Prediction of successful defibrillation in human victims of out-of-hospital cardiac arrest: a retrospective electrocardiographic analysis

Prediction of the effect of electrical defibrillation by using spectral feature parameters

This paper proposes a system for predicting the effect of electrical defibrillation using spectral feature parameters. The proposed method consists of two-stage prediction. The first stage involves predicting whether electrical defibrillation is "Successful" or "Ineffective." As the next stage, if the proposed prediction system determines "Ineffective," the proposed system discriminates between "VF recurrence" or "Failure" for electrical defibrillation. To develop the prediction system, feature parameters for the target electrocardiograms (ECGs) were first extracted by using the wavelet transform and spectral analysis. Next, effective feature parameters for prediction are selected through an analysis of variance. Moreover, in the preprocessing phase, the Synthetic Minority Oversampling Technique method and standardization are introduced. Finally, support vector machines with some kernel functions and the regularization method are utilized to predict the three states, i.e., "Successful," "Failure," and "VF recurrence," for electrical defibrillation in two phases. In this paper, we present our analysis method for ECGs and evaluate the effectiveness of the proposed prediction system.

Machine learning model to predict evolution of pulseless electrical activity during in-hospital cardiac arrest

Background

During pulseless electrical activity (PEA) the cardiac mechanical and electrical functions are dissociated, a phenomenon occurring in 25-42% of in-hospital cardiac arrest (IHCA) cases. Accurate evaluation of the likelihood of a PEA patient transitioning to return of spontaneous circulation (ROSC) may be vital for the successful resuscitation.

The aim

We sought to develop a model to automatically discriminate between PEA rhythms with favorable and unfavorable evolution to ROSC.

Methods

A dataset of 190 patients, 120 with ROSC, were acquired with defibrillators from different vendors in three hospitals. The ECG and the transthoracic impedance (TTI) signal were processed to compute 16 waveform features. Logistic regression models where designed integrating both automated features and characteristics annotated in the QRS to identify PEAs with better prognosis leading to ROSC. Cross validation techniques were applied, both patient-specific and stratified, to evaluate the performance of the algorithm.

Results

The best model consisted in a three feature algorithm that exhibited median (interquartile range) Area Under the Curve/Balanced accuracy/Sensitivity/Specificity of 80.3(9.9)/75.6(8.0)/ 77.4(15.2)/72.3(16.4) %, respectively.

Conclusions

Information hidden in the waveforms of the ECG and TTI signals, along with QRS complex features, can predict the progression of PEA. Automated methods as the one proposed in this study, could contribute to assist in the targeted treatment of PEA in IHCA.

Amplitude spectral area of ventricular fibrillation and defibrillation success at low energy in out-of-hospital cardiac arrest

The optimal energy for defibrillation has not yet been identified and very often the maximum energy is delivered. We sought to assess whether amplitude spectral area (AMSA) of ventricular fibrillation (VF) could predict low energy level defibrillation success in out-of-hospital cardiac arrest (OHCA) patients. This is a multicentre international study based on retrospective analysis of prospectively collected data. We included all OHCAs with at least one manual defibrillation. AMSA values were calculated by analyzing the data collected by the monitors/defibrillators used in the field (Corpuls 3 and Lifepak 12/15) and using a 2-s-pre-shock electrocardiogram interval. We run two different analyses dividing the shocks into three tertiles (T1, T2, T3) based on AMSA values. 629 OHCAs were included and 2095 shocks delivered (energy ranging from 100 to 360 J; median 200 J). Both in the "extremes analysis" and in the "by site analysis", the AMSA values of the effective shocks at low energy were significantly higher than those at high energy (p = 0.01). The likelihood of shock success increased significantly from the lowest to the highest tertile. After correction for age, call to shock time, use of mechanical CPR, presence of bystander CPR, sex and energy level, high AMSA value was directly associated with the probability of shock success [T2 vs T1 OR 3.8 (95% CI 2.5-6) p < 0.001; T3 vs T1 OR 12.7 (95% CI 8.2-19.2), p < 0.001]. AMSA values are associated with the probability of low-energy shock success so that they could guide energy optimization in shockable cardiac arrest patients.

