Estimating the duration of ventricular fibrillation

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.

Insights Into the Spatiotemporal Patterns of Complexity of Ventricular Fibrillation by Multilead Analysis of Body Surface Potential Maps

Background

Ventricular fibrillation (VF) is the main cause of sudden cardiac death, but its mechanisms are still unclear. We propose a noninvasive approach to describe the progression of VF complexity from body surface potential maps (BSPMs).

Methods

We mapped 252 VF episodes (16 ± 10 s) with a 252-electrode vest in 110 patients (89 male, 47 ± 18 years): 50 terminated spontaneously, otherwise by electrical cardioversion (DCC). Changes in complexity were assessed between the onset (“VF start”) and the end (“VF end”) of VF by the nondipolar component index (NDI_BSPM), measuring the fraction of energy nonpreserved by an equivalent 3D dipole from BSPMs. Higher NDI reflected lower VF organization. We also examined other standard body surface markers of VF dynamics, including fibrillatory wave amplitude (A_BSPM), surface cycle length (BsCL_BSPM) and Shannon entropy (ShEn_BSPM). Differences between patients with and without structural heart diseases (SHD, 32 vs. NSHD, 78) were also tested at those stages. Electrocardiographic features were validated with simultaneous endocardium cycle length (CL) in a subset of 30 patients.

Results

All BSPM markers measure an increase in electrical complexity during VF (p < 0.0001), and more significantly in NSHD patients. Complexity is significantly higher at the end of sustained VF episodes requiring DCC. Intraepisode intracardiac CL shortening (VF start 197 ± 24 vs. VF end 169 ± 20 ms; p < 0.0001) correlates with an increase in NDI, and decline in surface CL, f-wave amplitude, and entropy (p < 0.0001). In SHD patients VF is initially more complex than in NSHD patients (NDI_BSPM, p = 0.0007; ShEn_BSPM, p < 0.0001), with moderately slower (BsCL_BSPM, p = 0.06), low-amplitude f-waves (A_BSPM, p < 0.0001). In this population, lower NDI (p = 0.004) and slower surface CL (p = 0.008) at early stage of VF predict self-termination. In the NSHD group, a more abrupt increase in VF complexity is quantified by all BSPM parameters during sustained VF (p < 0.0001), whereas arrhythmia evolution is stable during self-terminating episodes, hinting at additional mechanisms driving VF dynamics.

Conclusion

Multilead BSPM analysis underlines distinct degrees of VF complexity based on substrate characteristics.

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.

Frequency Variation of Ventricular Fibrillation May Help Predict Successful Defibrillation in a Rat Model of Cardiac Arrest

BACKGROUND

To evaluate whether the frequency variation of ventricular fibrillation (VF) helps to predict successful defibrillation in a rat model of cardiac arrest.

METHODS

VF was induced in rats followed by cardiopulmonary resuscitation and then defibrillation. The electrocardiographic signals of 30 rats with first-shock success were obtained from our previous animal experiments, and 300 rats without first-shock success were selected as control. The VF waveform immediately before the first defibrillation was analyzed.

RESULTS

Eighty-eight percentages of the frequency variations of an electrocardiogram (ECG) record falling in the range -9.5-9.5 Hz was selected with sensitivity of 0.8, specificity of 0.583, and area under curve (AUC) of 0.708. Compared with amplitude spectrum area (AMSA) (sensitivity = 0.767, specificity= 0.547, and AUC = 0.678), combining frequency variation and AMSA significantly increases the predictability with sensitivity of 0.933, specificity of 0.493, and AUC of 0.732 (p = 0.005).

CONCLUSIONS

The frequency variation of VF may serve a useful parameter to predict defibrillation success.

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.

Intraarrest rhythms and rhythm conversion in asphyxial cardiac arrest

OBJECTIVES

The objective was to analyze the cardiac arrest rhythms presenting during asphyxial cardiac arrest (ACA).

METHODS

Asphyxial cardiac arrest was induced in 30 Landrace large white piglets, aged 12 to 15 weeks and with a mean (±SD) weight of 20 (±2) kg. After the onset of cardiac arrest, the animals were left untreated for 4 minutes, after which cardiopulmonary resuscitation was commenced. Heart rhythms were monitored from the onset of asphyxia until return of spontaneous circulation or death.

RESULTS

After endotracheal tube clamping and prior to cardiac arrest, normal sinus rhythm was noted in 14 animals, atrial fibrillation in two animals, Mobitz II atrioventricular block in 10 animals, and third-degree atrioventricular block in four animals. At the onset of cardiac arrest, seven animals had ventricular fibrillation (VF), two had asystole, and 21 had pulseless electrical activity (PEA). During the 4-minute period of untreated arrest, however, significant changes in the monitored rhythm were noted; at the end of the fourth minute, 19 animals had VF, two animals had asystole, and nine animals had PEA.

CONCLUSIONS

The most common rhythm after 4 minutes of untreated ACA was VF, while in 57% of animals, PEA was spontaneously converted to VF during the cardiac arrest interval.

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.

Electrocardiogram frequency change by extracorporeal blood perfusion in a swine ventricular fibrillation model

Background

Extracorporeal cardiopulmonary resuscitation (ECPR) refers to the application of extracorporeal blood circulation with oxygenation as a resuscitation tool. The objective of this study is to observe the frequency component changes in the electrocardiogram (ECG) by ECPR during prolonged ventricular fibrillation (VF).

Methods

Six swine were prepared as a VF model. Extracorporeal blood circulation with a pulsatile blood pump and oxygenator was set up for the model. ECG signals were measured for 13 min during VF and analyzed using frequency analysis methods. The median frequency (MF), dominant frequency (DF), and amplitude spectrum area (AMSA) were calculated from a spectrogram obtained using short-time Fourier transform (STFT).

Results

MF decreased from 11 Hz at the start to 9 Hz at 2 min after VF and then increased to 11 Hz at 4.5 min after VF. DF started at 7 Hz and increased to 11 Hz within the first min and decreased to 9 Hz at 2 min, then increased to 12 Hz at 4.5 min after VF. Both frequency components decreased gradually from 4.5 min until 10 min after VF. After the oxygenated blood perfusion was initiated, both MF and DF increased remarkably and exceeded 12 and 14 Hz, respectively. Similarly, AMSA decreased gradually for the first 10 min, but increased remarkably and varied beyond 13 mV∙Hz after the oxygenated blood supply started. Remarkable frequency increases in ECG due to the oxygenated blood perfusion during ECPR were observed in the swine VF model.

Conclusions

The ECG frequency analysis during ECPR can give the resuscitation provider important information about the cardiac perfusion status and the appropriateness of the ECPR setup as well as the prediction of defibrillation success.

