Objective To improve the myocardial protection result, observe the effects of 11,12 epoxyeicosatrienoic acid (11,12 EET) on reperfusion arrhythmias in the isolated perfused immature rabbit hearts, which underwent long term preservation. Methods Sixteen isolated rabbit hearts were randomly assigned to two groups, 8 rabbits each group. Control group: treated with St.Thomas Ⅱ solution, experimental group: treated with St.Thomas Ⅱ solution plus 11,12 EET. By means of the Langendorff technique, these isolated rabbit hearts were arrested and stored for 16 hours with 4℃ hypothermia, and underwent 30 minutes of reperfusion(37℃). The mean times until the cessation of both electrical and mechanical activity were measured after infusion of cardioplegia. The heart rate (HR), coronary flow (CF), myocardial water content (MWC), value of creatine kinase (CK) and lactic dehydrogenase (LDH), myocardial calcium content and the arrhythmias score (AS) during the period and at the endpoint of the reperfusion were observed. Results The times until electrical and mechanical activity arrest in the experimental group were significantly shorter than those in control group ; HR, CF, MWC, CK, LDH, myocardial calcium content and AS were significantly better than those in control group. Conclusions These data suggest that 11,12 EET added to the cardioplegic solution of St.Thomas Ⅱ has lower incidence rate of reperfusion arrhythmias.
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).
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.
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 investigate whether single cycle ischemic preconditioning （IP） improves the myocardial preservation in patients undergoing cardiac valve replacement. Methods From August 2002 to April 2006, 85 patients who had chronic heart valve disease and required cardiac valve replacement were randomly divided into two groups. IP group, 47 allocated to receive IP and arrested with 4 C St. Thomas＇ Hospital cardioplegic solution during cardiopulmonary bypass（CPB）, preconditioning was accomplished by using single cycle of 2 minutes occlusion of aorta followed by 3 minutes of reperfusion before cross-clamping. Control group, 38 allocated to receive 4 C St. Thomas＇ Hospital cardioplegic solution alone. Myocardial protective effects were assessed by determinations of creatinine kinase-MB isoenzyme （CK-MB） and cardiac troponin I（cTnI）, ST-T changes, ventricular arrhythmias and other clinical data in ICU. Results Serum CK-MB and cTnI concentrations were increased postoperatively in two groups. At 24, 48 and 72h after operation, values of CK-MB in IP group was significantly lower than that in control group （P〈0.05）, cTnI at 24 and 48h after operation also less in IP group （P〈0.05）. The duration for patients needed for antiarrhythmic drugs in IP group was lower than that in control group （P〈0.05）. Compared with control group, fewer inotropic drugs were used in IP group. As a result, ICU stay time in IP group was shorter than that in control group （P〈0.05）. Conclusion IP enhances the myocardial protective effect when it was used with hypothermic hyper kalemic cardioplegic solution in patients undergoing cardiac valve replacement, IP significantly reduces the postoperative increase of CK-MB, cTnI and plessens the severity of postoperative ventricular arrhythmias.
The cardiac conduction system (CCS) is a set of specialized myocardial pathways that spontaneously generate and conduct impulses transmitting throughout the heart, and causing the coordinated contractions of all parts of the heart. A comprehensive understanding of the anatomical characteristics of the CCS in the heart is the basis of studying cardiac electrophysiology and treating conduction-related diseases. It is also the key of avoiding damage to the CCS during open heart surgery. How to identify and locate the CCS has always been a hot topic in researches. Here, we review the histological imaging methods of the CCS and the specific molecular markers, as well as the exploration for localization and visualization of the CCS. We especially put emphasis on the clinical application prospects and the future development directions of non-destructive imaging technology and real-time localization methods of the CCS that have emerged in recent years.
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.
Objective To evaluate the feasibility of imaging the rat cardiac conduction system (CCS) using transaortic antegrade perfusion of Alexa Fluor 633-labeled antibodies targeting hyperpolarization-activated cyclic nucleotide-gated cation channel 4 (HCN4) and connexin (Cx). The study also sought to optimize antibody dosage, perfusion duration, and assess the photostability of the dye. Methods Ex vivo rat heart model with transaortic antegrade perfusion was established using 33 male SPF-grade Sprague-Dawley (SD) rats. Primary and secondary antibody solutions were sequentially perfused in an antegrade manner. After perfusion for predetermined durations, the atrioventricular junction was observed, and the fluorescence intensity of the corresponding area was recorded. Five dose-gradient groups (n=3 rats/group), five perfusion time-gradient groups (n=3 rats/group), and ten continuous LED light exposure time-gradient groups (using 3 rats prepared with a fixed dose and time) were established to observe and record regional fluorescence intensity. Standard immunofluorescence staining was performed on both paraffin and frozen sections for comparative histological analysis. Results A region of aggregated red fluorescent signal was observed in the atrioventricular junction. Following semi-quantitative fluorescence intensity analysis of HCN4/Cx43 and validation through comparative histology, this structure was identified as the atrioventricular node (AVN) region. The AVN-to-background fluorescence intensity ratio showed no statistically significant differences among groups with increasing antibody dosage (P>0.05). The ratio increased with longer antibody perfusion times. Furthermore, no statistically significant differences in the ratio were observed among groups with extended light exposure (P>0.05). Conclusion Transaortic antegrade perfusion of fluorescently labeled antibodies can successfully image the AVN within the CCS of ex vivo rat hearts. Increasing the antibody dosage does not significantly improve the AVN imaging effect. Longer antibody perfusion time results in better imaging quality of the AVN. The fluorescent dye maintains sufficient visualization of the AVN even after prolonged (8 h) exposure to light.
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.
Electrocardiogram (ECG) signals are easily disturbed by internal and external noise, and its morphological characteristics show significant variations for different patients. Even for the same patient, its characteristics are variable under different temporal and physical conditions. Therefore, ECG signal detection and recognition for the heart disease real-time monitoring and diagnosis are still difficult. Based on this, a wavelet self-adaptive threshold denoising combined with deep residual convolutional neural network algorithm was proposed for multiclass arrhythmias recognition. ECG signal filtering was implemented using wavelet adaptive threshold technology. A 20-layer convolutional neural network (CNN) containing multiple residual blocks, namely deep residual convolutional neural network (DR-CNN), was designed for recognition of five types of arrhythmia signals. The DR-CNN constructed by residual block local neural network units alleviated the difficulty of deep network convergence, the difficulty in tuning and so on. It also overcame the degradation problem of the traditional CNN when the network depth was increasing. Furthermore, the batch normalization of each convolution layer improved its convergence. Following the recommendations of the Association for the Advancements of Medical Instrumentation (AAMI), experimental results based on 94 091 2-lead heart beats from the MIT-BIH arrhythmia benchmark database demonstrated that our proposed method achieved the average detection accuracy of 99.034 9%, 99.498 0% and 99.334 7% for multiclass classification, ventricular ectopic beat (Veb) and supra-Veb (Sveb) recognition, respectively. Using the same platform and database, experimental results showed that under the comparable network complexity, our proposed method significantly improved the recognition accuracy, sensitivity and specificity compared to the traditional deep learning networks, such as deep Multilayer Perceptron (MLP), CNN, etc. The DR-CNN algorithm improves the accuracy of the arrhythmia intelligent diagnosis. If it is combined with wearable equipment, internet of things and wireless communication technology, the prevention, monitoring and diagnosis of heart disease can be extended to out-of-hospital scenarios, such as families and nursing homes. Therefore, it will improve the cure rate, and effectively save the medical resources.