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      A positive association between RDW and coronary heart disease in the rheumatoid arthritis population: A cross-sectional study from NHANES

      research-article
      , Master a , * , , , Master a , , Doctor a , , Master a , , Master a , , Master a , , Master a
      Medicine
      Lippincott Williams & Wilkins
      CHD, RA, RDW and atherosclerosis

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          Abstract

          Previous research has indicated that higher red blood cell distribution width (RDW) increases the risk of coronary heart disease. However, no studies have established a link between RDW and coronary heart disease in the rheumatoid arthritis population. This research aims to explore the association between RDW and coronary heart disease among individuals with rheumatoid arthritis. We selected demographic data, laboratory data, lifestyle, and medical history from the National Health and Nutrition Examination Survey (NHANES), specifically including age, gender, poverty, RDW, race, BMI, diabetes, education, coronary heart disease, hypertension, cholesterol, smoking, and drinking. RDW and coronary heart disease were found to have a positive association in the rheumatoid arthritis population (OR = 1.145, 95%CI: 1.036–1.266, P = .0098), even after adjusting for factors such as age, gender, race, education level, smoking, and drinking. Subgroup analysis showed a stronger positive association, particularly in individuals aged 55–66 years, males, and the Hispanic White population with diabetes or hypercholesterolemia. There is a significant correlation between RDW and coronary heart disease among individuals with rheumatoid arthritis.

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          Relation Between Red Blood Cell Distribution Width and Cardiovascular Event Rate in People With Coronary Disease.

          BACKGROUND: Higher levels of red blood cell distribution width (RDW) may be associated with adverse outcomes in patients with heart failure. We examined the association between RDW and the risk of all-cause mortality and adverse cardiovascular outcomes in a population of people with coronary disease who were free of heart failure at baseline. METHODS AND RESULTS: We performed a post hoc analysis of data from the Cholesterol and Recurrent Events study. Baseline RDW was measured in 4111 participants who were randomized to receive pravastatin 40 mg daily or placebo and followed for a median of 59.7 months. We used Cox proportional hazards models to examine the association between RDW and adverse clinical outcomes. During nearly 60 months of follow-up, 376 participants died. A significant association was noted between baseline RDW level and the adjusted risk of all-cause mortality (hazard ratio per percent increase in RDW, 1.14; 95% confidence interval, 1.05 to 1.24). After categorization based on quartile of baseline RDW and further adjustment for hematocrit and other cardiovascular risk factors, a graded independent relation between RDW and death was observed (P for trend=0.001). For instance, participants with RDW in the highest quartile had an adjusted hazard ratio for death of 1.78 (95% confidence interval, 1.28 to 2.47) compared with those in the lowest quartile. Higher levels of RDW were also associated with increased risk of coronary death/nonfatal myocardial infarction, new symptomatic heart failure, and stroke. CONCLUSIONS: We found a graded independent relation between higher levels of RDW and the risk of death and cardiovascular events in people with prior myocardial infarction but no symptomatic heart failure at baseline.
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            Data mining in clinical big data: the frequently used databases, steps, and methodological models

            Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.
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              Update on the epidemiology, risk factors, and disease outcomes of rheumatoid arthritis

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                Author and article information

                Contributors
                Journal
                Medicine (Baltimore)
                Medicine (Baltimore)
                MD
                Medicine
                Lippincott Williams & Wilkins (Hagerstown, MD )
                0025-7974
                1536-5964
                08 March 2024
                08 March 2024
                : 103
                : 10
                : e37315
                Affiliations
                [a ]Department of Emergency, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, P.R. China.
                Author notes
                [* ]Correspondence: Mei Qi Zhang, Department of Emergency, Affiliated Hospital of North Sichuan Medical College, No.1 MaoYuan Nan Road, Nanchong, Sichuan 637000, P.R. China (e-mail: 1027085703@ 123456qq.com / lwd10000@ 123456126.com ).
                Author information
                https://orcid.org/0009-0005-6512-9352
                Article
                00053
                10.1097/MD.0000000000037315
                10919533
                38457585
                d9142cfa-018c-47ac-a734-f58722a7362e
                Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.

                This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 September 2023
                : 26 January 2024
                : 29 January 2024
                Categories
                3400
                Research Article
                Observational Study
                Custom metadata
                TRUE

                chd,ra,rdw and atherosclerosis
                chd, ra, rdw and atherosclerosis

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