Amplitude Changes during Ventricular Fibrillation: A Mechanistic Insight

Introduction: Clinically in ventricular fibrillation (VF), ECG amplitude, and frequency decrease as ischemia progresses and predict defibrillation success. In vitro ECG amplitude declines without ischemia, independent of VF frequencies. This study examines the contribution of cellular electrical activity and global organization to ECG amplitude changes during VF. Methods and Results: Rabbit hearts were Langendorff-perfused (40 mL/min, Tyrode’s solution) and loaded with RH237. During VF, ECG, and epicardial optical action potentials were recorded (photodiode array; 256 sites, 15 mm × 15 mm). After 60 s of VF, perfusion was either maintained, global ischemia produced by low-flow (6 mL/min), or solution [K⁺]_o raised to 8 mM. Peak-to-peak amplitude was determined for all signals. During VF, in control, ECG amplitude decreased to a steady-state (∼57% baseline), whereas in low-flow steady-state was not reached with the amplitude continuing to fall to 33% of baseline by 600 s. Optically, LV amplitude declined more than RV, reaching significance in control (LV vs. RV; 33 ± 5 vs. 63 ± 8%, p < 0.01). During VF in 8 mM [K⁺]_o, amplitude changes were more complex; ECG amplitude increased with time (105 ± 13%), whilst LV amplitude decreased (60 ± 15%, p < 0.001). Microelectrode studies showed amplitude reduction in control and 8 mM [K⁺]_o (to ∼79 and ∼93% baseline, respectively). Evaluation of electrical coordination by cross-correlation of optical signals showed as VF progressed coordination reduced in control (baseline 0.36 ± 0.02 to 0.28 ± 0.003, p < 0.01), maintained in low-flow (0.41 ± 0.03 to 0.37 ± 0.005, p = NS) and increased in 8 mM [K⁺]_o (0.36 ± 0.02 to 0.53 ± 0.08, p < 0.05). Conclusion: ECG amplitude decline in VF is due to a combination of decreased systolic activation at the cellular level and increased desynchronization of inter-cellular electrical activity.

Slowing of Electrical Activity in Ventricular Fibrillation is Not Associated with Increased Defibrillation Energies in the Isolated Rabbit Heart

Prolonged out-of-hospital ventricular fibrillation (VF) arrests are associated with reduced ECG dominant frequency (DF) and diminished defibrillation success. Partial reversal of ischemia increases ECG DF and improves defibrillation outcome. We have investigated the metabolic components of ischemia responsible for the decline in ECG DF and defibrillation success. Isolated Langendorff-perfused rabbit hearts were loaded with the voltage-sensitive dye RH237. Using a photodiode array, epicardial membrane potentials were recorded at 252 sites (15 mm × 15 mm) on the anterior surface of the left and right ventricles. Simultaneously, a global ECG was recorded. VF was induced by burst pacing, and after 60s, perfusion was either reduced to 6 ml/min or the perfusate composition changed to impose hypoxia (95% N(2)/5% CO(2)), pH 6.7 (80% O(2)/20% CO(2)), or hyperkalemia (8 mM). Using fast Fourier transform, power spectra were created from the optical signals and the global ECG. The optical power spectra were summated to give a global power spectrum (pseudoECG). At 600 s the minimum defibrillation voltage (MDV) was determined by step-up protocol. During VF, the ECG and pseudoECG DF were reduced by low-flow ischemia (9.0 ± 1.0 Hz, p < 0.01, n = 5) and raised [K(+)](o) (12.2 ± 1.3 Hz, p < 0.05, n = 7) compared to control (19.2 ± 1.5 Hz, n = 20), but were unaffected by acidic pH(o) (16.7 ± 1.1 Hz, n = 11) and hypoxia (14.0 ± 1.2 Hz, n = 10). In contrast, the MDV was raised by acidic pH (156.1 ± 26.4 V, p < 0.001) and hypoxia (154.1 ± 22.1 V, p < 0.01) compared to control (65.6 ± 2.3 V), but comparable changes were not observed in low-flow ischemia (61.0 ± 0.5 V) or raised [K(+)](o) (56 ± 3 V). In summary, different metabolites are responsible for the reduction in DF and the increase in defibrillation energy during ischemic VF.

Detrended fluctuation analysis predicts successful defibrillation for out-of-hospital ventricular fibrillation cardiac arrest

AIMS

Repeated failed shocks for ventricular fibrillation (VF) in out-of-hospital cardiac arrest (OOHCA) can worsen the outcome. It is very important to rapidly distinguish between early and late VF. We hypothesised that VF waveform analysis based on detrended fluctuation analysis (DFA) can help predict successful defibrillation.

METHODS

Electrocardiogram (ECG) recordings of VF signals from automated external defibrillators (AEDs) were obtained for subjects with OOHCA in Taipei city. To examine the time effect on DFA, we also analysed VF signals in subjects who experienced sudden cardiac death during Holter study from PhysioNet, a publicly accessible database. Waveform parameters including root-mean-squared (RMS) amplitude, mean amplitude, amplitude spectrum analysis (AMSA), frequency analysis as well as fractal measurements including scaling exponent (SE) and DFA were calculated. A defibrillation was regarded as successful when VF was converted to an organised rhythm within 5s after each defibrillation.

RESULTS

A total of 155 OOHCA subjects (37 successful and 118 unsuccessful defibrillations) with VF were included for analysis. Among the VF waveform parameters, only AMSA (7.61+/-3.30 vs. 6.30+/-3.13, P=0.028) and DFAalpha2 (0.38+/-0.24 vs. 0.49+/-0.24, P=0.013) showed significant difference between subjects with successful and unsuccessful defibrillation. The area under the curves (AUCs) for AMSA and DFAalpha2 was 0.63 (95% confidence interval (CI)=0.52-0.73) and 0.65 (95% CI=0.54-0.75), respectively. Among the waveform parameters, only DFAalpha2, SE and dominant frequency showed significant time effect.

CONCLUSIONS

The VF waveform analysis based on DFA could help predict first-shock defibrillation success in patients with OOHCA. The clinical utility of the approach deserves further investigation.

