Background: Despite its benefits, oral anticoagulant (OAC) therapy in patients with atrial fibrillation (AF) is associated with hemorrhagic complications.
Aims: We aimed to evaluate clinical characteristics of AF patients at high risk of bleeding and the frequency of OAC use as well as identify factors that predict nonuse of OACs in these patients.
Methods: Consecutive AF patients hospitalized for urgent or planned reasons in cardiac centers were prospectively included in the registry in 2019. Patients with HAS-BLED ≥3 (high HAS-BLED group) were assumed to have a high risk of bleeding.
Results: Among 3598 patients enrolled in the study, 29.2% were at high risk of bleeding (44.7% female; median [Q1-Q3] age 72 [65-81], CHA2DS2-VASc score 5 [4-6], HAS-BLED 3 [3-4]). In this group, 14.5% of patients did not receive OACs, 68% received NOACs, and 17.5% VKAs. In multivariable analysis, the independent predictors of nonuse of oral OACs were as follows: creatinine level (odds ratio [OR], 1.441; 95% confidence interval [CI], 1.174-1.768; P <0.001), a history of gastrointestinal bleeding (OR, 2.918; 95% CI, 1.395-6.103; P = 0.004), malignant neoplasm (OR, 3.127; 95% CI, 1.332-7.343; P = 0.009), and a history of strokes or transient ischemic attacks (OR, 0.327; 95% CI, 0.166-0.642; P = 0.001).
Conclusions: OACs were used much less frequently in the group with a high HAS-BLED score than in the group with a low score. Independent predictors of nonuse of OACs were creatinine levels, a history of gastrointestinal bleeding, and malignant neoplasms. A history of stroke or transient ischemic attack increased the chances of receiving therapy.
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http://dx.doi.org/10.33963/v.kp.98356 | DOI Listing |
Sci Rep
December 2024
School of Physical Education, Southwest Petroleum University, Chengdu, 610500, China.
Stroke is one of the leading causes of death in developing countries, and China bears the largest global burden of stroke. This study aims to investigate the relationship between different dimensions of physical activity levels and stroke risk using a nationally representative database. We performed a cross-sectional analysis using data from the China Health and Retirement Longitudinal Study (CHARLS) 2020.
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December 2024
The School of Nursing, Fujian Medical University, No. 1 Xuefu North Road, Fuzhou, 350122, Fujian, China.
Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerably higher risk of dementia, whose daily self-care and life management (i.e.
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December 2024
Weather Program Office, Ocean and Atmospheric Research, NOAA, Silver Spring, MD, USA.
Tropical cyclone risks are expected to increase with climate change. One such risk is extreme ocean waves generated by surface winds from these systems. We use synthetic databases of both historical (1980-2017) and future (2015-2050) tropical cyclone tracks to generate wind fields and force a computationally efficient wave model to estimate significant wave heights across all global tropical cyclone basins.
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December 2024
School of Pharmacy, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Cuproptosis, a newly identified form of cell death, has drawn increasing attention for its association with various cancers, though its specific role in colorectal cancer (CRC) remains unclear. In this study, transcriptomic and clinical data from CRC patients available in the TCGA database were analyzed to investigate the impact of cuproptosis. Differentially expressed genes linked to cuproptosis were identified using Weighted Gene Co-Expression Network Analysis (WGCNA).
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December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
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