Electrocardiographic analysis during uninterrupted cardiopulmonary resuscitation

Emergency medicine updates: Defibrillation strategies in cardiac arrest

INTRODUCTION

Cardiac arrest is a commonly managed condition in the emergency department (ED), and defibrillation of shockable rhythms is a key component of treatment, along with high-quality chest compressions.

OBJECTIVE

This narrative review seeks to evaluate evidence-based updates concerning defibrillation in cardiac arrest.

DISCUSSION

Cardiac arrest management includes cardiopulmonary resuscitation (CPR) and defibrillation of shockable rhythms. CPR should be provided until a defibrillator is applied. In those with pulseless ventricular tachycardia or ventricular fibrillation, defibrillation should be performed as soon as possible. If the arrest is unwitnessed, or there will be a delay in rhythm analysis or applying/obtaining a defibrillator, CPR should be performed while the defibrillator is being obtained and prepared for use. Biphasic waveform defibrillators are recommended. Shorter pre- and peri-shock pauses are associated with higher survival rates. Charging the defibrillator during chest compressions, holding compressions for rhythm analysis alone, and immediately resuming compressions following defibrillation are recommended to maximize chest compression fraction. Two common defibrillator pad configurations include anterolateral (AL) and anterior-posterior (AP). Vector-change defibrillation can be attempted if the first defibrillation attempt is unsuccessful. Double defibrillation (DD) includes either double simultaneous defibrillation (DSD) or dual sequential external defibrillation (DSED), though DSED is more common. DD utilizes two biphasic defibrillators and two sets of defibrillator pads in an AL and AP configuration. If the patient is refractory to 3 or more defibrillation attempts, DD may be attempted. Defibrillator damage with DD is rare, and clinicians must consider the potential survival benefit with DD, patient and provider safety issues, cost, and system-level impact when using two defibrillators.

CONCLUSIONS

An understanding of literature updates focused on defibrillation can improve the ED care of patients in cardiac arrest.

Enhancing the accuracy of shock advisory algorithms in automated external defibrillators during ongoing cardiopulmonary resuscitation using a cascade of CNNEDs

Delivery of continuous cardiopulmonary resuscitation (CPR) plays an important role in the out-of-hospital cardiac arrest (OHCA) survival rate. However, to prevent CPR artifacts being superimposed on ECG morphology data, currently available automated external defibrillators (AEDs) require pauses in CPR for accurate analysis heart rhythms. In this study, we propose a novel Convolutional Neural Network-based Encoder-Decoder (CNNED) structure with a shock advisory algorithm to improve the accuracy and reliability of shock versus non-shock decision-making without CPR pause in OHCA scenarios. Our approach employs a cascade of CNNEDs in conjunction with an AED shock advisory algorithm to process the ECG data for shock decisions. Initially, a CNNED trained on an equal number of shockable and non-shockable rhythms is used to filter the CPR-contaminated data. The resulting filtered signal is then fed into a second CNNED, which is trained on imbalanced data more tilted toward the specific rhythm being analyzed. A reliable shock versus non-shock decision is made when both classifiers from the cascade structure agree, while segments with conflicting classifications are labeled as indeterminate, indicating the need for additional segments to analyze. To evaluate our approach, we generated CPR-contaminated ECG data by combining clean ECG data with 52 CPR samples. We used clean ECG data from the CUDB, AFDB, SDDB, and VFDB databases, to which 52 CPR artifact cases were added, while a separate test set provided by the AED manufacturer Defibtech LLC was used for performance evaluation. The test set comprised 20,384 non-shockable CPR-contaminated segments from 392 subjects, as well as 3744 shockable CPR-contaminated samples from 41 subjects with coarse ventricular fibrillation (VF) and 31 subjects with rapid ventricular tachycardia (rapid VT). We observed improvements in rhythm analysis using our proposed cascading CNNED structure when compared to using a single CNNED structure. Specifically, the specificity of the proposed cascade of CNNED structure increased from 99.14% to 99.35% for normal sinus rhythm and from 96.45% to 97.22% for other non-shockable rhythms. Moreover, the sensitivity for shockable rhythm detection increased from 90.90% to 95.41% for ventricular fibrillation and from 82.26% to 87.66% for rapid ventricular tachycardia. These results meet the performance thresholds set by the American Heart Association and demonstrate the reliable and accurate analysis of heart rhythms during CPR using only ECG data without the need for CPR interruptions or a reference signal.

