Development and validation of case‐finding algorithms for the identification of patients with anti‐neutrophil cytoplasmic antibody‐associated vasculitis in large healthcare administrative databases. Issue 12 (1st November 2016)

• Record Type: Journal Article
• Authors: Sreih, Antoine G.; Annapureddy, Narender; Springer, Jason; Casey, George; Byram, Kevin; Cruz, Andy; Estephan, Maya; Frangiosa, Vince; George, Michael D.; Liu, Mei; Parker, Adam; Sangani, Sapna; Sharim, Rebecca; Merkel, Peter A.
• Is Part Of: Pharmacoepidemiology and drug safety. Volume 25:Issue 12(2016)
• Page Start: 1368
• Page End: 1374
• Publication Date: 2016-11-01
• Subjects: ANCA -- granulomatosis with polyangiitis -- eosinophilic granulomatosis with polyangiitis -- microscopic polyangiitis -- computable phenotypes -- vasculitis -- pharmacoepidemiology; Pharmacoepidemiology -- Periodicals; Chemotherapy -- Periodicals; Epidemiology -- Periodicals; 615.705
• DOI: 10.1002/pds.4116
• Languages: English
• ISSNs: 1053-8569

Abstract

Abstract: Purpose: The aim of this study was to develop and validate case‐finding algorithms for granulomatosis with polyangiitis (Wegener's, GPA), microscopic polyangiitis (MPA), and eosinophilic GPA (Churg–Strauss, EGPA). Methods: Two hundred fifty patients per disease were randomly selected from two large healthcare systems using the International Classification of Diseases version 9 (ICD9) codes for GPA/EGPA (446.4) and MPA (446.0). Sixteen case‐finding algorithms were constructed using a combination of ICD9 code, encounter type (inpatient or outpatient), physician specialty, use of immunosuppressive medications, and the anti‐neutrophil cytoplasmic antibody type. Algorithms with the highest average positive predictive value (PPV) were validated in a third healthcare system. Results: An algorithm excluding patients with eosinophilia or asthma and including the encounter type and physician specialty had the highest PPV for GPA (92.4%). An algorithm including patients with eosinophilia and asthma and the physician specialty had the highest PPV for EGPA (100%). An algorithm including patients with one of the diagnoses (alveolar hemorrhage, interstitial lung disease, glomerulonephritis, and acute or chronic kidney disease), encounter type, physician specialty, and immunosuppressive medications had the highest PPV for MPA (76.2%). When validated in a third healthcare system, these algorithms had high PPV (85.9% for GPA, 85.7% for EGPA, and 61.5% for MPA). Adding the anti‐neutrophil cytoplasmic antibody type increased the PPV to 94.4%, 100%, and 81.2% for GPA, EGPA, and MPA, respectively. Conclusion: Case‐finding algorithms accurately identify patients with GPA, EGPA, and MPA in administrative databases. These algorithms can be used to assemble population‐based cohorts and facilitate future research in epidemiology, drug safety, and comparative effectiveness. Copyright © 2016 John Wiley & Sons, Ltd.