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Effects of Cardiopulmonary Resuscitation on Predictors of Ventricular Fibrillation Defibrillation Success During Out-of-Hospital Cardiac Arrest

Background—Early defibrillation is considered the most important factor for restoring spontaneous circulation in cardiac arrest patients with ventricular fibrillation. Recent studies have shown that, after prolonged ventricular fibrillation, the rates of return of spontaneous circulation (ROSC) and survival are improved if defibrillation is delayed so that CPR can be given first. To examine whether CPR improves myocardial readiness for defibrillation, we analyzed whether CPR causes changes in predictors of defibrillation success calculated from the ventricular fibrillation waveform. Methods and Results—ECG recordings were retrieved for 105 patients from an original study of 200 patients receiving CPR or defibrillation first. Altogether, 267 CPR sequences from 77 patients were identified on which the effect of CPR could be evaluated. Five predictors of ROSC (spectral flatness measure, energy, centroid frequency, amplitude spectrum relationship, and estimated probability of ROSC) were determined from a spectral analysis of the ventricular fibrillation waveform immediately before and immediately after each of the 267 sequences. CPR increased spectral flatness measure, centroid frequency, and amplitude spectrum relationship (P <0.05, P <0.001, P <0.01). In an analysis of the effect of the duration of CPR, the probability of ROSC and amplitude spectrum relationship showed a positive change for CPR sequences lasting >3 minutes (P <0.001, P <0.05). Conclusions—During resuscitation from ventricular fibrillation, changes in the predictors calculated from the ventricular fibrillation waveform indicated a positive effect of CPR on the myocardium.

Predicting Outcome of Defibrillation by Spectral Characterization and Nonparametric Classification of Ventricular Fibrillation in Patients With Out-of-Hospital Cardiac Arrest

BackgroundIn 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 ResultsCentroid 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. ConclusionsThe 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.

Removal of cardiopulmonary resuscitation artifacts from human ECG using an efficient matching pursuit-like algorithm

We present a computationally efficient and numerically robust solution to the problem of removing artifacts due to precordial compressions and ventilations from the human electrocardiogram (ECG) in an emergency medicine setting. Incorporated into automated external defibrillators, this would allow for simultaneous ECG signal analysis and administration of precordial compressions and ventilations, resulting in significant clinical improvement to the treatment of cardiac arrest patients. While we have previously demonstrated the feasibility of such artifact removal using a multichannel Wiener filter, we here focus on an efficient matching pursuit-like approach making practical real-time implementations of such a scheme feasible for a wide variety of sampling rates and filter lengths. Using more realistic data than what have been previously available, we present evidence showing the excellent performance of our approach and quantify its computational complexity.

CPR artifact removal from human ECG using optimal multichannel filtering

The purpose of this study was to assess whether the artifacts presented by precordial compressions during cardiopulmonary resuscitation could be removed from the human electrocardiogram (ECG) using a filtering approach. This would allow analysis and defibrillator charging during ongoing precordial compressions yielding a very important clinical improvement to the treatment of cardiac arrest patients. In this investigation the authors started with noise-free human ECGs with ventricular fibrillation (VF) and ventricular tachycardia (VT) records. To simulate a realistic resuscitation situation, they added a weighted artifact signal to the human ECG, where the weight factor was chosen to provide the desired signal-to-noise ratio (SNR) level. As artifact signals the authors used ECGs recorded from animals in asystole during precordial compressions at rates 60, 90, and 120 compressions/min. The compression depth and the thorax impedance was also recorded. In a real-life situation such reference signals are available and, using an adaptive multichannel Wiener filter, the authors construct an estimate of the artifact signal, which subsequently can be subtracted from the noisy human ECG signal. The success of the proposed method is demonstrated through graphic examples, SNR, and rhythm classification evaluations.

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.

A Least Mean-Square Filter for the Estimation of the Cardiopulmonary Resuscitation Artifact Based on the Frequency of the Compressions

Cardiopulmonary resuscitation (CPR) artifacts caused by chest compressions and ventilations interfere with the rhythm diagnosis of automated external defibrillators (AED). CPR must be interrupted for a reliable diagnosis. However, pauses in chest compressions compromise the defibrillation success rate and reduce perfusion of vital organs. The removal of the CPR artifacts would enable compressions to continue during AED rhythm analysis, thereby increasing the likelihood of resuscitation success. We have estimated the CPR artifact using only the frequency of the compressions as additional information to model it. Our model of the artifact is adaptively estimated using a least mean-square (LMS) filter. It was tested on 89 shockable and 292 nonshockable ECG samples from real out-of-hospital sudden cardiac arrest episodes. We evaluated the results using the shock advice algorithm of a commercial AED. The sensitivity and specificity were above 95% and 85%, respectively, for a wide range of working conditions of the LMS filter. Our results show that the CPR artifact can be accurately modeled using only the frequency of the compressions. These can be easily registered after small changes in the hardware of the CPR compression pads.

