The accuracy of administrative health data for identifying patients with rheumatoid arthritis: a retrospective validation study using medical records in Western Australia

Abstract

The use of administrative health datasets is increasingly important for research on disease trends and outcome. The Western Australian (WA) Rheumatic Disease Epidemiological Registry contains longitudinal health data for over 10,000 patients with rheumatoid arthritis (RA). Accurate coding for RA is essential to the validity of this dataset. Investigate the diagnostic accuracy of International Classification of Diseases (ICD)-based discharge codes for RA at WA's largest tertiary hospital. Medical records for a sample of randomly selected patients with ICD-10 codes (M05.00-M06.99) in the hospital discharge database between 2008 and 2020 were retrospectively reviewed. Rheumatologist-reported diagnoses and ACR/EULAR classification criteria were used as reference standards to determine accuracy measures. Medical chart review was completed for 87 patients (mean (± SD) age 64.7 ± 17.2 years), 67.8% female). A total of 80 (91.9%) patients had specialist confirmed RA diagnosis, while seven patients (8%) had alternate clinical diagnoses. Among 87 patients, 69 patients (79.3%) were fulfilled ACR/EULAR classification criteria. The agreement between the reference standards was moderate (Kappa 0.41). Based on rheumatologist-reported diagnoses and ACR/EULAR classification criteria, primary diagnostic codes for RA alone had a sensitivity of (90% vs 89.8%), and PPV (90.9% vs 63.6%), respectively. A combination of a diagnostic RA code with biologic infusion codes in two or more codes increased the PPV to 97.9%. Hospital discharge diagnostic codes in WA identify RA patients with a high degree of accuracy. Combining a primary diagnostic code for RA with biological infusion codes can further increase the PPV.

Keywords: Arthritis; Data accuracy; Epidemiologic research design; Predictive value of tests; Rheumatoid; Routinely collected health data.