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

Background

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

Methods and Results

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

Conclusions

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

Registration

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

End-tidal carbon dioxide (ETCO2) and ventricular fibrillation amplitude spectral area (AMSA) for shock outcome prediction in out-of-hospital cardiac arrest. Are they two sides of the same coin?

AIM

Ventricular fibrillation amplitude spectral area (AMSA) and end-tidal carbon dioxide (ETCO₂) are predictors of shock success, understood as restoration of an organized rhythm, and return of spontaneous circulation (ROSC). However, little is known about their combined use. We aimed to assess the prediction accuracy when combined, and to clarify if they are correlated in out of hospital cardiac arrest' victims.

MATERIALS AND METHODS

Records acquired by external defibrillators in out-of-hospital cardiac arrest patients of the Lombardia Cardiac Arrest registry were processed. The 1-min pre-shock ETCO₂ median value (METCO₂) was computed from the capnogram and AMSA (2-48 mV.Hz range) computed applying the Fast Fourier Transform to a 2-second pre-shock filtered ECG interval (0.5-30 Hz). Support Vector Machine (SVM) predictive models based on METCO₂, AMSA and their combination were fit; results were given as the area under the curve (AUC) of the receiver operating characteristic (ROC) curves.

RESULTS

We considered 112 patients with 391 shocks delivered. METCO₂ and AMSA were predictors of shock success [AUC (IQR) of the ROC curve: 0.59 (0.56-0.62); 0.68 (0.65-0.72), respectively] and of ROSC [0.56 (0.53-0.59); 0.74 (0.71-0.78),]. Their combination in a SVM model increased the accuracy for predicting shock success [AUC (IQR) of the ROC curve: 0.71 (0.68-0.75)] and ROSC [0.77 (0.73-0.8)]. AMSA and METCO₂ were significantly correlated only in patients who achieved ROSC (rho = 0.33 p = 0.03).

CONCLUSIONS

AMSA and ETCO₂ predict shock success and ROSC after every shock, and their predictive power increases if combined. Notably, they were correlated only in patients who achieved ROSC.

Value of capnography to predict defibrillation success in out-of-hospital cardiac arrest

BACKGROUND AND AIM

Unsuccessful defibrillation shocks adversely affect survival from out-of-hospital cardiac arrest (OHCA). Ventricular fibrillation (VF) waveform analysis is the tool-of-choice for the non-invasive prediction of shock success, but surrogate markers of perfusion like end-tidal CO2 (EtCO2) could improve the prediction. The aim of this study was to evaluate EtCO2 as predictor of shock success, both individually and in combination with VF-waveform analysis.

MATERIALS AND METHODS

In total 514 shocks from 214 OHCA patients (75 first shocks) were analysed. For each shock three predictors of defibrillation success were automatically calculated from the device files: two VF-waveform features, amplitude spectrum area (AMSA) and fuzzy entropy (FuzzyEn), and the median EtCO2 (MEtCO2) in the minute before the shock. Sensitivity, specificity, receiver operating characteristic (ROC) curves and area under the curve (AUC) were calculated, for each predictor individually and for the combination of MEtCO2 and VF-waveform predictors. Separate analyses were done for first shocks and all shocks.

RESULTS

MEtCO2 in first shocks was significantly higher for successful than for unsuccessful shocks (31mmHg/25mmHg, p<0.05), but differences were not significant for all shocks (32mmHg/29mmHg, p>0.05). MEtCO2 predicted shock success with an AUC of 0.66 for first shocks, but was not a predictor for all shocks (AUC 0.54). AMSA and FuzzyEn presented AUCs of 0.76 and 0.77 for first shocks, and 0.75 and 0.75 for all shocks. For first shocks, adding MEtCO2 improved the AUC of AMSA and FuzzyEn to 0.79 and 0.83, respectively.

