Objectives The objectives of this analysis were to develop an algorithm to identify microscopic polyangiitis (MPA) in administrative claims data based on experts' input, as there is no specific ICD-9-CM diagnosis code for MPA, and to describe the clinical and economic burden of patients with MPA.
Methods The analysis utilized Truven Health Analytics' MarketScan® Commercial and Medicare Supplemental administrative claims databases. Patients with ≥2 claims with a diagnosis of ICD-9-CM 447.6 arteritis, unspecified were identified between 7/1/2010 and 6/30/2012. Patients were required to be ≥18 years old at first diagnosis, have continuous enrollment for ±6 months around first diagnosis (pre/peri-period and post-period), have no claims with an arteritis (447.6) diagnosis in the pre/peri-period and have no claims with a granulomatosis with polyangiitis (GPA) diagnosis (ICD-9-CM 446.4), hepatitis B diagnosis or hepatitis C diagnosis during the pre/peri-period or post-period. Lastly, to increase the likelihood of capturing true MPA patients, patients were required to have a claim with a diagnosis for renal failure, glomerulonephritis or hemoptysis during the pre/peri-period or post-period. Clinical characteristics, specifically major relapse-associated conditions informed by the Birmingham Vasculitis Activity Score, were identified based on the presence of a relevant diagnosis on claims. All-cause healthcare resource utilization and costs were measured for the following categories: inpatient admissions, emergency room visits, outpatient office visits/outpatient services and outpatient prescription medications. Costs are presented in 2013 US$.
Results There were 8390 patients with ≥2 claims with a diagnosis of arteritis, unspecified. After applying the inclusion and exclusion criteria, the final sample was 612 newly diagnosed MPA patients. The average age at diagnosis was 62 years (standard deviation [SD] 16) and 38% of patients were male). Results are presented in Table 1. Patients had evidence of major relapse-associated conditions before and after MPA diagnosis. All-cause healthcare costs nearly doubled following an MPA diagnosis, with costs related to inpatient admissions being the main driver before and after diagnosis.
Conclusions Patients with MPA were identified in these databases using a new algorithm, developed via clinical input, based on the presence of a non-specific diagnosis code and diagnoses of additional conditions associated with more severe disease. Receiving a diagnosis of MPA was associated with an increase in all-cause healthcare resource utilization and costs, primarily attributable to an increase in costs of inpatient admissions. Future studies are needed to explore whether similar trends are seen in other countries.
Disclosure of Interest K. Raimundo Employee of: Genentech, Inc., A. Farr Employee of: Truven Health Analytics which performs consulting work for pharmaceutical companies, including Genentech, G. Kim Employee of: Truven Health Analytics which performs consulting work for pharmaceutical companies, including Genentech, G. Duna Employee of: Genentech, Inc.