Background: Atrial fibrillation (AF) prediction improves by combining clinical scores with a polygenic risk score (PRS) for AF (AF-PRS), but there are limited studies of PRS for ventricular arrhythmia (VA) prediction.
Objective: We assessed the value of including multiple PRS for cardiovascular risk factors (CV-PRS) for incident AF and VA prediction.
Methods: We used 158,733 individuals of European ancestry from UK Biobank to build 3 models for AF: CHARGE-AF (AF1), AF1 + AF-PRS (AF2), AF2 + CV-PRS (AF3). Models for VA included sex and age (VA1), VA1 + coronary artery disease (CAD) PRS (CAD-PRS, VA2), and VA2 + CV-PRS (VA3), conducting separate analyses in subjects with and without ischemic heart disease (IHD). Performance was evaluated in individuals of European (N = 158,733), African (N = 7200), South Asian (N = 9241) and East Asian (N = 2076) ancestry from UK Biobank.
Results: AF2 had a higher C-index than AF1 (0.762 vs 0.746, P < .001), marginally improving to 0.765 for AF3 (P < .001, including PRS for heart failure, electrocardiogram and cardiac magnetic resonance measures). In South Asians, AF2 C-index was higher than AF1 (P < .001). For VA, the C-index for VA2 was greater than VA1 (0.692 vs 0.681, P < .001) in Europeans, which was also observed in South Asians (P < .001). VA3 improved prediction of VA in individuals with IHD.
Conclusion: CV-PRS improved AF prediction compared to CHARGE-AF and AF-PRS. A CAD-PRS improved VA prediction, while CV-PRS contributed in IHD. AF- and CAD-PRS were transferable to individuals of South Asian ancestry. Our results inform of the use of CV-PRS for personalized screening.
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http://dx.doi.org/10.1016/j.hrthm.2024.12.017 | DOI Listing |
Sci Rep
December 2024
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, 03680, Kyiv, Ukraine.
The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework.
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December 2024
Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Republic of Korea.
Vertebral collapse (VC) following osteoporotic vertebral compression fracture (OVCF) often requires aggressive treatment, necessitating an accurate prediction for early intervention. This study aimed to develop a predictive model leveraging deep neural networks to predict VC progression after OVCF using magnetic resonance imaging (MRI) and clinical data. Among 245 enrolled patients with acute OVCF, data from 200 patients were used for the development dataset, and data from 45 patients were used for the test dataset.
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December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
Department of Civil Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, have become popular for modeling traffic sensor networks. However, traditional graph convolutional networks (GCNs) face limitations in capturing long-range spatial correlations, which can hinder accurate long-term predictions.
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December 2024
School of Population Health, Faculty of Health Sciences, Curtin University, Bentley, WA, Australia.
While bacille-calmette-guerin (BCG) vaccination is one of the recommended strategies for preventing tuberculosis (TB), its coverage is low in several countries, including Ethiopia. This study investigated the spatial co-distribution and drivers of TB prevalence and low BCG coverage in Ethiopia. This ecological study was conducted using data from a national TB prevalence survey and the Ethiopian demographic and health survey (EDHS) to map the spatial co-distribution of BCG vaccination coverage and TB prevalence.
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