CONCLUSIONS

MEtCO2 predicted defibrillation success only for first shocks. Adding MEtCO2 to VF-waveform analysis in first shocks improved prediction of shock success. VF-waveform features and MEtCO2 were automatically calculated from the device files, so these methods could be introduced in current defibrillators adding only new software.

Association between ventricular fibrillation amplitude immediately prior to defibrillation and defibrillation success in out-of-hospital cardiac arrest

BACKGROUND

Several characteristics of the ventricular fibrillation (VF) waveform during cardiac arrest are associated with defibrillation success, including peak amplitude in the seconds prior to defibrillation. It is not known if immediate pre-defibrillation amplitude is associated with successful defibrillation, return of spontaneous circulation (ROSC) or survival to hospital discharge (SHD).

METHODS

We analyzed automated external defibrillation recordings of 80 patients with out-of-hospital VF cardiac arrest who received 284 defibrillations. We recorded the maximum amplitude during 3-second ECG tracings prior to each defibrillation attempt and the amplitude immediately prior to defibrillation.

RESULTS

Both the amplitude just prior to defibrillation and the highest amplitude within 3 seconds of the defibrillation were significantly higher in successful vs unsuccessful defibrillations (0.21 vs 0.11 mV, P = <.0001 and 0.51 vs 0.36 mV, P = <.0001). Amplitude immediately prior to defibrillation and maximal amplitude within 3 seconds of defibrillation were also higher in defibrillations with ROSC vs. defibrillations without ROSC (0.23 vs. 0.12 mV, P < .0001; and 0.52 vs. 0.38 mV, P < .0001). In defibrillations that resulted in SHD, immediate pre-defibrillation amplitude and maximum amplitude were also significantly larger (0.20 vs. 0.11 mV, P < .0001; and 0.52 vs. 0.35 mV, P < .0001). Binary logistic regression including both measures showed that only immediate pre-defibrillation amplitude remained significantly associated with ROSC while maximal amplitude did not (P = .006 and P = .135).

CONCLUSIONS

Amplitude of the VF waveform at the moment of defibrillation has a strong association with successful defibrillation, ROSC, and SHD.

Correlation of end tidal carbon dioxide, amplitude spectrum area, and coronary perfusion pressure in a porcine model of cardiac arrest

Amplitude Spectrum Area (AMSA) values during ventricular fibrillation (VF) correlate with myocardial energy stores and predict defibrillation success. By contrast, end tidal CO₂ (ETCO2) values provide a noninvasive assessment of coronary perfusion pressure and myocardial perfusion during cardiopulmonary resuscitation (CPR). Given the importance of the timing of defibrillation shock delivery on clinical outcome, we tested the hypothesis that AMSA and ETCO2 correlate with each other and can be used interchangably to correlate with myocardial perfusion in an animal laboratory preclinical, randomized, prospective investigation. After 6 min of untreated VF, 12 female pigs (32 ± 1 Kg), isoflurane anesthetized pigs received sequentially 3 min periods of standard (S) CPR, S‐CPR+ an impedance threshold device (ITD), and then active compression decompression (ACD) + ITD CPR. Hemodynamic, AMSA, and ETCO2 measurements were made with each method of CPR. The Spearman correlation and Friedman tests were used to compare hemodynamic parameters. ETCO2, AMSA, coronary perfusion pressure, cerebral perfusion pressure were lowest with STD CPR, increased with STD CPR + ITD and highest with ACD CPR + ITD. Further analysis demonstrated a positive correlation between AMSA and ETCO2 (r = 0.37, P = 0.025) and between AMSA and key hemodynamic parameters (P < 0.05). This study established a moderate positive correlation between ETCO2 and AMSA. These findings provide the physiological basis for developing and testing a novel noninvasive method that utilizes either ETCO2 alone or the combination of ETCO2 and AMSA to predict when defibrillation might be successful.

Does the choice of definition for defibrillation and CPR success impact the predictability of ventricular fibrillation waveform analysis?

