Objective To investigate the risk factors for arrhythmia after robotic cardiac surgery. Methods The data of the patients who underwent robotic cardiac surgery under cardiopulmonary bypass (CPB) from July 2016 to June 2022 in Daping Hospital of Army Medical University were retrospectively analyzed. According to whether arrhythmia occurred after operation, the patients were divided into an arrhythmia group and a non-arrhythmia group. Univariate analysis and multivariate logistic analysis were used to screen the risk factors for arrhythmia after robotic cardiac surgery. ResultsA total of 146 patients were enrolled, including 55 males and 91 females, with an average age of 43.03±13.11 years. There were 23 patients in the arrhythmia group and 123 patients in the non-arrhythmia group. One (0.49%) patient died in the hospital. Univariate analysis suggested that age, body weight, body mass index (BMI), diabetes, New York Heart Association (NYHA) classification, left atrial anteroposterior diameter, left ventricular anteroposterior diameter, right ventricular anteroposterior diameter, total bilirubin, direct bilirubin, uric acid, red blood cell width, operation time, CPB time, aortic cross-clamping time, and operation type were associated with postoperative arrhythmia (P<0.05). Multivariate binary logistic regression analysis suggested that direct bilirubin (OR=1.334, 95%CI 1.003-1.774, P=0.048) and aortic cross-clamping time (OR=1.018, 95%CI 1.005-1.031, P=0.008) were independent risk factors for arrhythmia after robotic cardiac surgery. In the arrhythmia group, postoperative tracheal intubation time (P<0.001), intensive care unit stay (P<0.001) and postoperative hospital stay (P<0.001) were significantly prolonged, and postoperative high-dose blood transfusion events were significantly increased (P=0.002). Conclusion Preoperative direct bilirubin level and aortic cross-clamping time are independent risk factors for arrhythmia after robotic cardiac surgery. Postoperative tracheal intubation time, intensive care unit stay, and postoperative hospital stay are significantly prolonged in patients with postoperative arrhythmia, and postoperative high-dose blood transfusion events are significantly increased.
Objective To analyze the risk factors affecting the occurrence of arrhythmia after esophageal cancer surgery, construct a risk prediction model, and explore its clinical value. Methods A retrospective analysis was conducted on the clinical data of patients who underwent radical esophagectomy for esophageal cancer in the Department of Thoracic Surgery at Anhui Provincial Hospital from 2020 to 2023. Univariate and multivariate analyses were used to screen potential factors influencing postoperative arrhythmia. A risk prediction model for postoperative arrhythmia was constructed, and a nomogram was drawn. The predictive performance of the model was then validated. Results A total of 601 esophageal cancer patients were randomly divided into a modeling group (421 patients) and a validation group (180 patients) at a 7 : 3 ratio. In the modeling group, patients were further categorized into an arrhythmia group (188 patients, 44.7%) and a non-arrhythmia group (233 patients, 55.3%) based on whether they developed postoperative arrhythmia. Among those with postoperative arrhythmia, 43 (10.2%) patients had atrial fibrillation (AF), 12 (2.9%) patients had atrial premature beats, 15 (3.6%) patients had sinus bradycardia, and 143 (34%) patients had sinus tachycardia. Some patients exhibited multiple arrhythmias, including 14 patients with AF combined with sinus tachycardia, 7 patients with AF combined with atrial premature beats, and 3 patients with AF combined with sinus bradycardia. Univariate analysis revealed that a history of hypertension, heart disease, pulmonary infection, acute respiratory distress syndrome, postoperative hypoxia, anastomotic leakage, and delirium were risk factors for postoperative arrhythmia in esophageal cancer patients (P<0.05). Multivariate logistic regression analysis showed that a history of heart disease, pulmonary infection, and postoperative hypoxia were independent risk factors for postoperative arrhythmia after esophageal cancer surgery (P<0.05). The area under the receiver operating characteristic curve (AUC) of the constructed risk prediction model for postoperative arrhythmia was 0.710 [95% CI (0.659, 0.760)], with a sensitivity of 0.617 and a specificity of 0.768. Conclusion A history of heart disease, pulmonary infection, and postoperative hypoxia are independent risk factors for postoperative arrhythmia after esophageal cancer surgery. The risk prediction model constructed in this study can effectively identify high-risk patients for postoperative arrhythmia, providing a basis for personalized interventions.
The automatic detection of arrhythmia is of great significance for the early prevention and diagnosis of cardiovascular diseases. Traditional arrhythmia diagnosis is limited by expert knowledge and complex algorithms, and lacks multi-dimensional feature representation capabilities, which is not suitable for wearable electrocardiogram (ECG) monitoring equipment. This study proposed a feature extraction method based on autoregressive moving average (ARMA) model fitting. Different types of heartbeats were used as model inputs, and the characteristic of fast and smooth signal was used to select the appropriate order for the arrhythmia signal to perform coefficient fitting, and complete the ECG feature extraction. The feature vectors were input to the support vector machine (SVM) classifier and K-nearest neighbor classifier (KNN) for automatic ECG classification. MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database were used to verify in the experiment. The experimental results showed that the feature engineering composed of the fitting coefficients of the ARMA model combined with the SVM classifier obtained a recall rate of 98.2% and a precision rate of 98.4%, and the F1 index was 98.3%. The algorithm has high performance, meets the needs of clinical diagnosis, and has low algorithm complexity. It can use low-power embedded processors for real-time calculations, and it’s suitable for real-time warning of wearable ECG monitoring equipment.