Deep Learning Strategy for Sliding ECG Analysis during Cardiopulmonary Resuscitation: Influence of the Hands-Off Time on Accuracy

This study aims to present a novel deep learning algorithm for a sliding shock advisory decision during cardiopulmonary resuscitation (CPR) and its performance evaluation as a function of the cumulative hands-off time. We retrospectively used 13,570 CPR episodes from out-of-hospital cardiac arrest (OHCA) interventions reviewed in a period of interest from 30 s before to 10 s after regular analysis of automated external defibrillators (AEDs). Three convolutional neural networks (CNNs) with raw ECG input (duration of 5, 10, and 15 s) were applied for the shock advisory decision during CPR in 26 sequential analyses shifted by 1 s. The start and stop of chest compressions (CC) can occur at arbitrary times in sequential slides; therefore, the sliding hands-off time (sHOT) quantifies the cumulative CC-free portion of the analyzed ECG. An independent test with CPR episodes in 393 ventricular fibrillations (VF), 177 normal sinus rhythms (NSR), 1848 other non-shockable rhythms (ONR), and 3979 asystoles (ASYS) showed a substantial improvement of VF sensitivity when increasing the analysis duration from 5 s to 10 s. Specificity was not dependent on the ECG analysis duration. The 10 s CNN model presented the best performance: 92-94.4% (VF), 92.2-94% (ASYS), 96-97% (ONR), and 98.2-99.5% (NSR) for sliding decision times during CPR; 98-99% (VF), 98.2-99.8% (ASYS), 98.8-99.1 (ONR), and 100% (NSR) for sliding decision times after end of CPR. We identified the importance of sHOT as a reliable predictor of performance, accounting for the minimal sHOT interval of 2-3 s that provides a reliable rhythm detection satisfying the American Heart Association (AHA) standards for AED rhythm analysis. The presented technology for sliding shock advisory decision during CPR achieved substantial performance improvement in short hands-off periods (>2 s), such as insufflations or pre-shock pauses. The performance was competitive despite 1-2.8% point lower ASYS detection during CPR than the standard requirement (95%) for non-noisy ECG signals. The presented deep learning strategy is a basis for improved CPR practices involving both continuous CC and CC with insufflations, associated with minimal CC interruptions for reconfirmation of non-shockable rhythms (minimum hands-off time) and early treatment of VF (minimal pre-shock pauses).

Numb and Numblike regulate sarcomere assembly and maintenance

A sarcomere is the contractile unit of the myofibril in striated muscles such as cardiac and skeletal muscles. The assembly of sarcomeres depends on multiple molecules that serve as raw materials and participate in the assembly process. However, the mechanism of this critical assembly process remains largely unknown. Here, we found that the cell fate determinant Numb and its homolog Numblike regulated sarcomere assembly and maintenance in striated muscles. We discovered that Numb and Numblike are sarcomeric molecules that were gradually confined to the Z-disc during striated muscle development. Conditional knockout of Numb and Numblike severely compromised sarcomere assembly and its integrity and thus caused organelle dysfunction. Notably, we identified that Numb and Numblike served as sarcomeric α-Actin-binding proteins (ABPs) and shared a conserved domain that can bind to the barbed end of sarcomeric α-Actin. In vitro fluorometric α-Actin polymerization assay showed that Numb and Numblike also played a role in the sarcomeric α-Actin polymerization process. Last, we demonstrate that Numb and Numblike regulate sarcomeric α-Actinin-dependent (ACTN-dependent) Z-disc consolidation in the sarcomere assembly and maintenance. In summary, our studies show that Numb and its homolog Numblike regulate sarcomere assembly and maintenance in striated muscles, and demonstrate a molecular mechanism by which Numb/Numblike, sarcomeric α-Actin, and ACTN cooperate to control thin filament formation and Z-disc consolidation.

