Antoine G Sreih1, Narender Annapureddy2, Jason Springer3, George Casey4, Kevin Byram2, Andy Cruz5, Maya Estephan3, Vince Frangiosa5, Michael D George1, Mei Liu6, Adam Parker3, Sapna Sangani1, Rebecca Sharim1, Peter A Merkel1,7. 1. Vasculitis Center, Division of Rheumatology, University of Pennsylvania, Philadelphia, PA, USA. 2. Division of Rheumatology, Vanderbilt University Medical Center, Nashville, TN, USA. 3. Division of Rheumatology, University of Kansas Medical Center, Kansas City, KS, USA. 4. The Vasculitis Foundation, Kansas City, MO, USA. 5. Penn Medicine Academic Computing Services, University of Pennsylvania, Philadelphia, PA, USA. 6. Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA. 7. Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA.
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.
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.
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