ArticleACR open rheumatology2022
Derivation and Validation of Algorithms to Identify Patients With Immunoglobulin-G4-Related Disease Using Administrative Claims Data.
Article in ACR open rheumatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Article
- IgG4-Related Disease: A Review of Persistent Challenges in the Pathogenesis, Diagnosis, and Approaches to Treatment.Medical science monitor : international medical journal of experimental and clinical research · 2025Review
- Burden of Glucocorticoid Use and Risk of Toxicities Among Patients with Immunoglobulin-G4-Related Disease: A Retrospective US-Based Claims Study.Rheumatology and therapy · 2025Article
- The clinical outcomes and healthcare resource utilization in IgG4-related disease: a claims-based analysis of commercially insured adults in the United States.Rheumatology (Oxford, England) · 2024Article
- Current and future advances in practice: IgG4-related disease.Rheumatology advances in practice · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectiveImmunoglobulin-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.
methodsPotential 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.
resultsWe 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.
conclusionWe 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.
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.