Development of the probability of return of spontaneous circulation in intervals without chest compressions during out-of-hospital cardiac arrest: an observational study

BackgroundOne of the factors that limits survival from out-of-hospital cardiac arrest is the interruption of chest compressions. During ventricular fibrillation and tachycardia the electrocardiogram reflects the probability of return of spontaneous circulation associated with defibrillation. We have used this in the current study to quantify in detail the effects of interrupting chest compressions.MethodsFrom an electrocardiogram database we identified all intervals without chest compressions that followed an interval with compressions, and where the patients had ventricular fibrillation or tachycardia. By calculating the mean-slope (a predictor of the return of spontaneous circulation) of the electrocardiogram for each 2-second window, and using a linear mixed-effects statistical model, we quantified the decline of mean-slope with time. Further, a mapping from mean-slope to probability of return of spontaneous circulation was obtained from a second dataset and using this we were able to estimate the expected development of the probability of return of spontaneous circulation for cases at different levels.ResultsFrom 911 intervals without chest compressions, 5138 analysis windows were identified. The results show that cases with the probability of return of spontaneous circulation values 0.35, 0.1 and 0.05, 3 seconds into an interval in the mean will have probability of return of spontaneous circulation values 0.26 (0.24–0.29), 0.077 (0.070–0.085) and 0.040(0.036–0.045), respectively, 27 seconds into the interval (95% confidence intervals in parenthesis).ConclusionDuring pre-shock pauses in chest compressions mean probability of return of spontaneous circulation decreases in a steady manner for cases at all initial levels. Regardless of initial level there is a relative decrease in the probability of return of spontaneous circulation of about 23% from 3 to 27 seconds into such a pause.

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).

Suppression of the cardiopulmonary resuscitation artefacts using the instantaneous chest compression rate extracted from the thoracic impedance

AIM To demonstrate that the instantaneous chest compression rate can be accurately estimated from the transthoracic impedance (TTI), and that this estimated rate can be used in a method to suppress cardiopulmonary resuscitation (CPR) artefacts. METHODS A database of 372 records, 87 shockable and 285 non-shockable, from out-of-hospital cardiac arrest episodes, corrupted by CPR artefacts, was analysed. Each record contained the ECG and TTI obtained from the defibrillation pads and the compression depth (CD) obtained from a sternal CPR pad. The chest compression rates estimated using TTI and CD were compared. The CPR artefacts were then filtered using the instantaneous chest compression rates estimated from the TTI or CD signals. The filtering results were assessed in terms of the sensitivity and specificity of the shock advice algorithm of a commercial automated external defibrillator. RESULTS The correlation between the mean chest compression rates estimated using TTI or CD was r=0.98 (95% confidence interval, 0.97-0.98). The sensitivity and specificity after filtering using CD were 95.4% (88.4-98.6%) and 87.0% (82.6-90.5%), respectively. The sensitivity and specificity after filtering using TTI were 95.4% (88.4-98.6%) and 86.3% (81.8-89.9%), respectively. CONCLUSIONS The instantaneous chest compression rate can be accurately estimated from TTI. The sensitivity and specificity after filtering are similar to those obtained using the CD signal. Our CPR suppression method based exclusively on signals acquired through the defibrillation pads is as accurate as methods based on signals obtained from CPR feedback devices.

Thoracic impedance changes measured via defibrillator pads can monitor ventilation in critically ill patients and during cardiopulmonary resuscitation.

Objective:Monitoring of ventilation performance during cardiopulmonary resuscitation would be desirable to improve the quality of cardiopulmonary resuscitation. To investigate the potential for measuring ventilation rate and inspiration time, we calculated the correlation in waveform between transthoracic impedance measured via defibrillator pads and tidal volume given by a ventilator. Design:Clinical study. Setting:Emergency department of a tertiary care university hospital. Patients:A convenience sample of mechanical ventilated patients (n = 32), cardiac arrest patients (n = 20), and patients after restoration of spontaneous circulation (n = 31) older than 18 were eligible. Interventions:The Heartstart 4000SP defibrillator (Laerdal Medical Cooperation, Stavanger, Norway) with additional capabilities of recording thoracic impedance changes was used. Measurements and Main Results:The relationship between impedance change and tidal volume (impedance coefficient) was calculated. The mean (sd) correlations between the impedance waveform and the tidal volume waveform in the patient groups studied were .971 (.027), .969 (.032), and .967 (.035), respectively. The mean (sd) impedance coefficient for all patients in the study was .00194 (.0078) &OHgr;/mL, and the mean (sd) specific (weight-corrected) impedance coefficient was .152 (.048) &OHgr;/kg/mL. The measured thorax impedance change for different tidal volumes (400–1000 mL) was approximately linear. Conclusions:The impedance sensor of a defibrillator is accurate in identifying tidal volumes, when chest compressions are interrupted. This also allows quantifying ventilation rates and inspiration times. However this technology, at its present state, provides only limited practical means for exact tidal volume estimation.