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.
American Heart Association (AHA) updated the advanced cardiovascular life support use of antiarrhythmic drugs during and immediately after cardiac arrest in the AHA guidelines for cardiopulmonary resuscitation and emergency cardiovascular care in November 2018. Based on the latest progress of relative evidence-based clinical evidence and 2015 AHA guidelines for cardiopulmonary resuscitation and cardiovascular emergency cardiovascular care. This update gave recommends on the use of antiarrhythmic drugs during resuscitation from adult shock-refractory ventricular fibrillation (VF) and pulseless ventricular tachycardia (pVT) cardiac arrest and immediately after restoration of spontaneous circulation following shock-refractory VF/pVT cardiac arrest, respectively. This review aims to interpret this update by reviewing the literature and comparing the recommends in this update with other guidelines.
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.
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.
Objective To investigate the relationship between thrombocytopenia after the restoration of spontaneous circulation and short-term prognosis of patients with in-hospital cardiac arrest. Methods The demographic data, post-resuscitation vital signs, post-resuscitation laboratory tests, and the 28-day mortality rate of patients who experienced in-hospital cardiac arrest at the Emergency Department of West China Hospital, Sichuan University between January 1st, 2016 and December 31st, 2016 were retrospectively analyzed. Logistic regression was used to analyze the correlation between thrombocytopenia after the return of spontaneous circulation and the 28-day mortality rate in these cardiac arrest patients. Results Among the 285 patients included, compared with the normal platelet group (n=130), the thrombocytopenia group (n=155) showed statistically significant differences in red blood cell count, hematocrit, white blood cell count, prothrombin time, activated partial thromboplastin time, and international normalized ratio (P<0.05). The 28-day mortality rate was higher in the thrombocytopenia group than that in the normal platelet group (84.5% vs. 71.5%, P=0.008). Multiple logistic regression analysis indicated that thrombocytopenia [odds ratio =2.260, 95% confidence interval (1.153, 4.429), P=0.018] and cardiopulmonary resuscitation duration [odds ratio=1.117, 95% confidence interval (1.060, 1.177), P<0.001] were independent risk factors for 28-day mortality in patients with in-hospital cardiac arrest. Conclusion Thrombocytopenia after restoration of spontaneous circulation is associated with poor short-term prognosis in patients with in-hospital cardiac arrest.
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.
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.
ObjectiveTo investigate the effects of esophageal cooling (EC) on lung injury and systemic inflammatory response after cardiopulmonary resuscitation in swine.MethodsThirty-two domestic male white pigs were randomly divided into sham group (S group, n=5), normothermia group (NT group, n=9), surface cooling group (SC group, n=9), and EC group (n=9). The animals in the S group only experienced the animal preparation. The animal model was established by 8 min of ventricular fibrillation and then 5 min of cardiopulmonary resuscitation in the other three groups. A normal temperature of (38.0±0.5)℃ was maintained by surface blanket throughout the experiment in the S and NT groups. At 5 min after resuscitation, therapeutic hypothermia was implemented via surface blanket or EC catheter to reach a target temperature of 33℃, and then maintained until 24 h post resuscitation, and followed by a rewarming rate of 1℃/h for 5 h in the SC and EC groups. At 1, 6, 12, 24 and 30 h after resuscitation, the values of extra-vascular lung water index (ELWI) and pulmonary vascular permeability index (PVPI) were measured, and meanwhile arterial blood samples were collected to measure the values of oxygenation index (OI) and venous blood samples were collected to measure the serum levels of tumor necrosis factor-α (TNF-α) and inerleukin-6 (IL-6). At 30 h after resuscitation, the animals were euthanized, and then the lung tissue contents of TNF-α, IL-6 and malondialdehyde, and the activities of superoxide dismutase (SOD) were detected.ResultsAfter resuscitation, the induction of hypothermia was significantly faster in the EC group than that in the SC group (2.8 vs. 1.5℃/h, P<0.05), and then its maintenance and rewarming were equally achieved in the two groups. The values of ELWI and PVPI significantly decreased and the values of OI significantly increased from 6 h after resuscitation in the EC group and from 12 h after resuscitation in the SC group compared with the NT group (all P<0.05). Additionally, the values of ELWI and PVPI were significantly lower and the values of OI were significantly higher from 12 h after resuscitation in the EC group than those in the SC group [ELWI: (13.4±3.1) vs. (16.8±2.7) mL/kg at 12 h, (12.4±3.0) vs. (16.0±3.6) mL/kg at 24 h, (11.1±2.4) vs. (13.9±1.9) mL/kg at 30 h; PVPI: 3.7±0.9 vs. 5.0±1.1 at 12 h, 3.4±0.8 vs. 4.6±1.0 at 24 h, 3.1±0.7 vs. 4.2±0.7 at 30 h; OI: (470±41) vs. (417±42) mm Hg (1 mm Hg=0.133 kPa) at 12 h, (462±39) vs. (407±36) mm Hg at 24 h, (438±60) vs. (380±33) mm Hg at 30 h; all P<0.05]. The serum levels of TNF-α and IL-6 significantly decreased from 6 h after resuscitation in the SC and EC groups compared with the NT group (all P<0.05). Additionally, the serum levels of IL-6 from 6 h after resuscitation and the serum levels of TNF-α from 12 h after resuscitation were significantly lower in the EC group than those in the SC group [IL-6: (299±23) vs. (329±30) pg/mL at 6 h, (336±35) vs. (375±30) pg/mL at 12 h, (297±29) vs. (339±36) pg/mL at 24 h, (255±20) vs. (297±33) pg/mL at 30 h; TNF-α: (519±46) vs. (572±49) pg/mL at 12 h, (477±77) vs. (570±64) pg/mL at 24 h, (436±49) vs. (509±51) pg/mL at 30 h; all P<0.05]. The contents of TNF-α, IL-6, and malondialdehyde significantly decreased and the activities of SOD significantly increased in the SC and EC groups compared with the NT group (all P<0.05). Additionally, lung inflammation and oxidative stress were further significantly alleviated in the EC group compared with the SC group [TNF-α: (557±155) vs. (782±154) pg/mg prot; IL-6: (616±134) vs. (868±143) pg/mg prot; malondialdehyde: (4.95±1.53) vs. (7.53±1.77) nmol/mg prot; SOD: (3.18±0.74) vs. (2.14±1.00) U/mg prot; all P<0.05].ConclusionTherapeutic hypothermia could be rapidly induced by EC after resuscitation, and further significantly alleviated post-resuscitation lung injury and systemic inflammatory response compared with conventional surface cooling.
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.