Abstracts tagged "Data Management"

Abstract Number: 0341 • ACR Convergence 2021
Addition of Narrative Text Abstraction to ICD-Based Abstraction Significantly Improves Identification of Lupus Nephritis in Real-World Data
Meghan Tierney and Chris Rowe, PicnicHealth, San Francisco, CA
Background/Purpose: Lupus nephritis (LN) is often underrecognized and difficult to identify retrospectively, presenting challenges for clinicians and researchers hoping to explore this condition using real-world…
Abstract Number: 0373 • ACR Convergence 2021
Self-monitoring of Disease Activity with a Smartphone App Is Feasible in Routine Clinical Management of Patients with Axial Spondyloarthritis – a Proof of Concept Study
Uta Kiltz¹, Robin Kempin¹, Jutta Richter², Anna Schlegel¹, Xenofon Baraliakos¹, Styliani Tsiami¹, Bjoern Buehring¹, David Kiefer³ and Juergen Braun¹, ¹Rheumazentrum Ruhrgebiet Herne, Ruhr-Universität Bochum, Herne, Germany, ²Rheumatology and Hiller Research Unit, Heinrich-Heine-University Duesseldorf, Medical Faculty, Duesseldorf, Germany, ³Rheumazentrum Ruhrgebiet Herne, Ruhr-Universität Bochum, Essen, Germany
Background/Purpose: Assessment and monitoring of disease activity and functioning is of major importance for qualified management of patients with axial spondyloarthritis (axSpA). This includes tight…
Abstract Number: 0853 • ACR Convergence 2021
A Combined Patient Registry and Biobank Laboratory Information System for Prospective Multisite Chronic Rheumatic Disease Research Using REDCap
Craig Willers¹, Tom Lynch¹, Vibhasha Chand², Mohammad Islam³, Marissa Lassere⁴, Helen Keen⁵, Tony Kenna⁶, Susan Lester⁷, Ranjeny Thomas⁸, Premarani Sinnathurai⁹, Mihir Wechalekar¹⁰, Ashley Fletcher¹¹, Daniel Lightowler¹², Sheeraz Mohd¹³, Nishta Ramnoruth¹⁴, Carlee Ruediger¹⁵, Helen Weedon¹⁶ and Lyn March¹⁷, ¹Institute of Bone and Joint Research, Kolling Institute, University of Sydney, St Leonards, Australia, ²Public Health and Preventive Medicine, Monash University, Melbourne, Australia, ³Information and Communications Technology, University of Sydney, Sydney, Australia, ⁴St George Hospital; School of Population Health, University of New South Wales, Carlton, Australia, ⁵University of Western Australia, Daglish, Australia, ⁶Queensland University of Technology, School of Biomedical Sciences, Brisbane, Australia, ⁷Rheumatology Department, The Basil Hetzel Institute, The Queen Elizabeth Hospital, Adelaide, Australia, ⁸University of Queensland, Diamantina Institute, Brisbane, Australia, ⁹Institute of Bone and Joint Research, Kolling Institute, University of Sydney; Department of Rheumatology, Royal North Shore Hospital, Northern Sydney Local Health District, Sydney, Australia, ¹⁰Flinders Medical Centre, Southern Adelaide Local Health Network, Bedford Park, Australia, ¹¹Cabrini Health, Malvern, Australia, ¹²Medical School, University of Western Australia; Rheumatology Department, Fiona Stanley Hospital, Perth, Australia, ¹³Fiona Stanley Hospital, South Metropolitan Health Services, Perth, Australia, ¹⁴Diamantina Institute, University of Queensland, Brisbane, Australia, ¹⁵University of Adelaide; The Queen Elizabeth Hospital, Adelaide, Australia, ¹⁶Flinders University, Bedford Park, Australia, ¹⁷Institute of Bone and Joint Research, Kolling Institute, University of Sydney; Department of Rheumatology, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, Sydney, Australia
Background/Purpose: As we enter the big data revolution, comprehensive informatics solutions are essential to realising precision medicine for rheumatic and other chronic disease patients, especially…
Abstract Number: 1896 • ACR Convergence 2021
Initial Results from the Implementation of a National Hydroxychloroquine Safe Prescribing Dashboard Within the Veterans Health Administration
Anna Montgomery¹, Gary Tarasovsky², Iziegbe Ehiorobo³, Mary Whooley², Jennifer Barton⁴, Khushboo Sheth⁵, Kimberly Reiter⁶, Meredith Keller⁷, Lorinda Chung⁸, Lori Bennett⁹, Jo Dana², Elizabeth Wahl¹⁰ and Gabriela Schmajuk³, ¹Department of Veterans Affairs, Tiburon, CA, ²San Francisco VA Healthcare System, San Francisco, CA, ³University of California San Francisco, San Francisco, CA, ⁴Portland VA Healthcare System, Portland, OR, ⁵Stanford University/VA Palo Alto, Atherton, CA, ⁶Raymond G Murphy VA Medical Center, Albuquerque, NM, ⁷New Mexico VA Health Care System, Albuquerque, NM, ⁸Stanford University, Palo Alto, CA, ⁹Charleston VAHCS, Charleston, SC, ¹⁰VA Puget Sound Health Care System, Seattle, WA
