Both HFpEF and HFmrEF should be included in calculating CHA2DS2-VASc score: A Taiwanese longitudinal cohort.

BACKGROUND: Congestive heart failure (CHF) as a risk of stroke in patients with atrial fibrillation (AF) mainly referred to patients with left ventricular systolic dysfunction. Whether this should include patients with preserved ejection fraction is debatable. OBJECTIVE: The study aimed to investigate the variation in stroke risk of AF patients with heart failure with preserved ejection fraction (HFpEF), heart failure with mid-range ejection fraction (HFmrEF), and heart failure with reduced ejection fraction (HFrEF) for enhancing risk assessment and subsequent management strategies. METHODS: In a longitudinal study using the National Taiwan University Hospital integrated Medical Database, 8358 patients with AF were observed for 10 years (mean follow-up, 3.76 years). The study evaluated the risk of ischemic stroke in patients with differing ejection fractions and CHA2DS2-VASc score, further using Cox models adjusted for risk factors of AF-related stroke. RESULTS: Patients with HFpEF and HFmrEF had a higher mean CHA2DS2-VASc score compared with patients with HFrEF (4.30 ± 1.729 vs 4.15 ± 1.736 vs 3.73 ± 1.712; P < .001) and higher risk of stroke during follow-up (hazard ratio [HR], 1.40 [1.161-1.688; P < .001] for HFmrEF; HR, 1.184 [1.075-1.303; P = .001] for HFpEF vs no CHF) after multivariate adjustment). In patients with lower CHA2DS2-VASc score (0-4), presence of any type of CHF increased ischemic stroke risk (HFrEF HR, 1.568 [1.189-2.068; P = .001]; HFmrEF HR, 1.890 [1.372-2.603; P < .001]; HFpEF HR, 1.800 [1.526-2.123; P < .001] vs no CHF). CONCLUSION: After multivariate adjustment, HFpEF and HFmrEF showed a similar risk of stroke in AF patients. Therefore, it is important to extend the criteria for C in the CHA2DS2-VASc score to include patients with HFpEF and HFmrEF. In patients with fewer concomitant stroke risk factors, the presence of any subtype of CHF increases risk for ischemic stroke.

CheXNet and feature pyramid network: a fusion deep learning architecture for multilabel chest X-Ray clinical diagnoses classification.

The existing multilabel X-Ray image learning tasks generally contain much information on pathology co-occurrence and interdependency, which is very important for clinical diagnosis. However, the challenging part of this subject is to accurately diagnose multiple diseases that occurred in a single X-Ray image since multiple levels of features are generated in the images, and create different features as in single label detection. Various works were developed to address this challenge with proposed deep learning architectures to improve classification performance and enrich diagnosis results with multi-probability disease detection. The objective is to create an accurate result and a faster inference system to support a quick diagnosis in the medical system. To contribute to this state-of-the-art, we designed a fusion architecture, CheXNet and Feature Pyramid Network (FPN), to classify and discriminate multiple thoracic diseases from chest X-Rays. This concept enables the model to extract while creating a pyramid of feature maps with different spatial resolutions that capture low-level and high-level semantic information to encounter multiple features. The model's effectiveness is evaluated using the NIH ChestXray14 dataset, with the Area Under Curve (AUC) and accuracy metrics used to compare the results against other cutting-edge approaches. The overall results demonstrate that our method outperforms other approaches and has become promising for multilabel disease classification in chest X-Rays, with potential applications in clinical practice. The result demonstrated that we achieved an average AUC of 0.846 and an accuracy of 0.914. Further, our proposed architecture diagnoses images in 0.013 s, faster than the latest approaches.

Pattern of Endothelialization in Left Atrial Appendage Occluder by Optic Coherence Tomography: A Pilot Study.

BACKGROUND: Implantation of the left atrial appendage occluder (LAAO) has been proven to prevent stroke effectively in patients with atrial fibrillation who cannot tolerate anticoagulants. Incomplete endothelization of LAAO may cause device-related thrombus, and currently no good image modality exists to clearly see LAAO endothelialization. We aimed to use coronary optic coherence tomography (OCT) to visualize LAAO endothelialization. METHODS AND RESULTS: We enrolled 14 patients (72.8±9.4 years old) undergoing pulmonary vein isolation with a preexisting LAAO implanted more than 1 year ago (5 Watchman and 9 Amulet). After pulmonary vein isolation, we did OCT via steerable sheath and coronary guiding catheter to adjust OCT probe location and injected contrast medium to visualize the LAAO surface. In vitro testing was also performed to see the bare occluder. In vitro OCT showed the surface of the bare device as an interrupted granule pattern, which included the Watchman surface polytetrafluoroethylene membrane string, Amulet disc metal strut, and inner polytetrafluoroethylene membrane string. In the implanted Watchman, OCT showed endothelialization as a smooth surface layer with noninterrupted coarser granules. In the implanted Amulet, OCT showed endothelialization as thin (early) or thick (late) endothelialization layer covering struts with OCT shadows. Among patients with Watchman, 2 showed no, 2 early, and 1 complete endothelialization. Among patients with Amulet, 2 showed no, 3 early, and 4 late endothelialization. CONCLUSIONS: We demonstrated the feasibility of OCT to visualize LAAO endothelization with high resolution. Further studies are needed to determine antithrombotic regimens if incomplete endothelization is detected. A new OCT catheter may be designed specifically for LAAO.