Effect of global ischemia and reperfusion during ventricular fibrillation in myopathic human hearts

The effect of lack of global coronary perfusion on myocardial activation rate, wavebreak, and its temporal progression during human ventricular fibrillation (VF) is not known. We tested the hypothesis that global myocardial ischemia decreases activation rate and spatiotemporal organization during VF in myopathic human hearts, while increasing wavebreak, and that a short duration of reperfusion can restore these spatiotemporal changes to baseline levels. The electrograms were acquired during VF in a human Langendorff model using global mapping consisting of two 112-electrode arrays placed on the epicardium and endocardium simultaneously. We found that global myocardial ischemia results in slowing of the global activation rate (combined endo and epi), from 4.89+/-0.04 Hz. to 3.60+/-0.04 Hz. during the 200 s of global ischemia (no coronary flow) (P<0.01) in eight myopathic hearts. Two minutes of reperfusion contributed to reversal of the slowing with activation rate value increasing close to VF onset (4.72+/-0.04 Hz). In addition, during the period of ischemia, an activation rate gradient between the endocardium (3.76+/-0.06 Hz) and epicardium (3.45+/-0.06 Hz) was observed (P<0.01). There was a concomitant difference in wavebreak index (that provides a normalized parameterization of phase singularities) between the epicardium (11.29+/-2.7) and endocardium (3.25+/-2.7) during the 200 s of ischemia (P=0.02). The activation rate, gradient, and wavebreak changes were reversed by short duration (2 min) of reperfusion. Global myocardial ischemia of 3 min leads to complex spatiotemporal changes during VF in myopathic human hearts; these changes can be reversed by a short duration of reperfusion.

Electrocardiogram waveforms for monitoring effectiveness of chest compression during cardiopulmonary resuscitation

BACKGROUND

Newer guidelines address the importance of effective chest compressions, citing evidence that this primary intervention is usually suboptimally performed during cardiopulmonary resuscitation. We therefore sought a readily available option for monitoring the effectiveness of chest compressions, specifically using the electrocardiogram.

METHODS AND RESULTS

Ventricular fibrillation was induced by coronary artery occlusion and untreated for 5 mins. Male domestic pigs weighing 40 +/- 2 kg were randomized to optimal or suboptimal chest compressions after onset of ventricular fibrillation. Optimal depth of mechanical compression in six animals was defined as a decrease of 25% in anterior posterior diameter of the chest during compression. Suboptimal compression, also in six animals, was defined as a decrease of 17.5% in anterior posterior diameter. For each group, the chest compressions were maintained at a rate of 100 per min. After 3 mins of chest compression, defibrillation was attempted with a 150-J biphasic shock. All animals had return of spontaneous circulation after optimal compressions. This contrasted with suboptimal compressions, after which none of the animals had return of spontaneous circulation. Amplitude spectrum area values, representing the electrocardiographic amplitude frequency spectral area computed from conventional precordial leads, like coronary perfusion pressure and end tidal PCO2, were predictive of outcomes.

CONCLUSION

The effectiveness of chest compressions was reflected in the amplitude spectrum area values. Accordingly, the amplitude spectrum area predictor may be incorporated in current automated external defibrillators to monitor and prompt the effectiveness of chest compression during cardiopulmonary resuscitation.

Estimation of the duration of ventricular fibrillation using ECG single feature analysis

The duration of untreated ventricular fibrillation (VF) is of paramount importance for CPR success. Moreover, therapeutic interventions taking into account the interval between cardiac arrest onset and initiation of CPR improve outcome. This study was performed to investigate whether VF feature analysis could be used to estimate the duration of VF in patients with out-of-hospital cardiac arrest. Demographic data recorded according to the Utstein guidelines and ECG recordings of 376 cardiac arrest patients from three European areas were analysed. Ten features in the time and frequency domain derived from different sub-bands of the initial VF ECG (n=127) were evaluated. The correlation between VF ECG features and cardiac arrest times was investigated using Pearson's correlation coefficient in a subset of 40 patients with reliably estimated downtimes and artefact-free initial VF tracings. No significant correlation (p<.05) between any of the VF ECG features and downtime could be found. The duration of cardiac arrest could not be estimated reliably from human VF ECG single feature analysis.

Analysis of the ventricular fibrillation waveform in refibrillation

OBJECTIVES

Frustrating outcomes are driving investigation of alternative resuscitation protocols. Previous analysis of the ventricular fibrillation (VF) waveform has focused on guiding whether to shock immediately or to delay for delivery of cardiopulmonary resuscitation in the case of presenting VF. The same issues emerge in the case of refibrillation.

MEASUREMENTS AND MAIN RESULTS

All cases of witnessed VF cardiac arrest in the Rochester, MN, area in a 9-yr period were analyzed. Rochester rescuers employed an early defibrillation protocol during the study period. A summary measure of the VF waveform before the shock delivered in 35 incidents of refibrillation was compared with the time elapsed from the initial shock, the intervening electrocardiographic rhythm, ambulance response time, and call-to-shock time for prediction of early return of spontaneous circulation and of neurologically intact survival. VF waveform analysis separated patients with good outcomes when treated with early defibrillation of refibrillation from those without good outcomes more clearly than other predictors.

CONCLUSIONS

Analysis of VF waveform offers promise for real-time guidance of resuscitation efforts on the basis of individual patient characteristics, in refibrillation and in the initial shock. It has advantages over guidance based on individual or aggregate system response times.

Ventricular fibrillation frequency characteristics and time evolution in piglets: a developmental study

BACKGROUND

Derived variables of ventricular fibrillation, such as the frequency distribution by fast Fourier transformation and its evolution over time, have been used to determine the optimum timing for defibrillation. We hypothesized that these frequency variables would differ among neonatal, young child and older child populations due to cardiac developmental and size differences. Such differences may have important implications for developing defibrillation algorithms for pediatric patients and for extrapolating adult defibrillation algorithms to children in VF.

METHODS

Ventricular fibrillation was induced and recorded for 6 min in 4 kg (n = 11), 14 kg (n = 10), and 24 kg (n = 16) piglets, corresponding to neonatal, young child and older children. Mean, median, and dominant frequencies were computed in 30 s intervals and compared among weight classes.

RESULTS

All frequency variables in all weight groups showed first a decline at 1.25-1.75 min, followed by a gradual rise and plateau. There were significant differences for mean, median and dominant frequencies among weight classes. Specifically, 14 kg piglets showed higher frequency variables overall with a time evolution that was different from that of 4 and 24 kg piglets. Mean frequency showed the most stable time evolution with the least moment-to-moment variability.

CONCLUSION

The frequency waveform characteristics and time course are somewhat different in 14 kg piglets compared with 4 and 24 kg piglets. If similar differences are demonstrable among children of different weights and ages, AEDs designed to determine optimal timing of defibrillation shocks in adults by frequency waveform characteristics may require modification for use in children with VF.

Dynamic nature of electrocardiographic waveform predicts rescue shock outcome in porcine ventricular fibrillation

STUDY OBJECTIVE

Survival decreases with duration of ventricular fibrillation, and it is possible that failed rescue shocks increase myocardial damage. Structure in the ECG signal during ventricular fibrillation can be quantified by using the scaling exponent, a dimensionless measure that correlates with ventricular fibrillation duration. This study examined whether the scaling exponent could predict rescue shock success and whether unsuccessful rescue shocks altered the structure of the ventricular fibrillation waveform and the responsiveness to subsequent rescue shocks.

