Derivation and Validation of Algorithms to Identify Patients With Immunoglobulin-G4-Related Disease Using Administrative Claims Data

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.