Yongqin Li

Amplitude spectrum area to guide resuscitation—A retrospective analysis during out-of-hospital cardiopulmonary resuscitation in 609 patients with ventricular fibrillation cardiac arrest☆

INTRODUCTION The capability of amplitude spectrum area (AMSA) to predict the success of defibrillation (DF) was retrospectively evaluated in a large database of out-of-hospital cardiac arrests. METHODS Electrocardiographic data, including 1260 DFs, were obtained from 609 cardiac arrest patients due to ventricular fibrillation. AMSA sensitivity, specificity, accuracy, and positive and negative predictive values (PPV, NPV) for predicting DF success were calculated, together with receiver operating characteristic (ROC) curves. Successful DF was defined as the presence of spontaneous rhythm ≥40bpm starting within 60s from the DF. In 303 patients with chest compression (CC) depth data collected with an accelerometer, changes in AMSA were analyzed in relationship to CC depth. RESULTS AMSA was significantly higher prior to a successful DF than prior to an unsuccessful DF (15.6±0.6 vs. 7.97±0.2mV-Hz, p<0.0001). Intersection of sensitivity, specificity and accuracy curves identified a threshold AMSA of 10mV-Hz to predict DF success with a balanced sensitivity, specificity and accuracy of almost 80%. Higher AMSA thresholds were associated with further increases in accuracy, specificity and PPV. AMSA of 17mV-Hz predicted DF success in two third of instances (PPV of 67%). Low AMSA, instead, predicted unsuccessful DFs with high sensitivity and NPV >97%. Area under the ROC curve was 0.84. CC depth affected AMSA value. When depth was <1.75in., AMSA decreased for consecutive DFs, while it increased when the depth was >1.75in. (p<0.05). CONCLUSIONS AMSA could be a useful tool to guide CPR interventions and predict the optimal timing of DF.

Amplitude Spectrum Area to Guide Defibrillation A Validation on 1617 Patients With Ventricular Fibrillation

Background— This study sought to validate the ability of amplitude spectrum area (AMSA) to predict defibrillation success and long-term survival in a large population of out-of-hospital cardiac arrests. Methods and Results— ECGs recorded by automated external defibrillators from different manufacturers were obtained from patients with cardiac arrests occurring in 8 city areas. A database, including 2447 defibrillations from 1050 patients, was used as the derivation group, and an additional database, including 1381 defibrillations from 567 patients, served as validation. A 2-second ECG window before defibrillation was analyzed, and AMSA was calculated. Univariable and multivariable regression analyses and area under the receiver operating characteristic curve were used for associations between AMSA and study end points: defibrillation success, sustained return of spontaneous circulation, and long-term survival. Among the 2447 defibrillations of the derivation database, 26.2% were successful. AMSA was significantly higher before a successful defibrillation than a failing one (13±5 versus 6.8±3.5 mV-Hz) and was an independent predictor of defibrillation success (odds ratio, 1.33; 95% confidence interval, 1.20–1.37) and sustained return of spontaneous circulation (odds ratio, 1.22; 95% confidence interval, 1.17–1.26). Area under the receiver operating characteristic curve for defibrillation success prediction was 0.86 (95% confidence interval, 0.85–0.88). AMSA was also significantly associated with long-term survival. The following AMSA thresholds were identified: 15.5 mV-Hz for defibrillation success and 6.5 mV-Hz for defibrillation failure. In the validation database, AMSA ≥15.5 mV-Hz had a positive predictive value of 84%, whereas AMSA ⩽6.5 mV-Hz had a negative predictive value of 98%. Conclusions— In this large derivation-validation study, AMSA was validated as an accurate predictor of defibrillation success. AMSA also appeared as a predictor of long-term survival.Background— This study sought to validate the ability of amplitude spectrum area (AMSA) to predict defibrillation success and long-term survival in a large population of out-of-hospital cardiac arrests. Methods and Results— ECGs recorded by automated external defibrillators from different manufacturers were obtained from patients with cardiac arrests occurring in 8 city areas. A database, including 2447 defibrillations from 1050 patients, was used as the derivation group, and an additional database, including 1381 defibrillations from 567 patients, served as validation. A 2-second ECG window before defibrillation was analyzed, and AMSA was calculated. Univariable and multivariable regression analyses and area under the receiver operating characteristic curve were used for associations between AMSA and study end points: defibrillation success, sustained return of spontaneous circulation, and long-term survival. Among the 2447 defibrillations of the derivation database, 26.2% were successful. AMSA was significantly higher before a successful defibrillation than a failing one (13±5 versus 6.8±3.5 mV-Hz) and was an independent predictor of defibrillation success (odds ratio, 1.33; 95% confidence interval, 1.20–1.37) and sustained return of spontaneous circulation (odds ratio, 1.22; 95% confidence interval, 1.17–1.26). Area under the receiver operating characteristic curve for defibrillation success prediction was 0.86 (95% confidence interval, 0.85–0.88). AMSA was also significantly associated with long-term survival. The following AMSA thresholds were identified: 15.5 mV-Hz for defibrillation success and 6.5 mV-Hz for defibrillation failure. In the validation database, AMSA ≥15.5 mV-Hz had a positive predictive value of 84%, whereas AMSA ≤6.5 mV-Hz had a negative predictive value of 98%. Conclusions— In this large derivation-validation study, AMSA was validated as an accurate predictor of defibrillation success. AMSA also appeared as a predictor of long-term survival. # CLINICAL PERSPECTIVE {#article-title-40}

