Derivation and Validation of Algorithms to Identify Patients With Immunoglobulin‐G4‐Related Disease Using Administrative Claims Data. Issue 4 (26th January 2022)

• Record Type: Journal Article
• Authors: Wallace, Zachary S.; Fu, Xiaoqing; Cook, Claire; Perugino, Cory A.; Zhang, Yuqing; Stone, John H.; Choi, Hyon K.
• Is Part Of: ACR open rheumatology. Volume 4:Issue 4(2022)
• Page Start: 371
• Page End: 377
• Publication Date: 2022-01-26
• Subjects: Rheumatology -- Periodicals; 616.723005
• DOI: 10.1002/acr2.11405
• Languages: English
• ISSNs: 2578-5745

Abstract

Abstract : Objective: Immunoglobulin‐G4‐related disease (IgG4‐RD) is a systemic autoimmune disease that can affect nearly any organ, but its epidemiology remains poorly understood. Validated algorithms to identify cases in claims data will enable studies to describe IgG4‐RD epidemiology in the general population. Methods: Potential claims‐based algorithms were developed by IgG4‐RD experts using a combination of International Classification of Diseases, Ninth Revision (ICD‐9) and International Classification of Diseases, 10th Revision (ICD‐10) codes, dispensed medications, and procedure codes for immunoglobulin G (IgG) subclass testing. Algorithms were tested using Medicare Parts A, B, and D linked to medical records (2007‐2017). Classification of cases as IgG4‐RD was determined using the American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR) classification criteria for IgG4‐RD. We estimated the positive predictive value (PPV) of each algorithm; sensitivity was determined using a cohort of patients with IgG4‐RD also enrolled in Medicare Parts A, B, and D during the study period. Results: We identified seven algorithms that used a combination of ICD‐9 and ICD‐10 codes, medication prescriptions, and/or IgG subclass tests to identify patients with IgG4‐RD. The PPV of algorithms in the derivation cohort ranged from 57% to 100%, and sensitivity ranged from 0% to 58%. The best performing algorithm in the validation cohort had a PPV of 81% and a sensitivity of 58%. Typical IgG4‐RD manifestations were observed in the cohort (n = 36) assembled by this algorithm, including 50% with sialadenitis, 64% with pancreatic disease, 31% with renal disease, and 59% with an elevated IgG4 concentration. Conclusion: We derived and validated a well‐performing algorithm to identify IgG4‐RD cases with typical manifestations of the disease. The claims‐based algorithm can be used in research studies of IgG4‐RD.