BACKGROUND

Quantitative analysis of ventricular fibrillation (VF), such as amplitude spectral area (AMSA), predicts shock outcomes. However, there is no uniform definition of shock/cardiopulmonary resuscitation (CPR) success in out-of-hospital cardiac arrest (OHCA). The objective of this study is to investigate post-shock rhythm variations and the impact of shock/CPR success definition on the predictability of AMSA.

METHODS

A total of 554 shocks from 257 OHCA patients with VF as initial rhythm were analyzed. Post-shock rhythms were analyzed every 5s up to 120s and annotated as VF, asystole (AS) and organized rhythm (OR) at serial time intervals. Three shock/CPR success definitions were used to evaluate the predictability of AMSA: (1) termination of VF (ToVF); (2) return of organized electrical activity (ROEA); (3) return of potentially perfusing rhythm (RPPR).

RESULTS

Rhythm changes occurred after 54.5% (N=302) of shocks and 85.8% (N=259) of them occurred within 60s after shock delivery. The observed post-shock rhythm changes were (1) from AS to VF (24.9%), (2) from OR to VF (16.1%), and (3) from AS to OR (12.1%). The area under the receiver operating characteristic curve (AUC) for AMSA as a predictor of shock/CPR success reached its maximum 60s post-shock. The AUC was 0.646 for ToVF, 0.782 for ROEA, and 0.835 for RPPR (p<0.001) respectively.

CONCLUSIONS

Post-shock rhythm is unstable in the first minute after the shock. The predictability of AMSA varies depending on the definition of shock/CPR success and performs best with the return of potentially perfusing rhythm endpoint for OHCA.

Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes

Objective

The timing of defibrillation is mostly at arbitrary intervals during cardio-pulmonary resuscitation (CPR), rather than during intervals when the out-of-hospital cardiac arrest (OOH-CA) patient is physiologically primed for successful countershock. Interruptions to CPR may negatively impact defibrillation success. Multiple defibrillations can be associated with decreased post-resuscitation myocardial function. We hypothesize that a more complete picture of the cardiovascular system can be gained through non-linear dynamics and integration of multiple physiologic measures from biomedical signals.

Materials and Methods

Retrospective analysis of 153 anonymized OOH-CA patients who received at least one defibrillation for ventricular fibrillation (VF) was undertaken. A machine learning model, termed Multiple Domain Integrative (MDI) model, was developed to predict defibrillation success. We explore the rationale for non-linear dynamics and statistically validate heuristics involved in feature extraction for model development. Performance of MDI is then compared to the amplitude spectrum area (AMSA) technique.

Results

358 defibrillations were evaluated (218 unsuccessful and 140 successful). Non-linear properties (Lyapunov exponent > 0) of the ECG signals indicate a chaotic nature and validate the use of novel non-linear dynamic methods for feature extraction. Classification using MDI yielded ROC-AUC of 83.2% and accuracy of 78.8%, for the model built with ECG data only. Utilizing 10-fold cross-validation, at 80% specificity level, MDI (74% sensitivity) outperformed AMSA (53.6% sensitivity). At 90% specificity level, MDI had 68.4% sensitivity while AMSA had 43.3% sensitivity. Integrating available end-tidal carbon dioxide features into MDI, for the available 48 defibrillations, boosted ROC-AUC to 93.8% and accuracy to 83.3% at 80% sensitivity.

Conclusion

At clinically relevant sensitivity thresholds, the MDI provides improved performance as compared to AMSA, yielding fewer unsuccessful defibrillations. Addition of partial end-tidal carbon dioxide (PetCO₂) signal improves accuracy and sensitivity of the MDI prediction model.