Effectiveness of near-infrared spectroscopy-guided continuous chest compression resuscitation without rhythm check in patients with out-of-hospital cardiac arrest: The prospective multicenter TripleCPR 16 study

BACKGROUND

The proportion of adult patients with return of spontaneous circulation (ROSC) following out-of-hospital cardiac arrest (OHCA) remains unchanged since 2012. A better resuscitation strategy is needed. This study evaluated the effectiveness of a regional cerebral oxygen saturation (rSO₂)-guided resuscitation protocol without rhythm check based on our previous study.

METHODS

Because defibrillation is the definitive therapy that should be performed without delay for shockable rhythm, the study subjects were OHCA patients with non-shockable rhythm on hospital arrival at three emergency departments. They were divided into three groups based on their baseline rSO₂ value (%): ≥50, ≥40 to <50, or <40. Continuous chest compression without rhythm checks was performed for 16 minutes or until a maximum increase in rSO₂ of 10%, 20%, or 35% was achieved in each group, respectively. This intervention cohort was compared with a historical control cohort regarding the probability of ROSC using inverse probability of treatment weighting (IPTW) with propensity score.

RESULTS

The control and intervention cohorts respectively included 86 and 225 patients. The rate of ROSC was not significantly different between the groups (adjusted OR 0.91 [95% CI, 0.64-1.29], P = 0.60), but no serious adverse events occurred. Sensitivity analyses 1 and 2 showed a significant difference or positive tendency for higher probability of ROSC (adjusted OR 1.63 [95% CI, 1.22-2.17], P < 0.001) (adjusted OR 1.25 [95% CI, 0.95-1.63], P = 0.11).

CONCLUSIONS

This trial suggested that a new cardiopulmonary resuscitation protocol with different rhythm check timing could be created using the rSO₂ value. Clinical trial number: UMIN000025684.

Optimization of End-to-End Convolutional Neural Networks for Analysis of Out-of-Hospital Cardiac Arrest Rhythms during Cardiopulmonary Resuscitation

High performance of the shock advisory analysis of the electrocardiogram (ECG) during cardiopulmonary resuscitation (CPR) in out-of-hospital cardiac arrest (OHCA) is important for better management of the resuscitation protocol. It should provide fewer interruptions of chest compressions (CC) for non-shockable organized rhythms (OR) and Asystole, or prompt CC stopping for early treatment of shockable ventricular fibrillation (VF). Major disturbing factors are strong CC artifacts corrupting raw ECG, which we aimed to analyze with optimized end-to-end convolutional neural network (CNN) without pre-filtering or additional sensors. The hyperparameter random search of 1500 CNN models with 2-7 convolutional layers, 5-50 filters and 5-100 kernel sizes was done on large databases from independent OHCA interventions for training (3001 samples) and validation (2528 samples). The best model, named CNN3-CC-ECG network with three convolutional layers (filters@kernels: 5@5,25@20,50@20) presented Sensitivity Se(VF) = 89%(268/301), Specificity Sp(OR) = 91.7%(1504/1640), Sp(Asystole) = 91.1%(3325/3650) on an independent test OHCA database. CNN3-CC-ECG's ability to effectively extract features from raw ECG signals during CPR was comprehensively demonstrated, and the dependency on the CPR corruption level in ECG was tested. We denoted a significant drop of Se(VF) = 74.2% and Sp(OR) = 84.6% in very strong CPR artifacts with a signal-to-noise ratio of SNR < -9 dB, p < 0.05. Otherwise, for strong, moderate and weak CC artifacts (SNR > -9 dB, -6 dB, -3 dB), we observed insignificant performance differences: Se(VF) = 92.5-96.3%, Sp(OR) = 93.4-95.5%, Sp(Asystole) = 92.6-94.0%, p > 0.05. Performance stability with respect to CC rate was validated. Generalizable application of the optimized computationally efficient CNN model was justified by an independent OHCA database, which to our knowledge is the largest test dataset with real-life cardiac arrest rhythms during CPR.

Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation

Background Because chest compressions induce artifacts in the ECG, current automated external defibrillators instruct the user to stop cardiopulmonary resuscitation (CPR) while an automated rhythm analysis is performed. It has been shown that minimizing interruptions in CPR increases the chance of survival. Methods and Results The objective of this study was to apply a deep-learning algorithm using convolutional layers, residual networks, and bidirectional long short-term memory method to classify shockable versus nonshockable rhythms in the presence and absence of CPR artifact. Forty subjects' data from Physionet with 1131 shockable and 2741 nonshockable samples contaminated with 43 different CPR artifacts that were acquired from a commercial automated external defibrillator during asystole were used. We had separate data as train and test sets. Using our deep neural network model, the sensitivity and specificity of the shock versus no-shock decision for the entire data set over the 4-fold cross-validation sets were 95.21% and 86.03%, respectively. This result was based on the training and testing of the model using ECG data in both the presence and the absence of CPR artifact. For ECG without CPR artifact, the sensitivity was 99.04% and the specificity was 95.2%. A sensitivity of 94.21% and a specificity of 86.14% were obtained for ECG with CPR artifact. In addition to 4-fold cross-validation sets, we also examined leave-one-subject-out validation. The sensitivity and specificity for the case of leave-one-subject-out validation were 92.71% and 97.6%, respectively. Conclusions The proposed trained model can make shock versus nonshock decision in automated external defibrillators, regardless of CPR status. The results meet the American Heart Association's sensitivity requirement (>90%).

Adult Basic Life Support: International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

This 2020 International Consensus on Cardiopulmonary Resuscitation (CPR) and Emergency Cardiovascular Care Science With Treatment Recommendations on basic life support summarizes evidence evaluations performed for 20 topics that were prioritized by the Basic Life Support Task Force of the International Liaison Committee on Resuscitation. The evidence reviews include 16 systematic reviews, 3 scoping reviews, and 1 evidence update. Per agreement within the International Liaison Committee on Resuscitation, new or revised treatment recommendations were only made after a systematic review.

Systematic reviews were performed for the following topics: dispatch diagnosis of cardiac arrest, use of a firm surface for CPR, sequence for starting CPR (compressions-airway-breaths versus airway-breaths-compressions), CPR before calling for help, duration of CPR cycles, hand position during compressions, rhythm check timing, feedback for CPR quality, alternative techniques, public access automated external defibrillator programs, analysis of rhythm during chest compressions, CPR before defibrillation, removal of foreign-body airway obstruction, resuscitation care for suspected opioid-associated emergencies, drowning, and harm from CPR to victims not in cardiac arrest.

The topics that resulted in the most extensive task force discussions included CPR during transport, CPR before calling for help, resuscitation care for suspected opioid-associated emergencies, feedback for CPR quality, and analysis of rhythm during chest compressions. After discussion of the scoping reviews and the evidence update, the task force prioritized several topics for new systematic reviews.

Adult Basic Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

This2020 International Consensus on Cardiopulmonary Resuscitation(CPR)and Emergency Cardiovascular Care Science With Treatment Recommendationson basic life support summarizes evidence evaluations performed for 22 topics that were prioritized by the Basic Life Support Task Force of the International Liaison Committee on Resuscitation. The evidence reviews include 16 systematic reviews, 5 scoping reviews, and 1 evidence update. Per agreement within the International Liaison Committee on Resuscitation, new or revised treatment recommendations were only made after a systematic review.

Systematic reviews were performed for the following topics: dispatch diagnosis of cardiac arrest, use of a firm surface for CPR, sequence for starting CPR (compressions-airway-breaths versus airway-breaths-compressions), CPR before calling for help, duration of CPR cycles, hand position during compressions, rhythm check timing, feedback for CPR quality, alternative techniques, public access automated external defibrillator programs, analysis of rhythm during chest compressions, CPR before defibrillation, removal of foreign-body airway obstruction, resuscitation care for suspected opioid-associated emergencies, drowning, and harm from CPR to victims not in cardiac arrest.

The topics that resulted in the most extensive task force discussions included CPR during transport, CPR before calling for help, resuscitation care for suspected opioid-associated emergencies, feedback for CPR quality, and analysis of rhythm during chest compressions. After discussion of the scoping reviews and the evidence update, the task force prioritized several topics for new systematic reviews.

