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

Zachary S Wallace; Xiaoqing Fu; Claire Cook; Cory A Perugino; Yuqing Zhang; John H Stone; Hyon K Choi

doi:10.1002/acr2.11405

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

ACR Open Rheumatol. 2022 Apr;4(4):371-377. doi: 10.1002/acr2.11405. Epub 2022 Jan 26.

Authors

Zachary S Wallace¹, Xiaoqing Fu², Claire Cook², Cory A Perugino¹, Yuqing Zhang¹, John H Stone¹, Hyon K Choi¹

Affiliations

¹ Massachusetts General Hospital, Boston.
² Harvard Medical School, Boston, Massachusetts.

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.

Abstract

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