Background/Purpose: Hydroxychloroquine (HCQ) is a commonly used medication for patients with lupus erythematosus, rheumatoid arthritis, and other autoimmune conditions. However, HCQ daily doses of ≥…
Abstract Number: PP02 • ACR Convergence 2021
Leveraging Digital Health Tracking to Improve Arthritis Management
Katie Roberts, Annapolis, MD
Background/Purpose: I was diagnosed with psoriasis when I was age 10 in 1986. At that time, my treatment plan consisted of regular application of Eucerin…
Abstract Number: 0047 • ACR Convergence 2020
Administrative Barriers to Enrollment of Ethnic Minorities in Clinical Research of Rheumatic/Immune-Mediated Diseases
Brittany Banbury¹, Sharon Dowell¹, Gail Kerr², Mercedes Quinones², Ginette Okoye¹ and Ilona Jileaeva¹, ¹Howard University Hospital, Washington, DC, ²Washington DC VA Medical Center, Washington, DC
Background/Purpose: Inclusion of ethnic minority subsets in clinical research remains subpar despite mandates for increased participation. Lack of trust, cultural sensitivity and bias, stereotyping, health…
Abstract Number: 0550 • ACR Convergence 2020
Mapping Multimorbidity Using Drug Concept Unique Identifiers (RxCUIs) via the Rx-Risk Comorbidity Index
Jared Vanderbleek¹, Justin Owensby¹, Alex Mccannaly¹, Lang Chen¹, Bryant England², Jeffrey R Curtis³ and Huifeng Yun³, ¹University of Alabama at Birmingham, Birmingham, AL, ²University of Nebraska Medical Center, Omaha, NE, ³Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL
Background/Purpose: Identifying and accurately classifying comorbid conditions in large, real-world data sources is crucial for cohort establishment and confounder adjustment. However, the ability to conduct…
Abstract Number: 1307 • ACR Convergence 2020
Detecting Subtle Changes in Fundoscopic Retinal Images in Patients with Axial Spondyloarthritis with Deep Learning
Yun Ju Huang¹, Chang-Fu Kuo², Yu Huei Huang³, Yih Shiou Hwang⁴ and Chihung Lin⁵, ¹Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital and Chang Gung University, New Taipei City, Taiwan (Republic of China), ²Chang Gung Memorial Hospital, Taoyuan, Taiwan (Republic of China), ³Department of Dermatology, Chang Gung Memorial Hospital, Taoyuan city, Taiwan (Republic of China), ⁴Department of Ophthalmology, Chang Gung Memorial Hospital, Taoyuan city, Taiwan (Republic of China), ⁵Chang Gung Memorial Hospital, Taoyuan city, Taiwan (Republic of China)
Background/Purpose: Fundoscopy is essential to identify the retinopathy of patients with autoimmune diseases. However, the classification of different autoimmune diseases is difficult by human eyes…
Abstract Number: 1723 • ACR Convergence 2020
RA Flare Prediction via Machine Learning and Algorithm Based on SSDM Big Data
Yan Zhao¹, Rong Mu², Xiaomei Li³, Hongsheng Sun⁴, Cundong Mi⁵, Guosheng Wang³, Shengqian Xu⁶, Minghua Xu⁷, Haiying Chen⁸, Qingchun Huang⁹, Ling Lei¹⁰, HaiLi Shen¹¹, Hui Xiao¹², Yuhua Jia¹³, Bing Wu¹³, Xin Chen¹², Shengsong Jia¹² and Fei Xiao¹³, ¹Peking Union Medical College hospital, Beijing, Beijing, China (People's Republic), ²People's Hospital, Beijing University Medical School, Beijing, Beijing, China (People's Republic), ³the First Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui, China (People's Republic), ⁴Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, China (People's Republic), ⁵The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China (People's Republic), ⁶the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China (People's Republic), ⁷Affiliated Hospital of Hebei University, Baoding, Hebei, China (People's Republic), ⁸The Third Affiliated Hospital of Hebei Medical University, Shijiazhuang, Hebei, China (People's Republic), ⁹Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, China (People's Republic), ¹⁰The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China (People's Republic), ¹¹Second Hospital of Lanzhou University, Lanzhou, Gansu, China (People's Republic), ¹²Shanghai Gothic Internet Technology Co., Ltd., Shanghai, Shanghai, China (People's Republic), ¹³Shanghai Gothic Internet Technology Co., Ltd., Shanghai, China (People's Republic)
Background/Purpose: Flare, relapse from status of treat-to-target (T2T, DAS28< =3.2), is hard predicted. We try to make it predictable by applying machine learning to a…

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