The performance of a new shock advisory algorithm to reduce interruptions during CPR

OBJECTIVE

To explore a new algorithm and strategy for rhythm analysis during chest compressions (CCs), and to improve the efficiency of cardiopulmonary resuscitation (CPR) by minimizing interruptions.

METHODS

The clinical data and ECG of patients with sudden cardiac arrest (CA) from three hospitals in China were collected with Philips MRx monitor/defibrillators. The length of each analyzed ECG segment was 23 s, the first 11.5 s was selected to contain CPR compressions, the next 5 s had no compressions, and the last 6.5 s had no requirement. Three experienced emergency doctors annotated the ECG segments without compression artifacts. A two-step analysis through CPR (ATC) algorithm was applied to the selected data. The first step was analysis during chest compressions. If a shockable rhythm was not detected, compression-free analysis followed. The results of the ATC algorithm were compared with the annotations by the physicians, to determine the sensitivity and specificity of the algorithm.

RESULTS

In total 166 CA patients were included with 100 out-of-hospital cardiac arrest (OHCA) patients and 66 in-hospital cardiac arrest (IHCA) patients. A total of 1578 ECG segments were analyzed, including 115 (7.3%) shockable rhythms, 1278 (81.0%) non-shockable rhythms, and 185 (11.7%) intermediate/unknown rhythms. The specificity of all non-shockable rhythms was 99.8% at the end of chest compressions, and 99.5% after analysis without compression artifact. 70.5% of ventricular fibrillation (VF) rhythms were detected by the end of chest compressions. After the CC-free analysis, 93.6% of VF was identified.

CONCLUSION

The ATC algorithm achieved sensitivity of 93.6% and specificity of 99.5% after the two-step analysis, and 70.5% of the patients with shockable rhythms did not require CC-free analysis. Such an approach has the potential to substantially reduce CC interruptions when identifying shockable rhythms.

An Enhanced Adaptive Filtering Method for Suppressing Cardiopulmonary Resuscitation Artifact

Cardiopulmonary resuscitation (CPR) must be interrupted for reliable rhythm analysis in current automatic external defibrillators because of artifacts produced by chest compressions. However, interruptions in CPR adversely affect the restoration of spontaneous circulation and survival. Suppressing CPR artifacts by digital signal processing techniques is a promising method to enable rhythm analysis during chest compressions, which would eliminate CPR interruptions for rhythm analysis. Although numerous methods have been developed to suppress CPR artifacts, the accuracy of rhythm analysis is still inadequate due to the residual artifact components in the filtered signal. This study proposes an enhanced adaptive filtering method to suppress CPR artifacts. A total of 183 shockable and 453 nonshockable segments of ECG signal, together with CPR-related reference signal, were extracted from 233 out of hospital cardiac arrest patients. The method was optimized on a training set with 85 shockable and 211 nonshockable segments, and evaluated on a testing set with 98 shockable and 242 nonshockable segments. Compared with artifact corrupted ECG signals, the signal-to-noise ratio (SNR) increased from -9.8 ± 12.5 to 11.2 ± 11.8 dB, and the accuracy was improved from 74.1% to 92.0% after filtering with the proposed method. Compared with the traditional adaptive filter, the SNR was improved by 1.7 dB and the accuracy was improved by 5.6 points. These results indicated that the proposed method could effectively suppress the chest compression related artifacts and improve the accuracy of rhythm analysis during uninterrupted CPR.

Hands-on defibrillation and electrocardiogram artefact filtering technology increases chest compression fraction and decreases peri-shock pause duration in a simulation model of cardiac arrest

Background

Reducing pauses during cardiopulmonary resuscitation (CPR) compressions result in better outcomes in cardiac arrest. Artefact filtering technology (AFT) gives rescuers the opportunity to visualize the underlying electrocardiogram (ECG) rhythm during chest compressions, and reduces the pauses that occur before and after delivering a shock. We conducted a simulation study to measure the reduction of peri-shock pause and impact on chest compression fraction (CCF) through AFT.

