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.
Objective To investigate the risky factors of ventricular arrhythmias following open heart surgery in patients with giant left ventricle, and offer the basis in order to prevent it’s occurrence. Methods The clinical materials of 176 patients who had undergone the open heart surgery were analyzed retrospectively. There were 44 patients who had ventricular arrhythmia (ventricular arrhythmia group), 132 patients who had no ventricular arrhythmia as contrast (control group). The preoperative clinical data, indexes of types of cardiopathy, ultrasonic cardiogram, electrocardiogram and cardiopulmonary bypass (CPB) etc. were choosed, and tested by using χ2 test,t test and logistic regression to analyse the high endangered factors for incidence of ventricular arrhythmia after open heart surgery. Results Age≥55 years (OR=3.469), left ventricular enddiastolic diameter(LVEDD)≥80 mm (OR=3.927), left ventricular ejection fraction（LVEF）≤55% (OR=2.967), CPB time≥120min(OR=5.170) and aortic clamping time≥80min(OR=4.501) were the independent risk factors of ventricular arrhythmia. Conclusion Ventricular arrhythmia is a severe complication for the patients with giant left ventricle after open heart surgery, and influence the prognosis of the patients. Patient’s age, size of the left ventricle, cardiac function, CPB time and clamping time could influence the incidence of ventricular arrhythmias.
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.
Cardiopulmonary resuscitation (CPR) is a very important treatment after cardiac arrest. The optimal treatment strategy of CPR is uncertain. With the accumulation of clinical medical evidence, the CPR treatment recommendations have been changed. This article will review the current hot issues and progress, including the pathophysiological mechanisms of CPR, how to achieve high-quality chest compression, how to achieve CPR quality monitoring, how to achieve optimal CPR for different individuals and how to use antiarrhythmic drugs.
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.
目的:研究猝死患者在心肺复苏过程中出现室性心率失常时胺碘酮的应用。方法:选心肺复苏过程中出现的室性心律失常患者共107例,随机分为治疗组57例采用胺碘酮治疗,对照组50例采用利多卡因治疗,持续心电监护观察其疗效。结果:治疗组、对照组有效率分别为93.1%、80.0%,有显著性差异。结论:胺碘酮组的疗效明显高于利多卡因组,在治疗心肺复苏过程中室性心律失常应首选胺碘酮。