As an important medical electronic equipment for the cardioversion of malignant arrhythmia such as ventricular fibrillation and ventricular tachycardia, cardiac external defibrillators have been widely used in the clinics. However, the resuscitation success rate for these patients is still unsatisfied. In this paper, the recent advances of cardiac external defibrillation technologies is reviewed. The potential mechanism of defibrillation, the development of novel defibrillation waveform, the factors that may affect defibrillation outcome, the interaction between defibrillation waveform and ventricular fibrillation waveform, and the individualized patient-specific external defibrillation protocol are analyzed and summarized. We hope that this review can provide helpful reference for the optimization of external defibrillator design and the individualization of clinical application.
Elderly patients account for 80% of cardiac arrest patients. The incidence of poor neurological prognosis after return of spontaneous circulation of these patients is as high as 90%, much higher than that of young. This is related to the fact that the mechanism of hippocampal brain tissue injury after ischemia-reperfusion in elderly cardiac arrest patients is aggravated. Therefore, this study reviews the possible mechanisms of poor neurological prognosis after return of spontaneous circulation in elderly cardiac arrest animals, and the results indicate that the decrease of hippocampal perfusion and the number of neurons after resuscitation are the main causes of the increased hippocampal injury, among which oxidative stress, mitochondrial dysfunction and protein homeostasis disorder are the important factors of cell death. This review hopes to provide new ideas for the treatment of elderly patients with cardiac arrest and the improvement of neurological function prognosis through the comparative analysis of elderly and young animals.
ObjectiveTo systematically review the early clinical prediction value of machine learning (ML) for cardiac arrest (CA).MethodsPubMed, EMbase, WanFang Data and CNKI databases were electronically searched to retrieve all ML studies on predicting CA from January 2015 to February 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. The value of each model was evaluated based on the area under receiver operating characteristic curve (AUC) and accuracy.ResultsA total of 38 studies were included. In terms of data sources, 13 studies were based on public database, and other studies retrospectively collected clinical data, in which 21 directly predicted CA, 3 predicted CA-related arrhythmias, and 9 predicted sudden cardiac death. A total of 51 models had been adopted, among which the most popular ML methods included artificial neural network (n=11), followed by random forest (n=9) and support vector machine (n=5). The most frequently used input feature was electrocardiogram parameters (n=20), followed by age (n=12) and heart rate variability (n=10). Six studies compared the ML models with other traditional statistical models and the results showed that the AUC value of ML was generally higher than that in traditional statistical models.ConclusionsThe available evidence suggests that ML can accurately predict the occurrence of CA, and the performance is significantly superior to traditional statistical model in certain cases.
Since the outbreak of the coronavirus disease 2019, the incidence and mortality of cardiac arrest have increased significantly worldwide, and the management of cardiac arrest is facing new challenges. The European Resuscitation Council issued the 2021 European Resuscitation Council Guidelines in March 2021 to update the important parts of cardiopulmonary resuscitation and added recommendations for the management of cardiopulmonary resuscitation during the coronavirus disease 2019 epidemic. This article will compare the difference between this guideline and the 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care and integrate some key points, review literature and then summarize the latest research progress in cardiopulmonary resuscitation since the outbreak of the coronavirus disease 2019 epidemic. The content mainly involves cardiopulmonary resuscitation during the coronavirus disease 2019 epidemic, early prevention, early recognition, application of new technologies, airway management, extracorporeal cardiopulmonary resuscitation and post-resuscitation treatment.
Currently, cardiac arrest has become a major public health problem, which has a high incidence rate and a high mortality rate in humans. With the continuous advancement of cardiopulmonary resuscitation techniques, the overall prognosis of cardiac arrest victims is gradually improved. However, cardiac arrest events under special circumstances are still serious threats to human health. This article reviews the progress of epidemiology, pathogenesis, treatment characteristics, and key points of cardiopulmonary resuscitation in those special cardiac arrest events associated with trauma, poisoning, drowning and pregnancy.
In recent years, target temperature management (TTM) has been increasingly applied to cardiac arrest patients, and programs and strategies for TTM are in a constant state of update and refinement. This paper analyzes and proposes relevant strategies from the concept of TTM, its clinical application status for cardiac arrest patients in domestic and international medical institutions, its deficiencies in the clinical practice, and factors affecting the development of TTM, with a view to providing a realistic basis for the development of high-quality TTM in medical institutions.
American Heart Association issued American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care in October 2020. A sixth link, recovery, has been added to both the adult out-of-hospital cardiac arrest chain and in-hospital cardiac arrest chain in this version of the guidelines to emphasize the importance of recovery and survivorship for resuscitation outcomes. Analogous chains of survival have also been developed for adult out-of-hospital cardiac arrest and in-hospital cardiac arrest. The major new and updated recommendations involve the early initiation of cardiopulmonary resuscitation by lay rescuers, early administration of epinephrine, real-time audiovisual feedback, physiologic monitoring of cardiopulmonary resuscitation quality, double sequential defibrillation not supported, intravenous access preferred over intraosseous, post-cardiac arrest care and neuroprognostication, care and support during recovery, debriefings for rescuers, and cardiac arrest in pregnancy. This present review aims to interpret these updates by reviewing the literature and comparing the recommendations in these guidelines with previous ones.
Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.
Although the survival rate reported in each center is different, according to the present studies, compared to conventional cardiopulmonary resuscitation (CCPR), extracorporeal cardiopulmonary resuscitation (ECPR) can improve the survival rate of cardiac arrest patient, no matter out-of-hospital or in-hospital. The obvious advantage of ECPR is that it can reduce the nervous system complications in the cardiac arrest patients and improve survival rate to hospital discharge. However, ECPR is expensive and without the uniformed indications for implantation. The prognosis for patients with ECPR support is also variant due to the different etiology. If we want to achieve better result, the ECPR technology itself needs to be further improved.
Extracorporeal cardiopulmonary resuscitation (ECPR) is a salvage therapy for patients suffering cardiac arrest refractory to conventional resuscitation, and provides circulatory support in patients who fail to achieve a sustained return of spontaneous circulation. ECPR serves as a bridge therapy that maintains organ perfusion whilst the underlying etiology of the cardiac arrest is determined and treated. Increasing recognition of the survival benefit associated with ECPR has led to increased use of ECPR during the past decade. Commonly used indications for ECPR are: age<70 years, initial rhythm of ventricular fibrillation or ventricular tachycardia, witnessed arrest, bystander cardiopulmonary resuscitation within 5 min, failure to achieve sustained return of spontaneous circulation within 15 min of beginning cardiopulmonary resuscitation. This review provides an overview of ECPR utilization, recent outcomes, risk factors, and complications of ECPR. Identifying ECPR indications, rapid deployment of extracorporeal life support equipment, and high-quality ECPR management strategies are of paramount importance to improve survival.