with slope <1.8 mV/s. With chest compressions, AMSA and slope were significantly lower for acute MI swine compared with control swine, whereas in post MI swine the waveform characteristics were similar to control swine. In particular, for an AMSA >33.5 mV-Hz, the sensitivity to identify an acute from nonacute (control or post MI) state was 83%. In a swine model of VF cardiac arrest, AMSA and slope predict ROSC independent of myocardial substrate. Furthermore, with chest compressions, the VF waveform evolves differently and may offer a means to distinguish an acute MI.

Predictors of resuscitation in a swine model of ischemic and nonischemic ventricular fibrillation cardiac arrest: Superiority of amplitude spectral area and slope to predict a return of spontaneous circulation when resuscitation efforts are prolonged*

We have demonstrated that a return of spontaneous circulation in the first 3 mins of resuscitation in swine is predicted by ventricular fibrillation waveform (amplitude spectral area or slope) when untreated ventricular fibrillation duration or presence of acute myocardial infarction is unknown. We hypothesized that in prolonged resuscitation efforts that return of spontaneous circulation immediately after a second or later shock with postshock chest compression is independently predicted by end-tidal CO2, coronary perfusion pressure, and ventricular fibrillation waveform measured before that shock in a swine model of ischemic and nonischemic ventricular fibrillation arrest. Animal intervention study with comparison to a control group. University animal laboratory. Twenty swine. Myocardial infarction was induced by steel plug occlusion of the left anterior descending coronary artery. Ventricular fibrillation was untreated for 8 mins in normal swine (n=10) and acute myocardial infarction swine (n=10). End-tidal CO2, coronary perfusion pressure, and ventricular fibrillation waveform characteristics of amplitude spectral area and slope were analyzed before second or later shocks. For an amplitude spectral area>35 mV-Hz, the odds ratio for achieving return of spontaneous circulation after that shock was 72 (95% confidence interval, 3.8-1300; p=.004) compared with an amplitude spectral area<28 mV-Hz and with an area under the receiver operator characteristic curve of 0.86. For slope>3.6 mV/s, the odds ratio for achieving return of spontaneous circulation was 36 (95% confidence interval, 2.7-480; p=.007) compared with slope<2.72 mV/s with an area under the curve of 0.86. End-tidal CO2 and coronary perfusion pressure were not predictive of return of spontaneous circulation after a shock, although coronary perfusion pressure was significantly related to both amplitude spectral area (p<.001) and slope (p<.001). : In prolonged untreated ventricular fibrillation arrest, the waveform characteristics of amplitude spectral area and slope predict the attainment of return of spontaneous circulation with a second or later shock. This has implications for the ideal means to customize the timing of shocks and chest compressions when return of spontaneous circulation is not promptly obtained.

Quantitative measures of the ventricular fibrillation waveform and resuscitation outcome*

Quantitative measures of the ventricular fibrillation waveform and resuscitation outcome*

Prompt prediction of successful defibrillation from 1-s ventricular fibrillation waveform in patients with out-of-hospital sudden cardiac arrest

Ventricular fibrillation (VF) is a common cardiac arrest rhythm that can be terminated by electrical defibrillation. During cardiopulmonary resuscitation, there is a strong need for a prompt and reliable predictor of successful defibrillation because myocardial damage can result from repeated futile defibrillation attempts. Continuous wavelet transform (CWT) provides excellent time and frequency resolution of signals. The purpose of this study was to evaluate whether features based on CWT could predict successful defibrillation. VF electrocardiogram (ECG) waveforms stored in ambulance-located defibrillators were collected. Predefibrillation waveforms were divided into 1.0- or 5.12-s VF waveforms. Indices in frequency domain or nonlinear analysis were calculated on the 5.12-s waveform. Simultaneously, CWT was performed on the 1.0-s waveform, and total low-band (1-3Hz), mid-band (3-10Hz), and high-band (10-32Hz) energy were calculated. In 152 patients with out-of-hospital cardiac arrest, a total of 233 ECG predefibrillation recordings, consisting of 164 unsuccessful and 69 successful episodes, were analyzed. Indices of frequency domain analysis (peak frequency, centroid frequency, and amplitude spectral area), nonlinear analysis (approximate entropy and Hurst exponent, detrended fluctuation analysis), and CWT analysis (mid-band and high-band energy) were significantly different between unsuccessful and successful episodes (P<0.01 for all). However, logistic regression analysis showed that centroid frequency and total mid-band energy were effective predictors (P<0.01 for both). Energy spectrum analysis based on CWT as short as a 1.0-s VF ECG waveform enables prompt and reliable prediction of successful defibrillation.