Methods

In a simulator setting, participants were given a standardized cardiac arrest scenario and were randomly assigned to perform CPR/defibrillation using the protocol from one of three experimental arms: 1) Standard of Care (pauses for rhythm analysis and shock delivery); 2) AFT (no pauses for rhythm analysis, but a pause for defibrillation); or 3) AFT with hands-on defibrillation (no pauses for rhythm analysis or defibrillation). The primary outcomes were CCF and peri-shock pause duration, with secondary outcomes of pre- and post-shock pause duration.

Results

AFT with hands-on defibrillation was found to have the highest CCF (86.4%), as compared to AFT alone (83.8%, p<0.001), and both groups significantly improved CCF in comparison with the Standard of Care (76.7%, p<0.001). AFT with hands-on defibrillation was associated with a reduced peri-shock pause (2.6 seconds) as compared to AFT alone (5.3 seconds, p<0.001), and the Standard of Care (7.4 seconds, p<0.001).

Conclusions

In this cardiac arrest model, AFT results in a greater CCF by reducing peri-shock pause duration. There is also a small but detectable improvement in CCF with the addition of hands-on defibrillation.

The impact of prehospital resuscitation research on in-hospital care

Since their introduction over 40 years ago, paramedics have been trained to deliver select advanced life support interventions in the community with the goal of reducing morbidity and mortality from cardiovascular disease and trauma. The ensuing decades witnessed a great deal of interest in paramedic care, with an exponential growth in prehospital resuscitation research. As part of the CJEM series on emergency medical services (EMS), we review recent prehospital research in out-of-hospital cardiac arrest and discuss how, in a novel departure from the origins of EMS, prehospital research is beginning to influence in-hospital care. We discuss emerging areas of study related to cardiopulmonary resuscitation (CPR) quality, therapeutic hypothermia, termination of resuscitation, and the use of end-tidal carbon dioxide measurement, as well as the subtle ripple effects that prehospital research is having on the broader understanding of the management of these critically ill patients.

A reliable method for rhythm analysis during cardiopulmonary resuscitation

Interruptions in cardiopulmonary resuscitation (CPR) compromise defibrillation success. However, CPR must be interrupted to analyze the rhythm because although current methods for rhythm analysis during CPR have high sensitivity for shockable rhythms, the specificity for nonshockable rhythms is still too low. This paper introduces a new approach to rhythm analysis during CPR that combines two strategies: a state-of-the-art CPR artifact suppression filter and a shock advice algorithm (SAA) designed to optimally classify the filtered signal. Emphasis is on designing an algorithm with high specificity. The SAA includes a detector for low electrical activity rhythms to increase the specificity, and a shock/no-shock decision algorithm based on a support vector machine classifier using slope and frequency features. For this study, 1185 shockable and 6482 nonshockable 9-s segments corrupted by CPR artifacts were obtained from 247 patients suffering out-of-hospital cardiac arrest. The segments were split into a training and a test set. For the test set, the sensitivity and specificity for rhythm analysis during CPR were 91.0% and 96.6%, respectively. This new approach shows an important increase in specificity without compromising the sensitivity when compared to previous studies.

Rhythm analysis during cardiopulmonary resuscitation: past, present, and future

Survival from out-of-hospital cardiac arrest depends largely on two factors: early cardiopulmonary resuscitation (CPR) and early defibrillation. CPR must be interrupted for a reliable automated rhythm analysis because chest compressions induce artifacts in the ECG. Unfortunately, interrupting CPR adversely affects survival. In the last twenty years, research has been focused on designing methods for analysis of ECG during chest compressions. Most approaches are based either on adaptive filters to remove the CPR artifact or on robust algorithms which directly diagnose the corrupted ECG. In general, all the methods report low specificity values when tested on short ECG segments, but how to evaluate the real impact on CPR delivery of continuous rhythm analysis during CPR is still unknown. Recently, researchers have proposed a new methodology to measure this impact. Moreover, new strategies for fast rhythm analysis during ventilation pauses or high-specificity algorithms have been reported. Our objective is to present a thorough review of the field as the starting point for these late developments and to underline the open questions and future lines of research to be explored in the following years.