The Frequency of Non-radiographic Axial Spondyloarthritis in Relation to Symptom Duration in Patients Referred Because of Chronic Back Pain: Results from the Berlin Early Spondyloarthritis Clinic
Overview
Authors
Affiliations
Objective: This study was aimed at investigating the frequencies of non-radiographic axial spondyloarthritis (nr-axSpA) and ankylosing spondylitis (AS) diagnoses and their ratios in relation to symptom duration in patients referred because of chronic back pain and suspicion of axial SpA.
Methods: In this monocentre study, orthopaedists and primary care physicians were requested to refer patients with chronic low back pain (duration >3 months) and onset of back pain before 45 years of age to a SpA-specialised rheumatology outpatient clinic for further diagnostic investigation, if proposed screening parameters were present. The ratio of nr-axSpA to AS was analysed in relation to the duration of symptoms.
Results: A diagnosis of definite axial SpA was made in 43.7% of the referred patients (n=522). Axial SpA was diagnosed in a similar percentage of about 50% if back pain duration was <9 years but decreased to 36% if symptom duration was >9 years. Nr-axSpA represented the majority of patients (67.3%) only if duration of back pain was 1 year and less at the time of referral. Between 1 and 6 years of back pain duration the probability of nr-axSpA and AS was nearly equal (1-3 years: 52.5% and 47.5%, respectively; 3-6 years: 53.7% and 46.3%, respectively). In patients with back pain duration of 6-9 years, AS was more likely (61.1%) to be diagnosed than nr-axSpA (38.9%), and this increased further over time.
Conclusions: Non-radiographic axial SpA represents an important differential diagnosis of back pain, especially in patients with recent symptom onset.
Diagnosis, monitoring, and management of axial spondyloarthritis.
Zimba O, Kocyigit B, Korkosz M Rheumatol Int. 2024; 44(8):1395-1407.
PMID: 38758383 PMC: 11222196. DOI: 10.1007/s00296-024-05615-3.
The Development and Validation of an AI Diagnostic Model for Sacroiliitis: A Deep-Learning Approach.
Lee K, Lee R, Lee K, Park W, Kwon S, Lim M Diagnostics (Basel). 2023; 13(24).
PMID: 38132228 PMC: 10743277. DOI: 10.3390/diagnostics13243643.
Mauro D, Forte G, Poddubnyy D, Ciccia F Rheumatol Ther. 2023; 11(1):19-34.
PMID: 38108992 PMC: 10796311. DOI: 10.1007/s40744-023-00627-0.
Kwon O, Park M Ther Adv Musculoskelet Dis. 2022; 14:1759720X211072994.
PMID: 35186125 PMC: 8848089. DOI: 10.1177/1759720X211072994.
Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance.
Bressem K, Vahldiek J, Adams L, Niehues S, Haibel H, Rios Rodriguez V Arthritis Res Ther. 2021; 23(1):106.
PMID: 33832519 PMC: 8028815. DOI: 10.1186/s13075-021-02484-0.