Comparison of VF waveform characteristics and EMS response time as predictors of out-of-hospital cardiac arrest outcomes

Purpose: We compared characteristics of the ventricular fibrillation (VF) waveform and Emergency Medical Service (EMS) response time in their ability to predict patient outcomes from out-of-hospital cardiac arrest (OOHCA). Although some studies have shown EMS response time to be a strong predictor of patient outcome, it is difficult to measure and may not accurately reflect patient down-time.1–4

Does obesity affect defibrillation parameters or outcomes among out-of-hospital cardiac arrest patients presenting in ventricular fibrillation?

Purpose: A limited amount of data has been published regarding the effects of body weight on out-of-hospital cardiac arrest.1,2 We sought to describe the effects of obesity on ventricular fibrillation (VF) waveform characteristics, defibrillation, and outcomes.

Initial ventricular fibrillation waveform characteristics and outcomes among EMS-witnessed cardiac arrests

Introduction: The 2005 American Heart Association Guidelines recommend defibrillation “as soon as possible” for emergency medical service (EMS)-witnessed out-of-hospital cardiac arrest (OOHCA).1 In such cases, ventricular fibrillation (VF) will likely have little time to deplete myocardial substrates or experience significant wavebreak, making it amenable to immediate defibrillation. We sought to describe the initial characteristics of VF waveforms for EMS-witnessed arrests and to relate these to patient outcome.

Part 6: Defibrillation

Part 6: Defibrillation

Part 4: CPR Overview

Cardiopulmonary resuscitation (CPR) is a series of lifesaving actions that improve the chance of survival following cardiac arrest.1 Although the optimal approach to CPR may vary, depending on the rescuer, the victim, and the available resources, the fundamental challenge remains: how to achieve early and effective CPR. Given this challenge, recognition of arrest and prompt action by the rescuer continue to be priorities for the 2010 AHA Guidelines for CPR and ECC. This chapter provides an overview of cardiac arrest epidemiology, the principles behind each link in the Chain of Survival, an overview of the core components of CPR (see Table 1), and the approaches of the 2010 AHA Guidelines for CPR and ECC to improving the quality of CPR. The goal of this chapter is to integrate resuscitation science with real-world practice in order to improve the outcomes of CPR. View this table: Table 1. Summary of Key BLS Components for Adults, Children and Infants Despite important advances in prevention, cardiac arrest remains a substantial public health problem and a leading cause of death in many parts of the world.2 Cardiac arrest occurs both in and out of the hospital. In the US and Canada, approximately 350 000 people/year (approximately half of them in-hospital) suffer a cardiac arrest and receive attempted resuscitation.3,–,7 This estimate does not include the substantial number of victims who suffer an arrest without attempted resuscitation. While attempted resuscitation is not always appropriate, there are many lives and life-years lost because appropriate resuscitation is not attempted. The estimated incidence of EMS-treated out-of-hospital cardiac arrest in the US and Canada is about 50 to 55/100 000 persons/year and approximately 25% of these present with pulseless ventricular arrhythmias.3,8 The estimated incidence of in-hospital cardiac arrest is 3 to 6/1000 admissions4,– …