METHODS

Ventricular fibrillation was electrically induced in 44 anesthetized swine, which were randomly assigned to receive 70-J biphasic rescue shocks at 2, 4, 6, 8, or 10 minutes. If rescue shocks failed, up to 2 subsequent rescue shocks were performed at 2-minute intervals. The scaling exponent was calculated at 1-second intervals from ECG to quantify the organization of the ventricular fibrillation waveform.

RESULTS

A total of 92 rescue shocks were delivered, of which 23 successfully converted ventricular fibrillation to an organized rhythm (immediate success). After these 23 rescue shocks, 14 swine sustained organized rhythms for more than 30 seconds (sustained success). Lower scaling exponent values were associated with increased probability of successful rescue shocks. Receiver operating characteristic curves had an area under the curve of 0.86 for immediate rescue shock success and 0.93 for sustained rescue shock success. Failed rescue shocks increased the rate of scaling exponent increase over time but did not appear to affect subsequent rescue shock success when the scaling exponent was taken into account.

CONCLUSION

Highly deterministic ventricular fibrillation, reflected by a low scaling exponent, predicted rescue shock success regardless of antecedent failed rescue shocks. In addition, unsuccessful rescue shocks might decrease post-rescue shock ventricular fibrillation waveform organization.

Analysing the ventricular fibrillation waveform

The surface electrocardiogram associated with ventricular fibrillation has been of interest to researchers for some time. Over the last few decades, techniques have been developed to analyse this signal in an attempt to obtain more information about the state of the myocardium and the chances of successful defibrillation. This review looks at the implications of analysing the VF waveform and discusses the various techniques that have been used, including fast Fourier transform analysis, wavelet transform analysis and mathematical techniques such as chaos theory.

Predicting the success of defibrillation by electrocardiographic analysis

BACKGROUND

We investigated an electrocardiographic signal analysis technique for predicting whether an electrical shock would reverse ventricular fibrillation (VF) in an effort to minimize the damaging effects of repetitive shocks during CPR.

METHODS AND RESULTS

An established model of CPR was utilized. VF was electrically induced in anesthetized 40 kg domestic pigs. Defibrillation was attempted after either 4 or 7 min of untreated VF. Failing to reverse VF, a 1 min interval of precordial compression and mechanical ventilation preceded each subsequent defibrillation attempt. The amplitude frequency spectrum of digitally filtered VF wavelets was computed with Fourier analysis during uninterrupted precordial compression from conventional right infraclavicular and left apical electrodes. Of a total of 34 electrical defibrillation attempts, 24 animals were restored to spontaneous circulation (ROSC). An amplitude spectrum analysis (AMSA) value of 21 mV Hz had a negative predictive value of 0.96 and a positive predictive value of 0.78.

CONCLUSIONS

AMSA predicted when an electrical shock failed to restore spontaneous circulation during CPR with a high negative predictive value. This method potentially fulfills the need for minimizing ineffective defibrillation attempts and their attendant adverse effects on the myocardium.

The predictive value of ventricular fibrillation electrocardiogram signal frequency and amplitude variables in patients with out-of-hospital cardiac arrest

We evaluated ventricular fibrillation frequency and amplitude variables to predict successful countershock, defined as pulse-generating electrical activity. We also elucidated whether bystander cardiopulmonary resuscitation (CPR) influences these electrocardiogram (ECG) variables. In 89 patients with out-of-hospital cardiac arrest, ECG recordings of 594 countershock attempts were collected and analyzed retrospectively. By using fast Fourier transformation analysis of the ventricular fibrillation ECG signal in the frequency range 0.333-15 Hz (median [range]), median frequency, dominant frequency, spectral edge frequency, and amplitude were as follows: 4.4 (2.4-7.5) Hz, 4.0 (0.7-7.0) Hz, 7.7 (3.7-13.7) Hz, and 0.94 (0.24-1.95) mV, respectively, before successful countershock (n = 59). These values were 3.8 (0.8-7.7) Hz (P = 0.0002), 3.0 (0.3-9.7) Hz (P < 0.0001), 7.3 (2.0-14.0) Hz (P < 0.05), and 0.53 (0.03-3.03) mV (P < 0.0001), respectively, before unsuccessful countershock (n = 535). In patients in whom bystander CPR was performed (n = 51), ventricular fibrillation frequency and amplitude before the first defibrillation attempt were higher than in patients without bystander CPR (n = 38) (median frequency, 4.4 [2.4-7.5] vs 3.7 [1.8-5.3] Hz, P < 0.0001; dominant frequency, 3.8 [0.9-7.7] vs 2.6 [0.8-5.9] Hz, P < 0.0001; spectral edge frequency, 8.4 [4.8-12.9] vs 7.2 [3.9-12.1] Hz, P < 0.05; amplitude, 0.79 [0.06-4.72] vs 0.67 [0.16-2.29] mV, P = 0.0647). Receiver operating characteristic curves demonstrate that successful countershocks will be best discriminated from unsuccessful countershocks by ventricular fibrillation amplitude (3000-ms epoch). At 73% sensitivity, a specificity of 67% was obtained with this variable.

IMPLICATIONS

In patients with out-of-hospital cardiac arrest, successful countershocks will be best discriminated from unsuccessful countershocks by ventricular fibrillation amplitude (3000-ms epoch). At 73% sensitivity, a specificity of 67% was obtained with this variable.

Optimizing timing of ventricular defibrillation

OBJECTIVE

Our intent was to evolve a prognosticator that would predict the likelihood that an electrical shock would restore a perfusing rhythm. Such a prognosticator was to be based on conventional electrocardiographic signals but without constraints caused by artifacts resulting from precordial compression. The adverse effects of "hands off" intervals for rhythm analyses would therefore be minimized. Such a prognosticator was further intended to reduce the number of electrical shocks and the total energy delivered and thereby minimize postresuscitation myocardial dysfunction.

DESIGN

Observational study.

SUBJECTS

Medical research laboratory of a university-affiliated research and educational institute.

SUBJECTS

Domestic pigs.

INTERVENTIONS

Ventricular fibrillation was induced in an established porcine model of cardiac arrest. Recordings of scalar lead 2 over the frequency range of 4-48 Hz were utilized. The area under the curve representing the amplitude and frequency was defined as the amplitude spectrum area (AMSA).

