TY - JOUR
T1 - Development and validation of case-finding algorithms for the identification of patients with anti-neutrophil cytoplasmic antibody-associated vasculitis in large healthcare administrative databases
AU - for the Vasculitis Patient-Powered Research Network
AU - Sreih, Antoine G.
AU - Annapureddy, Narender
AU - Springer, Jason
AU - Casey, George
AU - Byram, Kevin
AU - Cruz, Andy
AU - Estephan, Maya
AU - Frangiosa, Vince
AU - George, Michael D.
AU - Liu, Mei
AU - Parker, Adam
AU - Sangani, Sapna
AU - Sharim, Rebecca
AU - Merkel, Peter A.
N1 - Funding Information:
The Vasculitis Patient-Powered Research Network is a participant of PCORnet?, the National Patient-Centered Clinical Research Network, an initiative funded by the Patient-Centered Outcomes Research Institute. The Vasculitis Patient-Powered Research Network's participation was funded through Patient-Centered Outcomes Research Institute Awards (PPRN-1306-04758 and 1IP2PI000603). Dataset from the second healthcare system was obtained from Vanderbilt University Medical Center's Synthetic Derivative, which is supported by institutional funding and by the Vanderbilt Clinical and Translational Science Awards (CTSA) grant ULTR000445 from National Center for Advancing Translational Sciences (NCATS)/National Institute of Health (NIH). Dataset from the third healthcare system was obtained from the University of Kansas Medical Center Healthcare Enterprise Repository for Ontological Narration clinical data repository, which is supported by institutional funding and by the University of Kansas Medical Center Clinical and Translational Science Awards (CTSA) grant UL1TR000001 from NCATS/NIH.
Publisher Copyright:
Copyright © 2016 John Wiley & Sons, Ltd.
PY - 2016/12/1
Y1 - 2016/12/1
N2 - 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.
AB - 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.
KW - ANCA
KW - computable phenotypes
KW - eosinophilic granulomatosis with polyangiitis
KW - granulomatosis with polyangiitis
KW - microscopic polyangiitis
KW - pharmacoepidemiology
KW - vasculitis
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U2 - 10.1002/pds.4116
DO - 10.1002/pds.4116
M3 - Article
AN - SCOPUS:84996957274
VL - 25
SP - 1368
EP - 1374
JO - Pharmacoepidemiology and Drug Safety
JF - Pharmacoepidemiology and Drug Safety
SN - 1053-8569
IS - 12
ER -