Transthoracic impedance for the monitoring of quality of manual chest compression during cardiopulmonary resuscitation

OBJECTIVE The quality of cardiopulmonary resuscitation (CPR), especially adequate compression depth, is associated with return of spontaneous circulation (ROSC) and is therefore recommended to be measured routinely. In the current study, we investigated the relationship between changes of transthoracic impedance (TTI) measured through the defibrillation electrodes, chest compression depth and coronary perfusion pressure (CPP) in a porcine model of cardiac arrest. METHODS In 14 male pigs weighing between 28 and 34 kg, ventricular fibrillation (VF) was electrically induced and untreated for 6 min. Animals were randomized to either optimal or suboptimal chest compression group. Optimal depth of manual compression in 7 pigs was defined as a decrease of 25% (50 mm) in anterior posterior diameter of the chest, while suboptimal compression was defined as 70% of the optimal depth (35 mm). After 2 min of chest compression, defibrillation was attempted with a 120-J rectilinear biphasic shock. RESULTS There were no differences in baseline measurements between groups. All animals had ROSC after optimal compressions; this contrasted with suboptimal compressions, after which only 2 of the animals had ROSC (100% vs. 28.57%, p=0.021). The correlation coefficient was 0.89 between TTI amplitude and compression depth (p<0.001), 0.83 between TTI amplitude and CPP (p<0.001). CONCLUSION Amplitude change of TTI was correlated with compression depth and CPP in this porcine model of cardiac arrest. The TTI measured from defibrillator electrodes, therefore has the potential to serve as an indicator to monitor the quality of chest compression and estimate CPP during CPR.

Current is better than energy as predictor of success for biphasic defibrillatory shocks in a porcine model of ventricular fibrillation

OBJECTIVE The evidence that monophasic defibrillation success is mainly determined by current is secure. However, modern defibrillators use biphasic waveforms. The aim of this study was to compare energy, peak voltage and peak current in predicting biphasic shock success in a porcine model of ventricular fibrillation (VF) where the impedance varies within a wide of ranges. METHODS In 14 domestic male pigs weighing between 27 and 38 kg, VF was electrically induced and untreated for 15 s. Animals were randomized to receive defibrillation attempts from one of two defibrillators with different impedance compensation methods. A grouped up-and-down defibrillation threshold testing protocol was used to maintain the average success rate in the neighborhood of 50%. After a recovery interval of 5 min, the testing sequence was repeated for a total of 60 test shocks for each animal. RESULTS A high defibrillation success was observed when high peak current was delivered. The area under ROC curve for predicting shock success was 0.681 for peak current, 0.585 for peak voltage and 0.562 for energy. The odds ratio revealed that peak current was a better predictor (OR=1.321, p<0.001) for defibrillation outcome compared with energy (OR=0.979, p<0.001) and peak voltage (OR=1.000, p=0.69) when multivariable logistic regression was conducted. CONCLUSION In this porcine model of VF within a wide range of transthoracic impedance, peak current was a better indicator for shock success than the currently used energy for biphasic defibrillatory shocks. This finding may encourage design of new current-based biphasic defibrillators.

Even Four Minutes of Poor Quality of CPR Compromises Outcome in a Porcine Model of Prolonged Cardiac Arrest

Objective. Untrained bystanders usually delivered suboptimal chest compression to victims who suffered from cardiac arrest in out-of-hospital settings. We therefore investigated the hemodynamics and resuscitation outcome of initial suboptimal quality of chest compressions compared to the optimal ones in a porcine model of cardiac arrest. Methods. Fourteen Yorkshire pigs weighted 30 ± 2 kg were randomized into good and poor cardiopulmonary resuscitation (CPR) groups. Ventricular fibrillation was electrically induced and untreated for 6 mins. In good CPR group, animals received high quality manual chest compressions according to the Guidelines (25% of animals anterior-posterior thoracic diameter) during first two minutes of CPR compared with poor (70% of the optimal depth) compressions. After that, a 120-J biphasic shock was delivered. If the animal did not acquire return of spontaneous circulation, another 2 mins of CPR and shock followed. Four minutes later, both groups received optimal CPR until total 10 mins of CPR has been finished. Results. All seven animals in good CPR group were resuscitated compared with only two in poor CPR group (P < 0.05). The delayed optimal compressions which followed 4 mins of suboptimal compressions failed to increase the lower coronary perfusion pressure of five non-survival animals in poor CPR group. Conclusions. In a porcine model of prolonged cardiac arrest, even four minutes of initial poor quality of CPR compromises the hemodynamics and survival outcome.