MEASUREMENTS AND MAIN RESULTS

A derivation group of 55 animals yielded a threshold value of AMSA that uniformly predicted successful resuscitation. A separate group of 10 animals, a validation group, confirmed that an AMSA value of 21 mV.Hz predicted restoration of perfusing rhythm after 7 of 8 electrical shocks and failure of electrical conversion in 21 of 23 electrical shocks, yielding sensitivity and specificity of about 90%. The negative predictive value of AMSA was 95% and statistically equivalent to that of coronary perfusion pressure, mean amplitude, and median frequency. The positive predictive value that would prompt continuation of cardiopulmonary resuscitation without interruption for an unsuccessful defibrillation attempt was greatly improved with AMSA (78%) as compared with coronary perfusion pressure (42%), mean amplitude (32%), and median frequency (29%).

CONCLUSION

AMSA has the potential for guiding more optimal timing of defibrillation without adverse interruption of cardiopulmonary resuscitation or the delivery of unsuccessful high energy electrical shocks that contribute to postresuscitation myocardial injury.

Variation in the dominant period during ventricular fibrillation

Time-varying periodicities are commonly observed in biological time series. In this paper, we discuss three different algorithms to detect and quantify change in periodicity. Each technique uses a sliding window to estimate periodic components in short subseries of a longer recording. The three techniques we utilize are based on: 1) standard Fourier spectral estimation; 2) an information theoretic adaption of linear (autoregressive) modeling; and 3) geometric properties of the embedded time series. We compare the results obtained from each of these methods using artificial data and experimental data from swine ventricular fibrillation (VF). Spectral estimates have previously been applied to VF time series to show a time-dependent trend in the dominant frequency. We confirm this result by showing that the dominant period of VF, following onset, first decreases to a minimum and then rises to a plateau. Furthermore, our algorithms detect longer period correlations which may indicate the presence of additional periodic oscillations or more complex nonlinear structure. We show that in general this possibly nonlinear structure is most apparent immediately after the onset of VF.

Algorithms to analyze ventricular fibrillation signals

Prediction of the success of defibrillation to avoid myocardial injury and performance feedback during CPR requires algorithms to analyze ventricular fibrillation signals. This report reviews investigations on different parameters of ventricular fibrillation electrocardiographic signals, including amplitude, frequency, bispectral analysis, amplitude spectrum area, wavelets, nonlinear dynamics, N(alpha) histograms, and combinations of several of these parameters. To date, no satisfactory methods have been found that cope with CPR artifacts and show adequate predictive power of successful defibrillation. The usual limitations of the studies are the small number of subjects, which precludes separation into training and test data. Because many investigations are animal studies of untreated short ventricular fibrillation, the results may be different for prolonged ventricular fibrillation in humans. The universality of threshold values has to be examined, and promising new parameters have to be monitored over longer time periods and analyzed for the effects of chest compressions, ventilation, and concomitant vasopressor therapy.

Physician interpretation and quantitative measures of electrocardiographic ventricular fibrillation waveform

OBJECTIVES

The characteristics of the ventricular fibrillation (VF) waveform may influence treatment decisions and the likelihood of therapeutic success. However, assessment of VF as being fine or coarse and the distinction between fine VF and asystole are largely subjective. The authors sought to determine the level of agreement among physicians for interpretation of varying VF waveforms, and to compare these subjective interpretations with quantitative measures.

METHODS

Six-second segments of waveform from LIFEPAK 300 units were collected. Fifty segments, including 45 VF and five ventricular tachycardia (VT) distracters, were graphed to simulate rhythm strips. These waveforms were quantitatively described using scaling exponent, root-mean-squared amplitude, and centroid frequency. Thirty-two emergency medicine residents were asked to interpret the arrhythmias as VT, "coarse" VF, "fine" VF, or asystole. Their responses were compared with the qantitative measures. Interphysician agreement was assessed with the kappa statistic.

RESULTS

One thousand four hundred forty interpretations were analyzed. There was fair agreement between physicians about the classification of arrhythmias (kappa = 0.39). Mean values associated with coarse VF, fine VF, and asystole differed in all three quantitative measure categories. The decision whether to defibrillate was highly correlated with the distinction between VF and asystole (Pearson chi-square = 1,170.40, df = 1, p[two-sided] < 0.001).

CONCLUSIONS

With only fair agreement on the threshold of fine VF and asystole, defibrillation decisions are largely subjective and caregiver-specific. These data suggest that quantitative measures of the VF waveform could augment the current standard of subjective classification of VF by emergency care providers.

Scaling exponent predicts defibrillation success for out-of-hospital ventricular fibrillation cardiac arrest

BACKGROUND

-Defibrillator shocks often fail to terminate ventricular fibrillation (VF) in out-of-hospital cardiac arrest (OOHCA), and repeated failed shocks can worsen the subsequent response to therapy. Because the VF waveform changes with increasing duration of VF, it is possible that ECG analyses could estimate the preshock likelihood of defibrillation success. This study examined whether an amplitude-independent measure of preshock VF waveform morphology predicts outcome after defibrillation. Methods and Results-Clinical data and ECG recordings from an automated external defibrillator were obtained for 75 subjects with OOHCA in a suburban community with police first responders and a paramedic-based emergency medical system. An estimate of the fractal self-similarity dimension, the scaling exponent, was calculated off-line for the VF waveform preceding shocks. Success of the first shock was determined from the recordings. Return of pulses and survival were determined by chart review. The first shock resulted in an organized rhythm in 43% of cases, and 17% of cases survived to hospital discharge. A lower mean value of the scaling exponent was observed for cases in which the first defibrillation resulted in an organized rhythm (P:=0.004), for cases with return of pulses (P:=0.049), and for cases surviving to hospital discharge (P:<0.001). Receiver operator curves revealed the utility of the scaling exponent for predicting the probability of restoring an organized rhythm (area under the curve=0.70) and of survival (area under the curve=0.84).

CONCLUSIONS

-The VF waveform in OOHCA can be quantified with the scaling exponent, which predicts the probability of first-shock defibrillation and survival to hospital discharge.

"Probability of successful defibrillation" as a monitor during CPR in out-of-hospital cardiac arrested patients

The frequency spectrum of the ECG in ventricular fibrillation (VF) correlates with myocardial perfusion and might predict defibrillation success defined as return of spontaneous circulation (ROSC). The predictive power increases when more spectral variables are combined, but the complex information can be difficult to handle during the intensity of CPR. We therefore developed a method for expressing this multidimensional information in a single reproducible variable reflecting the probability of defibrillation success. This is based on the highest performing predictor for ROSC after 883 shocks given to 156 patients with VF. This was a combination of two decorrelated spectral features based on a principal component analysis of an original feature set with information on centroid frequency, peak power frequency, spectral flatness and energy. The function "Probability of defibrillation success" (P(ROSC)(v)) was developed by a 2-dimensional histogram technique. P(ROSC)(v) discriminated between shocks followed by ROSC and No-ROSC (P<0.0001). The present methodology indicates a possible way to develop a CPR monitor.

