%0 Journal Article
%T Diagnosing and characterizing inflammatory myopathies at an Australian tertiary public hospital: Resource utilization and direct healthcare costs.
%A Huang V
%A Ciciriello S
%A Nikpour M
%A Oon S
%A Day J
%J Int J Rheum Dis
%V 27
%N 4
%D 2024 Apr
%M 38661316
%F 2.558
%R 10.1111/1756-185X.15153
%X OBJECTIVE: To determine the direct health service costs and resource utilization associated with diagnosing and characterizing idiopathic inflammatory myopathies (IIMs), and to assess for limitations and diagnostic delay in current practice.
METHODS: A retrospective, single-center cohort analysis of all patients diagnosed with IIMs between January 2012 and December 2021 in a large tertiary public hospital was conducted. Demographics, resource utilization and costs associated with diagnosing IIM and characterizing disease manifestations were identified using the hospital's electronic medical record and Health Intelligence Unit, and the Medicare Benefits Schedule.
RESULTS: Thirty-eight IIM patients were identified. IIM subtypes included dermatomyositis (34.2%), inclusion body myositis (18.4%), immune-mediated necrotizing myopathy (18.4%), polymyositis (15.8%), and anti-synthetase syndrome (13.2%). The median time from symptom onset to diagnosis was 212 days (IQR: 118-722), while the median time from hospital presentation to diagnosis was 30 days (8-120). Seventy-six percent of patients required emergent hospitalization during their diagnosis, with a median length of stay of 8 days (4-15). The average total cost of diagnosing IIM was $15 618 AUD (STD: 11331) per patient. Fifty percent of patients underwent both MRI and EMG to identify affected muscles, 10% underwent both pan-CT and PET-CT for malignancy detection, and 5% underwent both open surgical and percutaneous muscle biopsies. Autoimmune serology was unnecessarily repeated in 37% of patients.
CONCLUSIONS: The diagnosis of IIMs requires substantial and costly resource use; however, our study has identified potential limitations in current practice and highlighted the need for streamlined diagnostic algorithms to improve patient outcomes and reduce healthcare-related economic burden.