ObjectiveTo compare the therapeutic effect of dual-chamber pacing (DDD) and ventricular single-chamber pacing (VVI) on arrhythmia via systematic evaluation.
MethodsWith the method of Cochrane system evaluation, we searched Medline, Embase, CNKI, PubMed and Wanfang database (the searching time was up to June 30, 2016) for randomized controlled trials comparing DDD with VVI treatingcardiac arrhythmias. Meta analysis was performed using RevMan5.3 software.
ResultsWe collected 12 randomized controlled trials of DDD and VVI pacing treating cardiac arrhythmia including 1 704 patients, but the quality of the studies were not good. The results of Meta analysis showed that:compared with VVI pacing mode, DDD pacing mode reduced the risk of atrial fibrillation[RR=0.36, 95%CI (0.22, 0.59), P < 0.000 1]; besides, it reduced the left atrial diameter[SMD=-0.43, 95%CI (-0.68, -0.17), P=0.001], the left ventricular end diastolic dimension[SMD=-0.33, 95%CI (-0.61, -0.05), P=0.02] and increased the left ventricular ejection fraction[SMD=1.03, 95%CI (0.49, 1.57), P=0.000 2].
ConclusionsComparing DDD with VVI on the treatment of cardiac arrhythmia in patients with cardiac arrhythmia, DDD pacing can reduce the incidence of atrial fibrillation and thrombosis, enhance heart function and improve blood supply. But because of the low quality of the included studies, the curative effect cannot be confirmed, and more randomized controlled trials with high quality needs to be carried out in the future.
Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F1-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
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.
Sepsis-associated organ dysfunction arises from uncontrolled inflammation and immune dysregulation, causing microcirculatory impairment and multi-organ failure. Stellate ganglion block (SGB) may confer organ protection by regulating the sympathetic nervous system and hypothalamic-pituitary-adrenal axis to suppress excessive inflammation and oxidative stress. Available evidence, mainly from experimental and small clinical studies, suggests potential benefits of SGB in sepsis-induced acute lung injury, ventricular arrhythmias, and limb ischemia, which require confirmation in multicenter randomized controlled trials. This review outlines the mechanisms and clinical advances of SGB in sepsis-related organ dysfunction, providing a theoretical basis for its application in critical care.
Existing arrhythmia classification methods usually use manual selection of electrocardiogram (ECG) signal features, so that the feature selection is subjective, and the feature extraction is complex, leaving the classification accuracy usually affected. Based on this situation, a new method of arrhythmia automatic classification based on discriminative deep belief networks (DDBNs) is proposed. The morphological features of heart beat signals are automatically extracted from the constructed generative restricted Boltzmann machine (GRBM), then the discriminative restricted Boltzmann machine (DRBM) with feature learning and classification ability is introduced, and arrhythmia classification is performed according to the extracted morphological features and RR interval features. In order to further improve the classification performance of DDBNs, DDBNs are converted to deep neural network (DNN) using the Softmax regression layer for supervised classification in this paper, and the network is fine-tuned by backpropagation. Finally, the Massachusetts Institute of Technology and Beth Israel Hospital Arrhythmia Database (MIT-BIH AR) is used for experimental verification. For training sets and test sets with consistent data sources, the overall classification accuracy of the method is up to 99.84% ± 0.04%. For training sets and test sets with inconsistent data sources, a small number of training sets are extended by the active learning (AL) method, and the overall classification accuracy of the method is up to 99.31% ± 0.23%. The experimental results show the effectiveness of the method in arrhythmia automatic feature extraction and classification. It provides a new solution for the automatic extraction of ECG signal features and classification for deep learning.