Predict Defibrillation Outcome Using Stepping Increment of Poincare Plot for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest

Early cardiopulmonary resuscitation together with early defibrillation is a key point in the chain of survival for cardiac arrest. Optimizing the timing of defibrillation by predicting the possibility of successful electric shock can guide treatments between defibrillation and cardiopulmonary resuscitation and improve the rate of restoration of spontaneous circulation. Numerous methods have been proposed for predicting defibrillation success based on quantification of the ventricular fibrillation waveform during past decades. To date, however, no analytical technique has been widely accepted for clinical application. In the present study, we investigate whether median stepping increment that is calculated from the Euclidean distance of consecutive points in Poincare plot could be used to predict the likelihood of successful defibrillation. Electrocardiographic recordings of out-of-hospital cardiac arrest patients were obtained from the external defibrillators. The performance of the proposed method was evaluated by receiver operating characteristic curve and compared with the results of other established features. The results indicated that median stepping increment has comparable performance to the established methods in predicting the likelihood of successful defibrillation.

Repeated epinephrine doses during prolonged cardiopulmonary resuscitation have limited effects on myocardial blood flow: a randomized porcine study

Background

In current guidelines, prolonged cardiopulmonary resuscitation (CPR) mandates administration of repeated intravenous epinephrine (EPI) doses. This porcine study simulating a prolonged CPR-situation in the coronary catheterisation laboratory, explores the effect of EPI-administrations on coronary perfusion pressure (CPP), continuous coronary artery flow average peak velocity (APV) and amplitude spectrum area (AMSA).

Methods

Thirty-six pigs were randomized 1:1:1 to EPI 0.02 mg/kg/dose, EPI 0.03 mg/kg/dose or saline (control) in an experimental cardiac arrest (CA) model. During 15 minutes of mechanical chest compressions, four EPI/saline-injections were administered, and the effect on CPP, APV and AMSA were recorded. Comparisons were performed between the control and the two EPI-groups and a combination of the two EPI-groups, EPI-all.

Result

Compared to the control group, maximum peak of CPP (P_max) after injection 1 and 2 was significantly increased in the EPI-all group (p = 0.022, p = 0.016), in EPI 0.02-group after injection 2 and 3 (p = 0.023, p = 0.027) and in EPI 0.03-group after injection 1 (p = 0.013). At P_max, APV increased only after first injection in both the EPI-all and the EPI 0.03-group compared with the control group (p = 0.011, p = 0.018). There was no statistical difference of AMSA at any P_max. Seven out of 12 animals (58%) in each EPI-group versus 10 out of 12 (83%) achieved spontaneous circulation after CA.

Conclusion

In an experimental CA-CPR pig model repeated doses of intravenous EPI results in a significant increase in APV only after the first injection despite increments in CPP also during the following 2 injections indicating inappropriate changes in coronary vascular resistance during subsequent EPI administration.

The ventricular fibrillation waveform approach to direct postshock chest compressions in a swine model of VF arrest

BACKGROUND

In retrospective swine and human investigations of ventricular fibrillation (VF) cardiac arrest, the amplitude-spectral area (AMSA), determined from the VF waveform, can predict defibrillation and a return of spontaneous circulation (ROSC).

OBJECTIVES

We hypothesized that an algorithm using AMSA in real time to direct postshock chest compression (CC) duration would shorten the time to ROSC and improve neurological outcome in a swine model of VF cardiac arrest with acute myocardial infarction (AMI) or nonischemic myocardium.

METHODS

AMI was induced by occlusion of the left anterior descending artery. VF was untreated for 10 min. Animals were randomized to either traditional resuscitation with 2 min of CC after each shock or to an AMSA-guided algorithm where postshock CCs were shortened to 1 min if the preshock AMSA exceeded 20 mV-Hz.

RESULTS

A total of 48 animals were studied, 12 in each group (AMI vs. normal, and traditional vs. AMSA-guided). There was a nonsignificant shorter time to ROSC with an AMSA-guided approach in AMI swine (17.2 ± 3.4 vs. 18.5 ± 4.7 min, p = NS), and in normal swine (13.5 ± 1.1 vs. 14.4 ± 1.2, p = NS). Neurological outcome was similar between traditional and AMSA-guided animals. AMSA predicted ROSC (p < 0.001), and a threshold of 20 mV-Hz gave a sensitivity of 89%, with specificity of 29%.

