Acute poisoning is characterized by a sudden and rapid onset, most poisons lack specific antidotes. Even with the full use of blood purification, mechanical ventilation, and various drugs, it is often difficult to change the fatal outcome of critically ill patients. In recent years, extracorporeal membrane oxygenation (ECMO) has gradually gained attention and exploratory application in the treatment of acute poisoning due to its significant cardiopulmonary function support, veno-venous ECMO is used for severe lung injury after poisoning, acute respiratory distress syndrome and respiratory failure due to ineffective mechanical ventilation, and it can also be used to assist the removal of residual poisons in the lungs. Veno-arterial ECMO is commonly employed in patients with circulatory failure following poisoning, fatal cardiac arrhythmias, and arrest of cardiac and respiratory. The application of veno-arterio-venous ECMO has also been reported. The mode of ECMO necessitates timely adjustments according to the evolving illness, while ongoing exploration of additional clinical indications is underway. This review analyzes and evaluates the application scope and effectiveness of ECMO in acute poisoning in recent years, with a view to better exploring and rationalizing the use of this technology.
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.
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.
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.
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.
The treatment of organ function damage secondary to return of spontaneous circulation in patients with cardiac arrest is an important part of advanced life support. The incidence of lung injury secondary to return of spontaneous circulation in patients with cardiac arrest is as high as 79%. Understanding the characteristics and related mechanisms of lung injury secondary to return of spontaneous circulation in patients with cardiac arrest, and early identification and treatment of lung injury secondary to return of spontaneous circulation are crucial to the clinical treatment of patients with cardiac arrest. Therefore, this article reviews the research progress on the characteristics, risk factors, mechanisms and treatment of lung injury secondary to return of spontaneous circulation in patients with cardiac arrest, in order to provide a reference for the research and clinical diagnosis and treatment of lung injury secondary to return of spontaneous circulation in patients with cardiac arrest.
ObjectiveTo systematically evaluate the clinical value of machine learning (ML) for predicting the neurological outcome of out-of-hospital cardiac arrest (OHCA), and to develop a prediction model. MethodsWe searched the PubMed, Web of Science, EMbase, CNKI, Wanfang database from January 1, 2011 to November 24, 2021. Studies on ML for predicting neurological outcomes in OHCA pateints were collected. Two researchers independently screened the literature, extracted the data and evaluated the bias of the included literature, evaluated the accuracy of different models and compared the area under the receiver operating characteristic curve (AUC). ResultsA total of 20 studies were included. Eleven of the studies were from open source databases and nine were from retrospective studies. Sixteen studies directly predicted OHCA neurological outcomes, and four predicted OHCA neurological outcomes after target temperature management. A total of seven ML algorithms were used, among which neural network was the ML algorithm with the highest frequency (n=5), followed by support vector machine and random forest (n=4). Three papers used multiple algorithms. The most frequently used input characteristic was age (n=19), followed by heart rate (n=17) and gender (n=13). A total of 4 studies compared the predictive value of ML with other classical statistical models, and the AUC value of ML model was higher than that of classical statistical models. ConclusionExisting evidence suggests that ML can more accurately predict OHCA nervous system outcomes, and the predictive performance of ML is superior to traditional statistical models in certain situations.
ObjectiveTo explore the value of platelet-lymphocyte ratio (PLR) after return of spontaneous circulation (ROSC) combined with Sequential Organ Failure Assessment (SOFA) for estimating the short-term prognosis of ROSC patients suffered from in-hospital cardiac arrest (IHCA).MethodsROSC adult patients who suffered from IHCA during treatment in the Emergency Department of West China Hospital of Sichuan University between 00:00, August 1st, 2010 and 23:59, July 31st, 2018 were included retrospectively. The basic and clinical data of patients were collected. Patients were divided into survival group and death group according to the 28-day prognosis. Through logistic regression and receiver operating characteristic (ROC) curve analysis, the efficacy of PLR after ROSC combined with SOFA score in predicting the 28-day prognosis of IHCA patients was explored.ResultsA total of 199 patients were included, including 135 males and 64 females, with a mean age of (60.45±17.52) years old. There were 154 deaths and 45 survivors within 28 days. There were statistically significant differences between the survival group and the death group in terms of epinephrine dosage, SOFA score, proportion of patients complicated with respiratory diseases, and post-ROSC laboratory indexes including PLR, hemoglobin, red blood cell count, lymphocyte count, indirect bilirubin, serum albumin, cholesterol, and activated partial thrombin time (P<0.05). The result of multivariate logistic regression analysis showed that epinephrine dosage [odds ratio (OR)=1.177, 95% confidence interval (CI) (1.024, 1.352), P=0.022], SOFA score [OR=1.536, 95%CI (1.173, 2.010), P=0.002], PLR after ROSC [OR=1.011, 95%CI (1.004, 1.018), P=0.002] were independent risk factors for ROSC patients’ death on day 28. The areas under the ROC curve of epinephrine dosage, SOFA score and PLR after ROSC were 0.702, 0.703 and 0.737, respectively, to predict the patients’ 28-day outcome. Combining the epinephrine dosage and PLR after ROSC with SOFA score respectively to predict the 28-day outcome of patients, the areas under the ROC curve were 0.768 and 0.813, respectively.ConclusionsThe significant increase of PLR after ROSC is an independent risk factor for death within 28 days after ROSC. The combined application of PLR after ROSC and SOFA score in the 28-day outcome prediction of patients has better predictive efficacy.
Objective To systematically review risk prediction models of in-hospital cardiac arrest in patients with cardiovascular disease, and to provide references for related clinical practice and scientific research for medical professionals in China. Methods Databases including CBM, CNKI, WanFang Data, PubMed, ScienceDirect, Web of Science, The Cochrane Library, Wiley Online Journals and Scopus were searched to collect studies on risk prediction models for in-hospital cardiac arrest in patients with cardiovascular disease from January 2010 to July 2022. Two researchers independently screened the literature, extracted data, and evaluated the risk of bias of the included studies. Results A total of 5 studies (4 of which were retrospective studies) were included. Study populations encompassed mainly patients with acute coronary syndrome. Two models were modeled using decision trees. The area under the receiver operating characteristic curve or C statistic of the five models ranged from 0.720 to 0.896, and only one model was verified externally and for time. The most common risk factors and immediate onset factors of in-hospital cardiac arrest in patients with cardiovascular disease included in the prediction model were age, diabetes, Killip class, and cardiac troponin. There were many problems in analysis fields, such as insufficient sample size (n=4), improper handling of variables (n=4), no methodology for dealing with missing data (n=3), and incomplete evaluation of model performance (n=5). Conclusion The prediction efficiency of risk prediction models for in-hospital cardiac arrest in patients with cardiovascular disease was good; however, the model quality could be improved. Additionally, the methodology needs to be improved in terms of data sources, selection and measurement of predictors, handling of missing data, and model evaluations. External validation of existing models is required to better guide clinical practice.
Detection and classification of malignant arrhythmia are key tasks of automated external defibrillators. In this paper, 21 metrics extracted from existing algorithms were studied by retrospective analysis. Based on these metrics, a back propagation neural network optimized by genetic algorithm was constructed. A total of 1,343 electrocardiogram samples were included in the analysis. The results of the experiments indicated that this network had a good performance in classification of sinus rhythm, ventricular fibrillation, ventricular tachycardia and asystole. The balanced accuracy on test dataset reached up to 99.06%. It illustrates that our proposed detection algorithm is obviously superior to existing algorithms. The application of the algorithm in the automated external defibrillators will further improve the reliability of rhythm analysis before defibrillation and ultimately improve the survival rate of cardiac arrest.