Background: Identifying features associated with atrial fibrillation (AF) documentation could inform screening. This study used published data to describe differences in documented and estimated AF prevalence in general practices, and explored predictors of variations in AF prevalence.
Methods: Cross-sectional study of 7318 general practices in England. Descriptive and inferential statistics were undertaken. Multiple linear regression was used to model the difference between estimated AF and documented AF, adjusted for population, practice and practice performance variables.
Results: Documented AF prevalence was lower than estimated (- 0.55% 95% confidence intervals, -1.89, 2.99). The proportion of variability accounted for in the final regression model was 0.25. Factors positively associated with AF documentation (increase in difference between estimated and documented), were patients 65-74 years, 75 years +, Black or South Asian ethnicity, diabetes mellitus and practices in East and Midlands of England. Eight variables (female patients, deprivation score, heart failure and peripheral artery disease, total patients per practice, full-time GPs and nurses; and location in South of England) were negatively associated with AF documentation (reduction in difference).
Conclusion: Variations in AF documentation were predicted by several practice and population characteristics. Screening could target these sources of variation to decrease variation and improve AF documentation.
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http://dx.doi.org/10.1093/pubmed/fdac009 | DOI Listing |
Bioinform Adv
December 2024
Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy.
Motivation: Proteins at the cell surface connect signaling networks and largely determine a cell's capacity to communicate and interact with its environment. In particular, variations in transcriptomic profiles are often observed between healthy and diseased cells, leading to distinct sets of cell-surface proteins. For these reasons, cell-surface proteins may act as biomarkers for the detection of cells of interest in tissues or body fluids, are often the target of pharmaceutical agents, and hold significant promise in the clinical practice for diagnosis, prognosis, treatment development, and evaluation of therapy response.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Dermatology, Hunan Key Laboratory of Skin Cancer and Psoriasis, Hunan Engineering Research Center of Skin Health and Disease, Xiangya Clinical Research Center for Cancer Immunotherapy, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan, China.
Aortic dissection (AD) is one of the most dangerous diseases of the cardiovascular system, which is characterized by acute onset and poor prognosis, while the pathogenesis of AD is still unclear and may affect or even delay the diagnosis of AD. Anchorage-dependent cell death (Anoikis) is a special mode of cell death, which is programmed cell death caused by normal cells after detachment from extracellular matrix (ECM) and has been widely studied in the field of oncology in recent years. In this study, we applied bioinformatics analysis, according to the results of research analysis and Gene Ontology (GO), as well as Kyoto Encyclopedia of Genes and Genomes (KEGG), finally found 3 anoikis-related genes (ARGs) based on machine learning.
View Article and Find Full Text PDFContemp Clin Trials
December 2024
San Francisco VA Medical Center, United States of America; Department of Medicine, University of California, San Francisco, CA, United States of America; Measurement Science Quality Enhancement Research Initiative, San Francisco VA Healthcare System, United States of America. Electronic address:
Background: Colorectal cancer (CRC) prevention is a Veterans Affairs (VA) priority. Colonoscopy quality, especially adenoma detection rate (ADR), is critical for effective screening. Our research indicates considerable variation in ADR among VA providers.
View Article and Find Full Text PDFBioinformatics
December 2024
Max-Planck-Institute for Biology Tübingen, Department of Molecular Biology, Tübingen, Germany.
Motivation: As genome graphs are powerful data structures for representing the genetic diversity within populations, they can help identify genomic variations that traditional linear references miss, but their complexity and size makes the analysis of genome graphs challenging. We sought to develop a genome graph analysis tool that helps these analyses to become more accessible by addressing the limitations of existing tools. Specifically, we improve scalability and user-friendliness, and we provide many new statistics tailored to variation graphs for graph evaluation, including sample-specific features.
View Article and Find Full Text PDFFront Cell Dev Biol
December 2024
Institute of Experimental Genetics, Helmholtz Munich GmbH, German Research Center for Environmental Health, Neuherberg, Germany.
Sexual dimorphism involves distinct anatomical, physiological, behavioral, and developmental differences between males and females of the same species, influenced by factors prior to conception and during early development. These sex-specific traits contribute to varied phenotypes and individual disease risks within and across generations and understanding them is essential in mammalian studies. Hormones, sex chromosomes, and imprinted genes drive this dimorphism, with over half of quantitative traits in wildtype mice showing sex-based variation.
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