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Background: Polypharmacy (i.e., treatment with ≥ 5 drugs) is common in patients with atrial fibrillation (AF) and has been associated with suboptimal management and worse outcomes. Little is known about how prescribed drug patterns affect management and prognosis in patients with AF.
Methods: Based on data from the prospective global GLORIA-AF Registry Phase III (recruiting patients with AF and CHADS-VASc score ≥ 1), we performed a latent class analysis to identify treatment patterns based on 14 drug classes including cardiovascular (CV) and non-CV drugs. We analysed associations with oral anticoagulant (OAC) use and risk of a composite primary outcome (all-cause death and major adverse cardiovascular events (MACE)) and secondary outcomes.
Results: Among 21,245 patients (mean age 70.2 ± 10.3 years, 44.9% females), we identified 6 patterns: i) Low Medicated pattern (18.3%); ii) Hypertension pattern (21.1%); iii) Heart Failure pattern (20.0%); iv) CV Prevention pattern (21.0%); v) Mixed Morbidity pattern (4.5%); and vi) High Medicated pattern (15.0%). All groups had higher odds of OAC use vs the Low Medicated pattern, with highest prevalences in the Heart Failure pattern (OR [95%CI]: 2.17 [1.90-2.48]) and the High Medicated pattern (OR [95%CI]: 2.08 [1.77-2.44]). Over 3-year follow-up, Heart Failure, Mixed Morbidity and High Medicated patterns were associated with higher risk of the primary composite outcome (aHR [95%CI]: 1.32 [1.14-1.53]; 1.45 [1.17-1.80] and 1.35 [1.14-1.60], respectively). Similar results were observed for all-cause mortality.
Conclusions: In patients with AF, different treatment patterns can be identified. Each pattern was associated with unique OAC use and long-term clinical outcomes.
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http://dx.doi.org/10.1186/s12916-025-03858-w | DOI Listing |
Neural Netw
September 2025
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
3D shape defect detection plays an important role in autonomous industrial inspection. However, accurate detection of anomalies remains challenging due to the complexity of multimodal sensor data, especially when both color and structural information are required. In this work, we propose a lightweight inter-modality feature prediction framework that effectively utilizes multimodal fused features from the inputs of RGB, depth and point clouds for efficient 3D shape defect detection.
View Article and Find Full Text PDFRetin Cases Brief Rep
September 2025
Doheny Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, California, USA.
Purpose: To report the examination and multimodal imaging findings of a patient with unilateral bull's eye maculopathy.
Methods: A retrospective chart review of a 77-year-old patient with unilateral bull's eye maculopathy who presented to a tertiary retinal practice was performed. The patient's history, visual acuity, examination and multimodal imaging findings over five years of follow-up were described.
Interact J Med Res
September 2025
Department of Medicine, MacKay Medical College, New Taipei, Taiwan.
Background: Dengue fever remains the most significant vector-borne disease in Southeast Asia, imposing a substantial burden on public health systems. Global warming and increased international mobility may exacerbate the disease's prevalence. Furthermore, the unprecedented COVID-19 pandemic may have influenced the epidemiological patterns of dengue.
View Article and Find Full Text PDFJMIR Public Health Surveill
September 2025
Earth Observation Centre (EOC), Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
Background: Neighborhoods resulting from rapid urbanization processes are often saturated with eateries for local communities, potentially increasing exposure to unhealthy foods and creating diabetogenic residential habitats.
Objective: We examined the association between proximity of commercial food outlets to local neighborhood residences and type 2 diabetes (T2D) cases to explore how local T2D rates vary by location and provide policy-driven metrics to monitor food outlet density as a potential control for high local T2D rates.
Methods: This cross-sectional ecological study included 11,354 patients with active T2D aged ≥20 years geocoded using approximate neighborhood residence aggregated to area-level rates and counts by subdistricts (mukims) in Penang, northern Malaysia.
J Med Internet Res
September 2025
Institute for Health Care Management and Research, University of Duisburg-Essen, Essen, Germany.
Background: Mental and behavioral disorders affect approximately 28% of the adult population in Germany per year, with treatment being provided through a diverse health care system. Yet there are access and capacity problems in outpatient mental health care. One innovation that could help reduce these barriers and improve the current state of care is the use of mobile health (mHealth) apps, known in Germany as Digitale Gesundheitsanwendungen (DiGA).
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