Our long-term goal is to improve health outcomes in patients with gout, the most common inflammatory arthritis in adult men. Inability to achieve a target serum urate of <6 mg/dl is common in gout patients undergoing treatment with urate-lowering therapy, which is associated with more acute gout flares, higher societal costs, and diminished quality of life. High-quality national studies of gout have been limited by the lack of availability of serum urate results in large national databases and use of only ICD-9 codes to identify gout cohorts, and when used alone ICD-9 codes may be inaccurate. Innovations in Veterans Affairs (VA) informatics including Natural language processing (NLP) algorithms, ability to obtain serum urate in national VA databases and to merge inpatient, outpatient and pharmacy databases allow conduct of high quality studies in gout. Data regarding patient, physician and healthcare factors influencing the ability to achieve target serum urate <6 mg/dl are lacking. Major adverse events (AEs) such as allopurinol hypersensitivity syndrome (AHS) and colchicine-associated neuromyopathy and hematologic AEs are associated with significant morbidity and mortality and have not been studied with well-designed, adequately-powered studies The main objective of this proposal is to study the effectiveness an safety of gout medications using national VA databases. Our project is innovative in using NLP algorithm to develop a large valid national cohort of gout patients, extracting serum urate levels and validating and studying AEs in a large gout cohort.
Specific Aim 1 : To identify key patient, provider, and health system factors associated with achieving and maintaining serum urate below 6 mg/dl ("target") in gout patients taking allopurinol.
Specific Aim 2 : To characterize the epidemiology and risk factors for major adverse events (AEs) associated with the use of allopurinol and colchicine for treatment of gout. We will use natural language processing (NLP) algorithm that incorporates the rich information from national VA EHR (text field data from clinic notes) and nationally available laboratory results (serum urate, synovial fluid result) in addition to lCD-9 codes to more accurately identify gout patients. With a similar approach, we will identify a validated cohort of gout patients with major AEs. We will examine association of key (patient, physician, healthcare) factors with ability to achieve target serum urate and with risk of major AEs.

Public Health Relevance

This translational research will improve our understanding of clinical and healthcare factors affecting optimal serum urate lowering in patients with gout receiving treatment with allopurinol, which is associated with improved quality of life, less activity limitation, lower risk of acute attacks and less health care costs. We will also fill the knowledge gap related to the frequency of major AEs and identify key factors associated with increased risk of AEs. Knowledge gained from this study will lead to improved health outcomes in gout.

National Institute of Health (NIH)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Specialized Center (P50)
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Special Emphasis Panel (ZAR1-KM (M1))
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University of Alabama Birmingham
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Taylor, William J; Redden, David; Dalbeth, Nicola et al. (2014) Application of the OMERACT filter to measures of core outcome domains in recent clinical studies of acute gout. J Rheumatol 41:574-80
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