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 optimize the therapy protocols of high dose prednisone combined with topiramate (TPM) in children with infantile spasms (IS). MethodsSixty cases were collected in our hospital from September 2012 to September 2013 and randomly divided into two groups(n=30) and followed-up for more than 6 months.The spasms were assesses by video-electroencephalogram (VEEG) monitoring including awake and asleep states before treatment, after two weeks of therapy and the end of the courses respectively.And the Gessel developmental quotient (DQ) scores were performed before treatment and after six months of therapy. ResultsFor the unresponders to high dose prednisone in one week of therapy, there were 46.67%and 60.00% in test group higher than 31.25% and 37.50% in control group respectively in 2 week and in the end of treatment.And the rate of complete resolution of hypsarrhythmia in the test group was 46.67% and 60.00% higher than 25.00% and 37.50% in control group respectively in 2 week and in the end of treatment.But there were no statistical significances between two groups(P >0.05).The incidence of side effects(83.33% vs. 80.00%) and the relapse rate(39.14% vs. 40.00%), were not statistically significant between two groups(P >0.05).The responsive rates for the cases with the lead time within 2 months higher than beyond 2 months in two groups respectively in 2 weeks and in the end of treatment. ConclusionsThe protocol of the test group was superior to that of the control group.The responsive rates of children within 2 months of lead time were higher than beyond 2 months, which indicates that early diagnosis and early treatment would improve efficacy and have an important influence on the prognosis of IS.
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 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.
ObjectiveTo investigate the efficacy of bipolar radiofrequency ablation for left ventricular aneurysm-related ventricular arrhythmia associated with mural thrombus. MethodsFifteen patients with left ventricular aneurysm-related frequent premature ventricular contractions associated with mural thrombus were enrolled in Beijing Anzhen Hospital between June 2013 and June 2015. There were 11 male and 4 female patients with their age of 63.5±4.8 years. All patients had a history of myocardial infarction, but no cerebral infarction. All patients received bipolar radiofrequency ablation combined with coronary artery bypass grafting, ventricular aneurysm plasty and thrombectomy. Holter monitoring and echocardiography were measured before discharge and 3 months following the operation. ResultsThere was no death during the operation. Cardiopulmonary bypass time was 92.7±38.3 min. The aortic clamping time was 52.4±17.8 min.The number of bypass grafts was 3.9±0.4. All the patients were discharged 7-10 days postoperatively. None of the patients had low cardiac output syndrome, malignant arrhythmias, perioperative myocardial infarction, or cerebral infarction in this study. Echocardiography conducted before discharge showed that left ventricular end diastolic diameter was decreased (54.87±5.21 cm vs. 60.73±6.24 cm, P=0.013). While there was no significant improvement in ejection fraction (45.20%±3.78% vs. 44.47%±6.12%, P=1.00) compared with those before the surgery. The number of premature ventricular contractions[4 021.00 (2 462.00, 5 496.00)beats vs. 11 097.00 (9 327.00, 13 478.00)beats, P < 0.001] and the percentage of premature ventricular contractions[2.94% (2.12%, 4.87%) vs. 8.11% (7.51%, 10.30%), P < 0.001] in 24 hours revealed by Holter monitoring were all significantly decreased than those before the surgery. At the end of 3-month follow-up, all the patients were angina and dizziness free. Echocardiography documented that there was no statistical difference in left ventricular end diastolic diameter (55.00±4.41 mm vs. 54.87±5.21 mm, P=1.00). But there were significant improvements in ejection fraction (49.93%±4.42% vs. 45.20%±3.78%, P=0.04) in contrast to those before discharge. Holter monitoring revealed that the frequency of premature ventricular contractions[2 043.00 (983.00, 3 297.00)beats vs. 4 021.00 (2 462.00, 5 496.00)beats, P=0.03] were further lessened than those before discharge, and the percentage of premature ventricular contractions[2.62% (1.44%, 3.49%)vs. 8.11% (7.51%, 10.30%), P < 0.001] was significantly decreased than those before the surgery, but no significant difference in contrast to those before discharge. ConclusionThe recoveries of cardiac function benefit from integrated improvements in myocardial ischemia, ventricular geometry, pump function, and myocardial electrophysiology. Bipolar radiofrequency ablation can correct the electrophysiological abnormality, significantly decrease the frequency of premature ventricular contractions, and further improve the heart function.
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.
Atrial tachyarrhythmias is a known complication after cardiac surgery and represents a major cause of morbidity, increased length of hospital stay, and economic costs. Atrial fibrillation is the most common heart rhythm disorder. And it is often associated with other atrial tachyarrhythmias, such as atrial flutter (AFlu), premature atrial complexes, and multifocal atrial tachycardia. Postoperative atrial fibrillation is often self-limiting, but it may require anticoagulation therapy and either a rate or rhythm control strategy. We provide a complete and updated review about mechanisms, risk factors, and treatment strategies for the main atrial tachyarrhythmias (atrial fibrillation).