HLA-B27 Testing in Clinical Practice: A Retrospective Analysis of Testing Indications and Rheumatology Referral Patterns
Bibliographic record
Abstract
Objective The HLA-B27 allele is strongly associated with spondyloarthritis (SpA). HLA-B27 is included in SpA classification criteria and referral strategies for axial SpA. Investigations of HLA-B27 testing in usual clinical practice are limited. Methods We identified all adult patients tested for HLA-B27 from January 1, 2022, to December 31, 2022, in the Mass General Brigham healthcare system. We examined patient demographics; ordering provider specialty; testing indication; concurrent testing with antinuclear antibodies (ANA), rheumatoid factor, and/or anticyclic citrullinated peptide autoantibodies; and rheumatology referral. We compared the rate of rheumatology referral between HLA-B27–positive and HLA-B27–negative patients. Results HLA-B27 tests were ordered for 1960 patients (62.4% female; average age: 47.4 yrs). The most common specialties testing HLA-B27 were rheumatology (39.7%) and ophthalmology (21.4%). The most common indications for HLA-B27 testing were peripheral arthritis (33%), uveitis (22%), and back pain (16.7%). The majority of HLA-B27 tests (69.3%) were ordered concurrently with other autoantibody tests. A total of 11% of tested patients were HLA-B27 positive. Ophthalmology had the highest positive rate (15.4%), whereas reactive arthritis was the indication with the highest positive test rate (50%). A greater proportion of HLA-B27–positive patients were referred to rheumatology (53% vs 32%; P = 0.002). Conclusion HLA-B27 testing was frequently performed by rheumatologists and nonrheumatologists for a broad spectrum of indications. Cotesting HLA-B27 with ANA and rheumatoid arthritis autoantibodies was common. Nearly half of HLA-B27–positive patients were not referred to rheumatology. Further efforts are needed to promote judicious use of HLA-B27 testing and optimize referral pathways to rheumatology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".