Gender differences in fibrosis remodeling in patients with long-standing persistent atrial fibrillation

The success rate of catheter ablation in atrial fibrillation (AF) is known to be lower in females than in males. However, while the exact mechanism for this phenomenon remains to be elucidated, tissue fibrosis may play an important role in this regard. It has been shown that fibrosis promotes AF and its recurrence, thereby substantially reducing the efficacy of catheter ablation in AF patients. Thus, we hypothesized that fibrosis may contribute to gender differences in the outcomes of AF catheter ablation. Here we systematically assessed pulmonary vein sleeves obtained from 166 patients with and without long-standing persistent-AF (LSP-AF) in order to identify gender-specific mechanistic differences in fibrosis remodeling of AF patients. Histological analysis revealed that the female LSP-AF group, rather than its male counterpart, had a higher degree of fibrosis when compared to the NON-AF group. Further analysis using microarray, immunohistochemistry and Western Blot displayed that gender differences in fibrosis remodeling of LSP-AF were mainly due to the inherent differential expression of fibrosis-related genes (n=32) and proteins (n=6). Especially, those related to the TGFβ/Smad3 pathway appeared to be up-regulated in the female LSP-AF group thus promoting an aggravation of fibrosis remodeling. In summary, our data suggest that the aggravation of fibrosis remodeling in women may be an important reason for the low success rate of AF catheter ablation when compared to men. Therefore, inhibiting the TGFβ/Smad3 pathway-mediated fibrosis could represent an interesting target for future therapeutic concepts to improve the success rate of AF catheter ablation in women

Gut Microbiota in Patients with Postoperative Atrial Fibrillation Undergoing Off-Pump Coronary Bypass Graft Surgery

Background: Post-operative atrial fibrillation (POAF) is one of the most common complications of cardiac surgery. However, the underlying mechanism is not well understood. Alterations in the gut microbiota are associated with the development of atrial fibrillation (AF). The aim of this study was to explore the relationship between gut microbiota and POAF. Methods: Fecal samples were collected before surgery from 45 patients who underwent coronary artery bypass grafting with POAF and 90 matched patients without POAF (1:2). 16S rRNA sequencing was used to detect the microbiome profiles of 45 POAF patients and 89 matched patients (one sample in the no-POAF group was deleted owing to low quality after sequencing). Plasma 25-hydroxy vitamin D level was measured by ELISA. Results: Compared to the patients without POAF, gut microbiota composition was remarkably changed in the patients with POAF, with an increase in Lachnospira, Acinetobacter, Veillonella and Aeromonas, and a decrease in Escherichia–Shigella, Klebsiella, Streptococcus, Brevundimonas and Citrobacter. Furthermore, plasma 25-hydroxy vitamin D levels were decreased in POAF patients and negatively correlated with an abundance of Lachnospira. Conclusions: The gut microbiota composition between patients with and without POAF is significantly different, implying that gut microbiota may play a role in the pathogenesis of POAF. Further studies are needed to fully clarify the role of gut microbiota in the initiation of AF

Robust Artificial Intelligence Tool for Atrial Fibrillation Diagnosis: Novel Development Approach Incorporating Both Atrial Electrograms and Surface ECG and Evaluation by Head‐to‐Head Comparison With Hospital‐Based Physician ECG Readers

Background Atrial fibrillation (AF) increases risk of embolic stroke, and in postoperative patients, increases cost of care. Consequently, ECG screening for AF in high‐risk patients is important but labor‐intensive. Artificial intelligence (AI) may reduce AF detection workload, but AI development presents challenges. Methods and Results We used a novel approach to AI development for AF detection using both surface ECG recordings and atrial epicardial electrograms obtained in postoperative cardiac patients. Atrial electrograms were used only to facilitate establishing true AF for AI development; this permitted the establishment of an AI‐based tool for subsequent AF detection using ECG records alone. A total of 5 million 30‐second epochs from 329 patients were annotated as AF or non‐AF by expert ECG readers for AI training and validation, while 5 million 30‐second epochs from 330 different patients were used for AI testing. AI performance was assessed at the epoch level as well as AF burden at the patient level. AI achieved an area under the receiver operating characteristic curve of 0.932 on validation and 0.953 on testing. At the epoch level, testing results showed means of AF detection sensitivity, specificity, negative predictive value, positive predictive value, and F1 (harmonic mean of positive predictive value and sensitivity) as 0.970, 0.814, 0.976, 0.776, and 0.862, respectively, while the intraclass correlation coefficient for AF burden detection was 0.952. At the patient level, AF burden sensitivity and positive predictivity were 96.2% and 94.5%, respectively. Conclusions Use of both atrial electrograms and surface ECG permitted development of a robust AI‐based approach to postoperative AF recognition and AF burden assessment. This novel tool may enhance detection and management of AF, particularly in patients following operative cardiac surgery

Development and validation of a deep learning-based fully automated algorithm for pre-TAVR CT assessment of the aortic valvular complex and detection of anatomical risk factors: a retrospective, multicentre studyResearch in context

Summary: Background: Pre-procedural computed tomography (CT) imaging assessment of the aortic valvular complex (AVC) is essential for the success of transcatheter aortic valve replacement (TAVR). However, pre-TAVR assessment is a time-intensive process, and the visual assessment of anatomical structures at the AVC shows interobserver variability. This study aimed to develop and validate a deep learning-based algorithm for pre-TAVR CT assessment and anatomical risk factor detection. Methods: This retrospective, multicentre study used AVC CT scans to develop a deep learning-based, fully automated algorithm, which was then internally and externally validated. After loading CT scans into the algorithm, it automatically assessed the essential anatomical structure data required for TAVR planning. CT scans of 1252 TAVR candidates continuously enrolled from Fuwai Hospital were used to establish training and internal validation datasets, while CT scans of 100 patients with aortic valve disease across 19 Chinese hospitals served as an external validation dataset. The validation focused on segmentation performance, localisation and measurement accuracy of key anatomical structures, detection ability of specific anatomical risk factors, and improvement in assessment efficiency. Findings: Relative to senior observers, our algorithm achieved significant consistent performance with remarkable accuracy, efficiency and ease in segmentation, localisation, and the assessment of the aortic annulus perimeter-derived diameter, and other basic planes, coronary ostia height, calcification volume, and aortic angle. The intraclass correlation coefficient values for the algorithm in the internal and external validation datasets were up to 0.998 (95% confidence interval 0.998–0.998), respectively. Furthermore, the algorithm demonstrated high alignment in detecting specific anatomical risk factors, with accuracy, sensitivity, and specificity up to 0.989 (95% CI 0.973–0.996), 0.979 (95% CI 0.936–0.995), 0.986 (95% CI 0.945–0.998), respectively. Interpretation: Our algorithm efficiently performs pre-TAVR assessments by using AVC CT imaging with accuracy comparable to senior observers, potentially improving TAVR planning in clinical practice. Funding: National Key R&D Program of China (2020YFC2008100), CAMS Innovation Fund for Medical Sciences (2022-I2M-C&T-B-044)