139. UTILIZATION OF CANADIAN HEALTHCARE ADMINISTRATIVE DATABASES TO ACCURATELY IDENTIFY CASES OF ANCA-ASSOCIATED VASCULITIS

Background: ANCA-associated vasculitis (AAV) is a group of diseases that include granulomatosis with polyangiitis (GPA), eosinophilic granulomatosis with polyangiitis (EGPA) and microscopic polyangiitis (MPA). Given the rarity of these multisystem autoimmune diseases they are difficult to study. Large healthcare administrative databases provide a unique opportunity to address this. In Ontario, Canada, a province with over 14 million people, there is a single provincial healthcare database. Local hospitals assign an ICD10 code to discharges and upload data to this provincial system. To date there has been no validation of the ICD10. Our objective was to validate local coding to determine if searching the provincial system by ICD10 code would result in patients being identified correctly. Methods: Validation at hospital level was done by searching Hamilton Health Sciences and St. Joseph’s Healthcare administrative databases from 2011 to 2017. The databases were searched for: AAV (GPA, MPA or eGPA), arteritis, polyarteritis nodosa, giant cell arteritits, and nephritis. When patients had multiple visits one was selected randomly for review. Two algorithms were developed to identify patients. The first (algorithm one) used ICD10 coding alone. The second (algorithm two) combined ICD10 codes with ANCA serology. Chart review was completed to confirm or refute the diagnosis of AAV. Results: A search of the hospital databases led to 417 unique patients being identified. In the sample 52.0% had AAV (GPA n = 112, MPA n = 88, and eGPA n = 15). Using algorithm one, 113 patients (89%) were coded correctly as having AAV with the algorithm having a sensitivity of 52.6% (CI 46.0-59.2%), specificity of 93.1% (CI 88.6-96.2%), and a positive predictive value (PPV) of 89.0% (CI 82.7-93.2%). Patients who were incorrectly coded were most commonly coded as arteritis (30.7%). Table 1 details the accuracy of algorithm one for each type of AAV. Algorithm two, which included ANCA status, improved sensitivity to 98.6% (CI 96.0-99.7) at the expense of reducing specificity to 85.6% (CI 80.0-90.2%). Conclusion: Our results show that the specificity of the ICD10 coding for AAV is quite high. The sensitivity however is quite low, particularly for identifying patients with MPA. Combining ANCA serology with ICD10 coding improved this. Disclosures: None Accuracy of Coding Using ICD10 CI (Confidence interval)

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