RT Journal Article
SR Electronic
T1 Identifying axial spondyloarthritis patients in large datasets: Expanding possibilities for observational research
JF The Journal of Rheumatology
JO J Rheumatol
FD The Journal of Rheumatology
SP jrheum.200570
DO 10.3899/jrheum.200570
A1 Jessica A.  Walsh
A1 Shaobo Pei
A1 Gopi K.  Penmetsa
A1 Rebecca S  Overbury
A1 Daniel O.  Clegg
A1 Brian C.  Sauer
YR 2020
UL http://www.jrheum.org/content/early/2020/08/24/jrheum.200570.abstract
AB Objective Observational research of axial spondyloarthritis (axSpA) is limited by a lack of methods for identifying diverse axSpA phenotypes in large datasets. AxSpA identification algorithms were previously designed to identify a broad spectrum of axSpA patients, 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. Methods 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. Results The mean age was 54.7 and 92% were male. Unweighted F1s (0.59-0.74) were higher than IPW F1s (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). Conclusion 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.