Reducing CPR artefacts in ventricular fibrillation in vitro

CPR creates artefacts on the ECG, and a pause in CPR is therefore mandatory during rhythm analysis. This hands-off interval is harmful to the already marginally circulated tissues during CPR, and if the artefacts could be removed by filtering, the rhythm could be analyzed during ongoing CPR. Fixed coefficient filters used in animals cannot solve this problem in humans, due to overlapping frequency spectra for artefacts and VF signals. In the present study, we established a method for mixing CPR-artefacts (noise) from a pig with human VF (signal) at various signal-to-noise ratios (SNR) from -10 dB to +10 dB. We then developed a new methodology for removing CPR artefacts by applying a digital adaptive filter, and compared the results with this filter to that of a fixed coefficient filter. The results with the adaptive filter clearly outperformed the fixed coefficient filter for all SNR levels. At an original SNR of 0 dB, the restored SNRs were 9.0+/-0.7 dB versus 0.9+/-0.7 dB respectively (P<0.0001).

Electrophysiological deterioration during long-duration ventricular fibrillation

BACKGROUND

Probability of survival from sudden cardiac arrest caused by ventricular fibrillation (VF) decreases rapidly with fibrillation duration. We hypothesized that cellular ischemia/fibrillation-induced electrophysiological deterioration underlies decreased survival.

METHODS AND RESULTS

We determined fibrillation monophasic action potential (MAP) morphology including action potential frequency content, duration, cycle length, developing diastolic intervals, and amplitude as a function of ischemic fibrillation duration in 10 isolated rabbit hearts. We also correlated ECG frequency (used clinically) and MAP amplitude and frequency. Fibrillation cycle length and diastole duration increased, whereas APD(100) shortened significantly with time (P:<0.001). Between 1 and 3 minutes, diastole appeared primarily as the result of APD(100) shortening, with only small changes in cycle length. Between 2 and 5 minutes, diastole increased primarily as the result of increased cycle length. Diastole developed progressively from 5% of VF cycles at 5 seconds to approximately 100% of VF cycles by 120 seconds (P:<0.001). Diastole increased from 1% of cycle length at 5 seconds to 62% at 5 minutes. Its duration increased from 4.7 ms at 5 seconds to 90 ms at 5 minutes (P:<0.001). Both MAP and ECG 1/frequency closely correlated with fibrillation cycle length.

CONCLUSIONS

These results show a rapid and progressive electrophysiological deterioration during fibrillation, leading to electrical diastole between fibrillation action potentials. This rapid deterioration may explain the decreased probability of successful resuscitation after prolonged fibrillation. Therefore, a greater understanding of cellular deterioration during fibrillation may lead to improved resuscitation methods, including development of specific defibrillator waveforms for out-of-hospital cardiac arrest.

The effects of potassium-ATP channel modulation on ventricular fibrillation and defibrillation in the pig heart

BACKGROUND

Drugs acting on the cardiac ATP-sensitive potassium (K-ATP) channels may modulate responses to ischaemia and arrhythmogenesis. We investigated the effects of K-ATP channel modulation on frequency patterns of ventricular fibrillation (VF) and on defibrillation threshold (DFT).

METHODS AND RESULTS

Each group of 24 pigs randomly received intravenous levcromakalim (LKM) 40 microgram/kg (K-ATP agonist), glibenclamide (Glib) 20 mg/kg (K-ATP antagonist), saline or vehicle. Firstly, QTc interval was measured before and after drug. VF was then induced by endocardial stimulation and its power spectra and dominant frequencies over 15 min determined by fast Fourier transformation. Secondly, transthoracic DFT was determined (step-up/step-down protocol) before and after each drug. LKM reduced QTc interval (e.g., lead II, 354-321 ms, P<0.05) and increased the dominant VF frequency between 6 and 8 min (9.5+/-0.5 Hz at 6.5 min compared with 7.2+/-0.6 Hz (saline), 7.4+/-0.8 Hz (vehicle), 6.8+/-0.5 Hz (Glib), P=0.03). LKM reduced (to 57.2+/-2.1 mmHg) and Glib increased (to 107.8+/-6.1) mean arterial BP compared with saline (80.3+/-5.6) and vehicle (87. 6+/-7.1; P<0.01). There was no significant difference in defibrillation threshold energy, current or voltage, after any drug.

CONCLUSIONS

Activation of K-ATP channels reduced blood pressure and QTc interval. The lack of major effect on VF dominant frequency and DFT of either LKM or Glib suggests that prior administration of similar drugs to patients should not prejudice outcome from VF cardiac arrest.

Electrocardiographic waveform analysis for predicting the success of defibrillation

A new electrocardiographic predictor of the likelihood that an electrical shock would restore a perfusing rhythm is described. The intent was to develop a prognosticator that would be displayed during precordial compression. We anticipated that such a predictor would allow more selective timing of electrical shocks and reduce electrical injury to the myocardium caused by repetitive shocks. In a porcine model of cardiac arrest because of ventricular fibrillation, electrocardiographic recordings of ventricular fibrillation wavelets were analyzed and transformed into an amplitude spectrum area (AMSA). An AMSA value of 21 mV x Hz predicted restoration of perfusing rhythm with a positive predictive value equivalent to that of coronary perfusion pressure. More important, the negative predictive value that a shock would fail to reestablish spontaneous circulation was 96%. AMSA, therefore, has the potential for guiding optimal timing of defibrillation.

Endocardial activation during ventricular fibrillation in normal and failing canine hearts

Because congestive heart failure (CHF) promotes ventricular fibrillation (VF), we compared VF in seven dogs with CHF induced by combined myocardial infarction and rapid ventricular pacing to VF in six normal dogs. A noncontact, multielectrode array balloon catheter provided full-surface real-time left ventricular (LV) endocardial electrograms and a dynamic color-coded display of endocardial activation projected onto a three-dimensional model of the LV. Fast Fourier transform (FFT) analysis of virtual electrograms showed no difference in peak or centroid frequency in CHF dogs compared with normals. The average number of simultaneous noncontiguous wavefronts present during VF was higher in normals (2.4 +/- 1.0 at 10 s of VF) than in CHF dogs (1.3 +/- 1.0, P < 0.005) and decreased in both over time. The wavefront "turnover" rate, estimated using FFT of the noncontiguous wavefront data, did not differ between normals and CHF and did not change over 5 min of VF. Thus the fundamental frequency characteristics of VF are unaltered by CHF, but dilated abnormal ventricles sustain fewer active wavefronts than do normal ventricles.

Predicting outcome of defibrillation by spectral characterization and nonparametric classification of ventricular fibrillation in patients with out-of-hospital cardiac arrest

BACKGROUND

In 156 patients with out-of-hospital cardiac arrest of cardiac cause, we analyzed the ability of 4 spectral features of ventricular fibrillation before a total of 868 shocks to discriminate or not between segments that correspond to return of spontaneous circulation (ROSC).