CONCLUSION

Although AMSA predicts ROSC in a swine model of VF arrest in both AMI and normal swine, a waveform-guided approach that uses AMSA to direct postshock CC duration does not significantly shorten the time to ROSC or alter neurological outcome.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Signal integral for optimizing the timing of defibrillation

OBJECTIVE

The possibility of successful defibrillation decreases with an increased duration of ventricular fibrillation (VF). Futile electrical shocks are inversely correlated with myocardial contractile function and long-term survival. Previous studies have demonstrated that various ECG waveform analyses predict the success of defibrillation. This study investigated whether the absolute amplitude of pre-shock VF waveform is likely to predict the success of defibrillation.

METHODS

ECG recordings of 350 out-of-hospital cardiac arrest (OOHCA) patients were obtained from the automated external defibrillator (AED) and analyzed by the method of signal integral. Successful defibrillation was defined as organized rhythm with heart rate ≥40beat/min commencing within one min of post-shock period and persisting for a minimum of 30s.

RESULTS

Signal integral was significantly greater in successful defibrillation than unsuccessful defibrillation (81.76±32.3mV vs. 34.9±15.33mV, p<0.001). The intersection of the sensitivity and specificity curve provided a threshold value of 51mV. The corresponding values of sensitivity, specificity, positive predictive and negative predictive values for successful defibrillation were 90%, 86%, 80% and 93%, respectively. The receiver operator curve further revealed that signal integral predicted the likelihood of successful defibrillation (area under the curve=0.949).

CONCLUSIONS

Signal integral predicted successful electrical shocks on patients with ventricular fibrillation and have potential to optimize the timing of defibrillation and reduce the number of electrical shocks.

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

Background

Ventricular 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.

Methods

A 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.

Results

The 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.

Conclusion

We 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.

Prediction of countershock success in patients using the autoregressive spectral estimation

OBJECTIVES

Ventricular fibrillation (VF) is a life-threatening cardiac arrhythmia and within of minutes of its occurrence, optimal timing of countershock therapy is highly warranted to improve the chance of survival. This study was designed to investigate whether the autoregressive (AR) estimation technique was capable to reliably predict countershock success in VF cardiac arrest patients.

METHODS

ECG data of 1077 countershocks applied to 197 cardiac arrest patients with out-of-hospital and in-hospital cardiac arrest between March 2002 and July 2004 were retrospectively analyzed. The ECG from the 2.5 s interval of the precountershock VF ECG was used for computing the AR based features Spectral Pole Power (SPP) and Spectral Pole Power with Dominant Frequency weighing (SPPDF) and Centroid Frequency (CF) and Amplitude Spectrum Area (AMSA) based on Fast Fourier Transformation (FFT).

RESULTS

With ROC AUC values up to 84.1% and diagnostic odds ratio up to 19.12 AR based features SPP and SPPDF have better prediction power than the FFT based features CF (80.5%; 6.56) and AMSA (82.1%; 8.79).

CONCLUSIONS

AR estimation based features are promising alternatives to FFT based features for countershock outcome when analyzing human data.

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

OBJECTIVE

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.

DESIGN

Animal intervention study with comparison to a control group.

SETTING

University animal laboratory.

SUBJECTS

Twenty swine.

INTERVENTIONS

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).

MEASUREMENTS AND MAIN RESULTS

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).

CONCLUSIONS

: 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.

Part 8: adult advanced cardiovascular life support: 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care

The goal of therapy for bradycardia or tachycardia is to rapidly identify and treat patients who are hemodynamically unstable or symptomatic due to the arrhythmia. Drugs or, when appropriate, pacing may be used to control unstable or symptomatic bradycardia. Cardioversion or drugs or both may be used to control unstable or symptomatic tachycardia. ACLS providers should closely monitor stable patients pending expert consultation and should be prepared to aggressively treat those with evidence of decompensation.