Electrical features of eighteen automated external defibrillators: A systematic evaluation

AIM Assessment and comparison of the electrical parameters (energy, current, first and second phase waveform duration) among eighteen AEDs. METHOD Engineering bench tests for a descriptive systematic evaluation in commercially available AEDs. AEDs were tested through an ECG simulator, an impedance simulator, an oscilloscope and a measuring device detecting energy delivered, peak and average current, and duration of first and second phase of the biphasic waveforms. All tests were performed at the engineering facility of the Lombardia Regional Emergency Service (AREU). RESULTS Large variations in the energy delivered at the first shock were observed. The trend of current highlighted a progressive decline concurrent with the increases of impedance. First and second phase duration varied substantially among the AEDs using the exponential biphasic waveform, unlike rectilinear waveform AEDs in which phase duration remained relatively constant. CONCLUSIONS There is a large variability in the electrical features of the AEDs tested. Energy is likely not to be the best indicator for strength dose selection. Current and shock duration should be both considered when approaching the technical features of AEDs. These findings may prompt further investigations to define the optimal current and duration of the shock waves to increase the success rate in the clinical setting.

A Review of the Performance of Artifact Filtering Algorithms for Cardiopulmonary Resuscitation

Various filtering strategies have been adopted and investigated to suppress the cardiopulmonary resuscitation (CPR) artifact. In this article, two types of artifact removal methods are reviewed: one is the method that removes CPR artifact using only ECG signals, and the other is the method with additional reference signals, such as acceleration, compression depth and transthoracic impedance. After filtering, the signal-to-noise ratio is improved from 0 dB to greater than 2.8 dB, the sensitivity is increased to > 90% as recommended by the American Heart Association, whereas the specificity was far from the recommended 95%, which is considered to be the major drawback of the available artifact removal methods. The overall performance of the adaptive filtering methods with additional reference signal outperforms the methods using only ECG signals. Further research should focus on the refinement of artifact filtering methods and the improvement of shock advice algorithms with the presence of CPR.

Combining multiple ECG features does not improve prediction of defibrillation outcome compared to single features in a large population of out-of-hospital cardiac arrests

IntroductionQuantitative electrocardiographic (ECG) waveform analysis provides a noninvasive reflection of the metabolic milieu of the myocardium during resuscitation and is a potentially useful tool to optimize the defibrillation strategy. However, whether combining multiple ECG features can improve the capability of defibrillation outcome prediction in comparison to single feature analysis is still uncertain.MethodsA total of 3828 defibrillations from 1617 patients who experienced out-of-hospital cardiac arrest were analyzed. A 2.048-s ECG trace prior to each defibrillation without chest compressions was used for the analysis. Sixteen predictive features were optimized through the training dataset that included 2447 shocks from 1050 patients. Logistic regression, neural network and support vector machine were used to combine multiple features for the prediction of defibrillation outcome. Performance between single and combined predictive features were compared by area under receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and prediction accuracy (PA) on a validation dataset that consisted of 1381 shocks from 567 patients.ResultsAmong the single features, mean slope (MS) outperformed other methods with an AUC of 0.876. Combination of complementary features using neural network resulted in the highest AUC of 0.874 among the multifeature-based methods. Compared to MS, no statistical difference was observed in AUC, sensitivity, specificity, PPV, NPV and PA when multiple features were considered.ConclusionsIn this large dataset, the amplitude-related features achieved better defibrillation outcome prediction capability than other features. Combinations of multiple electrical features did not further improve prediction performance.

Removal of Cardiopulmonary Resuscitation Artifacts with an Enhanced Adaptive Filtering Method: An Experimental Trial

Current automated external defibrillators mandate interruptions of chest compression to avoid the effect of artifacts produced by CPR for reliable rhythm analyses. But even seconds of interruption of chest compression during CPR adversely affects the rate of restoration of spontaneous circulation and survival. Numerous digital signal processing techniques have been developed to remove the artifacts or interpret the corrupted ECG with promising result, but the performance is still inadequate, especially for nonshockable rhythms. In the present study, we suppressed the CPR artifacts with an enhanced adaptive filtering method. The performance of the method was evaluated by comparing the sensitivity and specificity for shockable rhythm detection before and after filtering the CPR corrupted ECG signals. The dataset comprised 283 segments of shockable and 280 segments of nonshockable ECG signals during CPR recorded from 22 adult pigs that experienced prolonged cardiac arrest. For the unfiltered signals, the sensitivity and specificity were 99.3% and 46.8%, respectively. After filtering, a sensitivity of 93.3% and a specificity of 96.0% were achieved. This animal trial demonstrated that the enhanced adaptive filtering method could significantly improve the detection of nonshockable rhythms without compromising the ability to detect a shockable rhythm during uninterrupted CPR.

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

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