ObjectiveTo compare the effectiveness of different β-blockers for preventing postoperative atrial fibrillation (POAF) after cardiac surgery. Methods Databases of PubMed, Science Direct, Web of Science, The Cochrane Library, SinoMed, CKNI, VIP, WanFang were searched by the computer from inception to April 31, 2022 to collect randomized controlled studies on the comparison of the effectiveness of different β-blockers for preventing POAF. Two investigators independently screened the literature and extracted information. The quality of the literature was evaluated using Cochrane bias risk tool, and RevMan 5.3 and STATA 17.0 were used for meta-analysis. Results A total of 17 randomized controlled studies with 3 290 patients were included. Direct meta-analysis showed that landiolol and metoprolol were more effective than placebo (P≤0.001), and carvedilol was more effective than metoprolol in preventing the development of POAF (P<0.001). Network meta-analysis showed that landiolol, carvedilol and metoprolol were more effective than placebo in preventing the incidence of POAF (P<0.05). Landiolol and carvedilol were more effective than metoprolol, and carvedilol were more effective than nebivolol (P<0.05). The surface under the cumulative ranking curve from high to low were carvedilol, landilol, propranolol, atenolol, metoprolol, and nebivolol. Conclusion Carvedilol and landilol have different degrees of improvement in the occurrence of POAF, and carvedilol has the best preventive effect. More studies are required to verify the strength of evidence due to the limited sample size.
ObjectivesTo systematically review the efficacy of traditional Chinese medicine for arrhythmia caused by anthracycline drugs.MethodsPubMed, EMbase, The Cochrane Library, CBM, CNKI, WanFang Data databases were electronically searched to collect randomized controlled trials (RCTs) on the efficacy of traditional Chinese medicine for arrhythmia caused by anthracycline drugs from inception to October 2017. Two reviewers independently screened literature, extracted data and evaluated risk of bias of included studies. Meta-analysis was then performed by Revman 5.3 software.ResultsA total of 4 RCTs involving 312 patients were included. The results of meta-analysis showed that: the incidence of tachycardia in the Wenxin granule treatment group was lower than that in the control group (RR=0.35, 95%CI 0.18 to 0.67, P=0.002). Baoxinkang was more effective than antioxidant western medicine in protecting myocardial SOD activity (RR=2.25, 95%CI 1.74 to 2.76, P<0.000 01). But there was no significant difference between two groups on the incidence of atrial premature beats (RR=0.40, 95%CI 0.15 to 1.08,P=0.07), premature ventricular contractions (RR=0.56, 95%CI 0.23 to 1.34, P=0.19) and atrial fibrillation (RR=0.41, 95%CI 0.11 to 1.53, P=0.18). In addition, there was no significant difference between Wenxin granules and amiodarone in treating arrhythmia induced by anthracycline. The addition of Wenxin granules on the basis of anthracycline antitumor chemotherapy regimens was not effective in delaying disease progression compared with anthracycline alone. Wenxin granules could not change the SOD level of breast cancer patients with cardiotoxicity caused by anthracycline chemotherapy, compared with chemotherapy and basic treatment.ConclusionsThe current evidence shows that Wenxin granules can prevent and reduce anthracycline-induced tachycardia, but its efficacy in improving the overall efficiency, preventing and reducing atrial premature beats, premature ventricular contractions, atrial fibrillation, and SOD levels are unclear. Baoxinkang can protect myocardial SOD activity. Due to limited quality and quantity of the included studies, more high quality studies are required to verify above conclusions.
Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart disease. It is characterized by an interventricular communication with an overriding aorta, subpulmonary obstruction, and consequent right ventricular hypertrophy. The potential for late complications is an important concern for growing number of survivors after surgical repair, although long-term survival rates are excellent. Progressive pulmonary valve regurgitation leading to right heart failure and arrhythmias are common late complications and major reasons of mortality. In this review, we focus on research progress of pathogenesis and treatment of late complications after TOF repair, and the importance of long-term follow-up is emphasized.
Electrocardiogram (ECG) signal is an important basis for the diagnosis of arrhythmia and myocardial infarction. In order to further improve the classification effect of arrhythmia and myocardial infarction, an ECG classification algorithm based on Convolutional vision Transformer (CvT) and multimodal image fusion was proposed. Through Gramian summation angular field (GASF), Gramian difference angular field (GADF) and recurrence plot (RP), the one-dimensional ECG signal was converted into three different modes of two-dimensional images, and fused into a multimodal fusion image containing more features. The CvT-13 model could take into account local and global information when processing the fused image, thus effectively improving the classification performance. On the MIT-BIH arrhythmia dataset and the PTB myocardial infarction dataset, the algorithm achieved a combined accuracy of 99.9% for the classification of five arrhythmias and 99.8% for the classification of myocardial infarction. The experiments show that the high-precision computer-assisted intelligent classification method is superior and can effectively improve the diagnostic efficiency of arrhythmia as well as myocardial infarction and other cardiac diseases.