METHODS AND RESULTS

Centroid frequency, peak power frequency, spectral flatness, and energy were studied. A second decorrelated feature set was generated with the coefficients of the principal component analysis transformation of the original feature set. Each feature set was split into training and testing sets for improved reliability in the evaluation of nonparametric classifiers for each possible feature combination. The combination of centroid frequency and peak power frequency achieved a mean+/-SD sensitivity of 92+/-2% and specificity of 27+/-2% in testing. The highest performing classifier corresponded to the combination of the 2 dominant decorrelated spectral features with sensitivity and specificity equal to 92+/-2% and 42+/-1% in testing or a positive predictive value of 0.15 and a negative predictive value of 0.98. Using the highest performing classifier, 328 of 781 shocks not leading to ROSC would have been avoided, whereas 7 of 87 shocks leading to ROSC would not have been administered.

CONCLUSIONS

The ECG contained information predictive of shock therapy. This could reduce the delivery of unsuccessful shocks and thereby the duration of unnecessary "hands-off" intervals during cardiopulmonary resuscitation. The low specificity and positive predictive value indicate that other features should be added to improve performance.

Effect of rates of perfusion on dominant frequency and defibrillation energy in isolated fibrillating hearts

This study assessed the influence of rates of reperfusion on excitability of the myocardium using dominant frequency (DF) (in Hz) of VF and the relationship of DF to the minimum defibrillation energy (MDE) (in J). Our hypothesis was that increasing flow during reperfusion increases DF that raises MDE. Initially, six Langendorff perfused swine hearts were serially fibrillated and perfusion arrested for 4 minutes followed by reperfusion and defibrillation to establish reproducibility of the model. The epicardial ECG was analyzed for DF. In subsequent studies (n = 8), no flow VF was followed by 1-minute reperfusion at normal flow or 10% flow (low flow) and shocked with increasing energy via epicardial pads until defibrillation. The DF at onset of no flow VF was 9.5 +/- 1.4 and decreased to 3.6 +/- 1.4 after 4 minutes. Reperfusion at normal flow increased the DF of VF compared to low flow after 1 minute (10.8 +/- 1.1 vs 4.5 +/- 1.1 Hz, P = 0.0002) and was associated with increased defibrillation energy requirements (13.5 +/- 5.0 vs 7.3 +/- 6.2 J, P = 0.047). In summary, defibrillation energy requirements are lower when myocardial excitability is reduced during low flow reperfusion.

A novel wavelet transform based analysis reveals hidden structure in ventricular fibrillation

We report a new method of interrogating the surface ECG signal using techniques developed in the field of wavelet transform analysis. Previously unreported structure within the ECG during ventricular fibrillation (VF) is found using a high-resolution decomposition of the signal employing the continuous wavelet transform. We believe that wavelet transform methods could lead to the development of powerful tools for use in the resuscitation of patients with cardiac arrest.

Electrocardiographic prediction of the success of cardiac resuscitation

OBJECTIVES

To identify a method for predicting the success or failure of a defibrillatory shock such as to avoid potentially detrimental interruptions of cardiopulmonary resuscitation (CPR). Such a method would also guide more optimal programming of automated external defibrillators.

DESIGN

Prospective, observational animal study.

SETTING

Medical research laboratory in a university-affiliated research and educational foundation.

SUBJECTS

Domestic pigs.

INTERVENTIONS

Ventricular fibrillation (VF) was electrically induced in 66 domestic pigs. After an interval of between 3 and 5 mins of untreated VF, precordial compression was begun. Electrocardiographic lead 2 was monitored and artifacts produced during precordial compression were removed by digital filtering.

MEASUREMENTS AND MAIN RESULTS

In the derivation study, electrical defibrillation restored spontaneous circulation in 30 of the 66 animals. Successfully resuscitated animals had significantly greater coronary perfusion pressure, maximum VF amplitude, mean VF amplitude, and dominant VF frequency. No animals were resuscitated if the coronary perfusion pressure was <8 mm Hg, maximum amplitude was <0.48 mV, mean amplitude was <0.25 mV, or dominant frequency <9.9 Hz independently of the duration of untreated VF. When mean amplitude and dominant frequency were combined, the predictability was further improved. In an additional validation study of 14 animals, consecutive defibrillations were uniformly unsuccessful if the combination of mean amplitude and dominant frequency did not exceed the threshold values obtained in derivation study.

CONCLUSION

Mean VF amplitude alone or in combination with dominant frequency of VF was expressed as a numerical score. It served as an objective noninvasive measurement on a par with that of coronary perfusion pressure for predicting the success of defibrillation. As such, it minimizes the detriment of repetitively interrupting mechanical interventions during CPR for electrical defibrillation when an electrical shock predictably fails to restore an effective rhythm.

A rule for early outcome classification of out-of-hospital cardiac arrest patients presenting with ventricular fibrillation

The aim of the study was to develop a scoring system for outcome classification at the start of prehospital first tier resuscitation for patients with cardiac arrest from ventricular fibrillation (VF). We studied a consecutive sample of 100 out-of-hospital cardiac arrest patients, presenting with VF of presumed cardiac etiology on arrival of the first tier (in a two-tiered urban Emergency Medical Services system). The number of patients discharged was 29 ('survivors') and 71 died ('non-survivors'). The electrocardiography (ECG) tracings recorded during resuscitation using a semi-automatic defibrillator were retrospectively analysed. For each patient, VF amplitude in mV (VF_a) and the number of base-line crossings per second (VF_blc) were calculated. Fisher's linear discriminant analysis was applied to discriminate between survivors and non-survivors using the variables VF_a, VF_blc and age. Patients were classed as potential survivors or non-survivors using a survival index = 0.6*(VF_a) + 0.4*(VF_blc)-4.0. If for a given patient the survival index is < 0, he is classified in the non-survivor group, if the survival index is > 0, he is classified in the survivor group. Using this index 79% of the survivors and 70% of the non-survivors could be classified correctly. Adding age to the formula increased the correct classification of survivors to 86 and 73% for the non-survivors. The survival index provides a research tool for the discrimination between potential survivors and non-survivors, which opens the possibility for the development of alternative treatment protocols in cardiac arrest.

Median fibrillation frequency in cardiac surgery: influence of temperature and guide to countershock therapy

OBJECTIVE

This study was designed (1) to investigate the effects of normothermic and hypothermic perfusion on the median frequency of the fibrillating myocardium, and (2) to elucidate whether frequency-guided countershock therapy improves countershock success during the reperfusion phase of cardiac surgery.

DESIGN

Prospective, randomized study.

