Percutaneous left atrial appendage closure with SeaLA device in non-valvular atrial fibrillation.

OBJECTIVES: The clinical efficacy and safety of a novel left atrial appendage (LAA) occluder of the SeaLA closure system in patients with nonvalvular atrial fibrillation (NVAF) were reported. BACKGROUND: Patients with NVAF are at a higher risk of stroke compared to healthy individuals. Left atrial appendage closure (LAAC) has emerged as a prominent strategy for reducing the risk of thrombosis in individuals with NVAF. METHODS: A prospective, multicenter study was conducted in NVAF patients with a high risk of stroke. RESULTS: The LAAC was successfully performed in 163 patients. The mean age was 66.93 ± 7.92 years, with a mean preoperative CHA2DS2-VASc score of 4.17 ± 1.48. One patient with residual flow >3 mm was observed at the 6-month follow-up, confirmed by TEE. During the follow-up, 2 severe pericardiac effusions were noted, and 2 ischemic strokes were observed. Four device-related thromboses were resolved after anticoagulation treatment. There was no device embolism. CONCLUSIONS: The LAAC with the SeaLA device demonstrates encouraging feasibility, safety, and efficacy outcomes.

An Automatic Coronary Microvascular Dysfunction Classification Method Based on Hybrid ECG Features and Expert Features.

OBJECTIVE: In recent years, the early diagnosis and treatment of coronary microvascular dysfunction (CMD) have become crucial for preventing coronary heart disease. This paper aims to develop a computer-assisted autonomous diagnosis method for CMD by using ECG features and expert features. APPROACH: Clinical electrocardiogram (ECG), myocardial contrast echocardiography (MCE), and coronary angiography (CAG) are used in our method. Firstly, morphological features, temporal features, and T-wave features of ECG are extracted by multi-channel residual network with BiLSTM (MCResnet-BiLSTM) model and the multi-source T-wave features (MTF) extraction model, respectively. And these features are fused to form ECG features. In addition, the CFR[Formula: see text] is calculated based on the parameters related to the MCE at rest and stress state, and the Angio-IMR is calculated based on CAG. The combination of CFR[Formula: see text] and Angio-IMR is termed as expert features. Furthermore, the hybrid features, fused from the ECG features and the expert features, are input into the multilayer perceptron to implement the identification of CMD. And the weighted sum of the soft maximum loss and center loss is used as the total loss function for training the classification model, which optimizes the classification ability of the model. RESULT: The proposed method achieved 93.36% accuracy, 94.46% specificity, 92.10% sensitivity, 95.89% precision, and 93.95% F1 score on the clinical dataset of the Second Affiliated Hospital of Zhejiang University. CONCLUSION: The proposed method accurately extracts global ECG features, combines them with expert features to obtain hybrid features, and uses weighted loss to significantly improve diagnostic accuracy. It provides a novel and practical method for the clinical diagnosis of CMD.