Identifying Patients With Axial Spondyloarthritis in Large Datasets: Expanding Possibilities for Observational Research.

Abstract

Observational research of axial spondyloarthritis (axSpA) is limited by a lack of methods for identifying diverse axSpA phenotypes in large datasets. Algorithms were previously designed to identify a broad spectrum of patients with axSpA, including patients not identifiable with diagnosis codes. The study objective was to estimate the performance of axSpA identification methods in the general Veterans Affairs (VA) population. A patient sample with known axSpA status (n = 300) was established with chart review. For feasibility, this sample was enriched with veterans with axSpA risk factors. Algorithm performance outcomes included sensitivities, positive predictive values (PPV), and F1 scores (an overall performance metric combining sensitivity and PPV). Performance was estimated with unweighted outcomes for the axSpA-enriched sample and inverse probability weighted (IPW) outcomes for the general VA population. These outcomes were also assessed for traditional identification methods using diagnosis codes for the ankylosing spondylitis (AS) subtype of axSpA. The mean age was 54.7 and 92% were male. Unweighted F1 scores (0.59-0.74) were higher than IPW F1 scores (0.48-0.65). The full algorithm had the best overall performance (F1IPW 0.65). The Early Algorithm was the most inclusive (sensitivityIPW 0.90, PPVIPW 0.38). The traditional method using ≥ 2 AS diagnosis codes from rheumatology had the highest PPV (PPVIPW 0.84, sensitivityIPW 0.34). The axSpA identification methods demonstrated a range of performance attributes in the general VA population that may be appropriate for various types of studies. The novel identification algorithms may expand the scope of research by enabling identification of more diverse axSpA populations.