SETTING

University hospital cardiac surgery room.

PATIENTS

Thirty patients (first part of the study) and 38 patients (second part of the study) scheduled for elective coronary artery bypass surgery.

METHODS AND RESULTS

During cardiopulmonary bypass, ventricular fibrillation (VF) was induced at a core body temperature of 34.1+/-0.2 degrees C (normothermia) (n=15) or at a core body temperature of 29.8+/-0.2 degrees C (hypothermia) (n=15). Using fast Fourier transformation of the ECG signal, median fibrillation frequency was recorded continuously for a period of 120 s. At the end of surgery, countershock was performed as soon as VF was recognized on the ECG monitor (X Hz group; n=19) or countershock was not performed until median fibrillation frequency had increased to the threshold of at least 5 Hz (5 Hz group; n=19). Median fibrillation frequency in the normothermic fibrillation group was statistically higher than in the hypothermic group. In the X Hz and 5 Hz countershock group, median fibrillation frequency before the first countershock attempt was 3.6+/-0.2 Hz and 5.4+/-0.1 Hz (p<0.0001), respectively. In the X Hz group, six countershocks resulted in supraventricular rhythm, 10 in VF, two in electromechanical dissociation, and one in asystole. In the 5 Hz group, 16 countershocks resulted in supraventricular rhythm, two in VF, and one in asystole (p=0.008).

CONCLUSIONS

During normothermia, median fibrillation frequency is significantly higher than during hypothermic perfusion conditions. During the reperfusion phase of cardiac surgery, countershock success rate is significantly higher when a threshold of at least 5 Hz had been reached before the first countershock attempt.

Analysis of the ventricular fibrillation ECG signal amplitude and frequency parameters as predictors of countershock success in humans

OBJECTIVE

The purpose of this study was to assess from the ventricular fibrillation ECG signal whether certain amplitude parameters, or frequency parameters derived using fast Fourier transform analysis, are predictive of countershock success (defined as a stable supraventricular rhythm following countershock).

DESIGN

Retrospective, descriptive study.

SETTING

Emergency medical service at a university hospital.

PATIENTS

Twenty-six patients with out-of-hospital cardiac arrest, whose initial ECG rhythm was identified as ventricular fibrillation.

METHODS AND RESULTS

In all patients, advanced cardiac life support was performed in the out-of-hospital setting and a semiautomatic defibrillator was used for countershock therapy and simultaneous on-line ECG recording. For each patient, ECG data were stored in modules in digitized form over a period of 20 min and analyzed retrospectively. Using fast Fourier transform analysis of the ventricular fibrillation ECG signal in the frequency range of 0.3 to 30 Hz (mean +/- SD), median frequency, dominant frequency, edge frequency, and amplitude were as follows: 5.17 +/- 1.05 Hz, 4.56 +/- 0.99 Hz, 10.74 +/- 3.46 Hz, and 1.33 +/- 0.44 mV before successful countershock (n = 20); and 4.21 +/- 1.17 Hz (p = 0.0034), 3.31 +/- 1.57 Hz (p = 0.0004), 9.46 +/- 2.93 Hz (p = 0.5390), and 1.15 +/- 0.69 mV (p = 0.0134) before unsuccessful countershock (n = 134). Using software filters to completely eliminate interference due to manual cardiopulmonary resuscitation from the ventricular fibrillation power spectrum, only amplitude remained statistically different (p < or = 0.03) in predicting countershock success.

CONCLUSIONS

We conclude that in patients, median frequency, dominant frequency, and amplitude are predictive of countershock success in humans.

One-year survival after out-of-hospital cardiac arrest in Bonn city: outcome report according to the 'Utstein style'

Outcome after prehospital cardiac arrest was examined in the EMS system of Bonn, a midsized urban community, and presented according to the Utstein style. The data were collected from January 1st, 1989 to December 31st, 1992 by the Bonn-north ALS unit, which serves 240,000 residents. Fifty-six patients suffered from cardiac arrest of non-cardiac aetiology and were excluded; 464 patients were resuscitated after cardiac arrest of presumed cardiac aetiology (incidence of CPR attempts: 48.33 per year/100,000 population). The collapse was unwitnessed, bystander witnessed or EMS personnel witnessed in 178, 214 or 72 patients, respectively. In these subgroups discharge rates and 1-year survival accounted for 7.3% (4.5%), 22.9% (15.9%) and 16.7% (11.1%), respectively. Thirty-four patients were discharged without neurological deficits (cerebral performance category 1: CPC 1), 22 and nine patients scored CPC 2 or CPC 3, respectively. Nine patients were comatose (CPC 4) when they were discharged and remained in this state until they died. Of the 50 1-year survivors 35 lived without neurological deficit, eight demonstrated mild (CPC 2) and five severe (CPC 3) cerebral disability at 1-year after resuscitation, and, finally, two patients remained comatose for more than 1 year. The Utstein template recommends the selection of patients who were found in VF after bystander witnessed collapse. In our cohort 118 patients met these criteria. Of them 41 (35%) could be discharged from hospital and 28 (24%) lived more than 1 year. The comparison of our data with those from double-response EMS systems of other communities revealed that, in midsized urban and suburban communities the highest discharging rates could be achieved. Our study demonstrated that survival depends crucially on short response intervals and life support which will be performed by well-trained emergency technicians, paramedics and physicians.

Spectral analysis of ventricular fibrillation and closed-chest cardiopulmonary resuscitation

This study was designed to assess the interference by closed-chest cardiopulmonary resuscitation (CPR) on the ventricular fibrillation (VF) ECG signal in a porcine model of cardiac arrest and to elucidate which variable of VF spectral analysis reflects best myocardial blood flow and resuscitation success during CPR. Fourteen domestic pigs were allocated to receive either 0.4 U/kg vasopressin (n = 7) or 10 ml saline (n = 7) after 4 min of VF and 3 min of CPR. Using radiolabeled microspheres, myocardial blood flow was determined during CPR before, and 90 s and 5 min after, drug administration. Using spectral analysis of VF, the median frequency, dominant frequency, edge frequency and amplitude of VF were determined simultaneously and before the first defibrillation attempt. Using filters in order to specify frequency ranges, stepwise elimination of mechanical artifacts resulting from CPR revealed that at a frequency bandpass of 4.3-35 Hz, median fibrillation frequency has a sensitivity, specificity, positive and negative predictive value of 100% to differentiate between resuscitated and non-resuscitated animals. The best correlation between myocardial blood flow and fibrillation frequency was found at a median frequency range of 4.3-35 Hz. We conclude that spectral analysis of VF can provide reliable information relating to successful resuscitation. In this model after elimination of oscillations due to mechanical CPR, median fibrillation frequency best reflects the probability of resuscitation success.