ArticleACR open rheumatology2021
Identification of Acute Giant Cell Arteritis in Real-World Data Using Administrative Claims-Based Algorithms.
Article in ACR open rheumatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Pooled it
- Real-World Analysis of Patient Characteristics, Adverse Events, and Economic Burden Among Patients with Giant Cell Arteritis Treated with Glucocorticoids.Rheumatology and therapy · 2025Article
- Association of SSRI and SNRI use with incidence of cardiovascular events in veterans with giant cell arteritis and polymyalgia rheumatica.Frontiers in immunology · 2025Article
- Giant cell arteritis treatment patterns and rates of serious infections.Clinical and experimental rheumatology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
objectiveThe 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.
methodsWe 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.
resultsThe 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.
conclusionA 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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.