Identification of Acute Giant Cell Arteritis in Real-World Data Using Administrative Claims-Based Algorithms

Abstract

Objective: The objective of this study was to validate claims-based algorithms for identifying acute giant cell arteritis (GCA) that will help generate real-world evidence on comparative effectiveness research and epidemiologic studies. Among patients identified by the GCA algorithm, we further investigated whether GCA flares could be detected by using claims data.

Methods: We developed five claims-based algorithms based on a combination of International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes, specialist visits, and dispensed medications using Medicare Parts A, B, and D linked to electronic medical records (2006-2014). Acute cases of GCA were determined by chart review using the treating physician's diagnosis of GCA as the gold standard. Among the patients identified with acute GCA, we assessed if a GCA flare occurred during the year after initial diagnosis.

Results: The number of patients identified by each algorithm ranged from 220 to 896. Positive predictive values (PPVs) of the algorithms ranged from 60.7% to 84.8%. Requirement for disease-specific workups, multiple diagnosis codes, or specialist visits improved the PPVs. The highest PPV (84.8%) was noted in an algorithm that required two or more diagnosis codes of GCA from inpatient, emergency department, or outpatient rheumatology visits plus a prednisone-equivalent dose greater than or equal to 40 mg/day occurring 14 days before or after the second ICD-9 diagnosis date, with the cumulative days' supply greater than or equal to 14 days. Among patients identified as having GCA, 18.2% of patients had definite evidence of a flare and 25% had a potential flare.

Conclusion: A claims-based algorithm requiring two or more ICD-9 diagnosis codes from inpatient, emergency department, or outpatient rheumatology visits and high-dose glucocorticoid dispensing can be a useful tool to identify acute GCA cases in large administrative claims databases.