Abstract
BackgroundThis study aimed to classify the distinct group of patients with axial spondyloarthritis (SpA) on tumour necrosis factor alpha inhibitors (TNFi) according to the baseline characteristics using a clustering algorithm.MethodsThe clinical characteristics and demographic data of patients with axial SpA included in the Korean College of Rheumatology Biologics and Targeted Therapy registry were investigated. The patterns of disease manifestations were examined using divisive hierarchical cluster analysis. After clustering, we compared the clinical characteristics of patients and the drug survival of TNFi between the classified groups.ResultsA total of 1042 patients were analysed. The cluster analysis classified patients into two groups: axial group predominantly showing isolated axial manifestations (n = 828) and extra-axial group more frequently showing extra-axial symptoms (n = 214). Almost all extra-axial symptoms (peripheral arthritis, enthesitis, uveitis, and psoriasis) were more frequently observed in the extra-axial group than in the axial group. Moreover, patients in the extra-axial group had shorter disease duration, later disease onset, and higher disease activity than those in the axial group. The disease activity was comparable between the two groups after 1 year of treatment with TNFi. Interestingly, the extra-axial group had a lower drug survival with TNFi than the axial group (p = 0.001).ConclusionsCluster analysis of patients with axial SpA using TNFi classified two distinct clinical phenotypes. These clusters had different TNFi drug survival, clinical characteristics, and disease activity.
Highlights
This study aimed to classify the distinct group of patients with axial spondyloarthritis (SpA) on tumour necrosis factor alpha inhibitors (TNFi) according to the baseline characteristics using a clustering algorithm
The following variables were selected for both multiple correspondence analysis (MCA) and cluster analysis: sex, age at disease onset (< 40 or ≥ 40 years), duration of disease symptoms (< 2 or ≥ 2 years), human leucocyte antigen (HLA)-B27 positivity, inflammatory back pain, sacroiliitis detected on radiography according to the modified New York Criteria, peripheral arthritis, enthesitis, uveitis, psoriasis, inflammatory bowel disease (IBD), and response to non-steroidal anti-inflammatory drugs (NSAIDs)
Because previous studies have reported the differences in clinical features depending on radiographic changes and the presence or absence of HLA-B27, we investigated the correlation of the aforementioned variables with the divided clusters
Summary
This study aimed to classify the distinct group of patients with axial spondyloarthritis (SpA) on tumour necrosis factor alpha inhibitors (TNFi) according to the baseline characteristics using a clustering algorithm. In view of the fact that the prognosis differs according to the disease phenotype, attempts have been made to classify patients into subgroups based on SpA features and demographic characteristics. As the relationships between clinical variables and phenotypes are not linear but rather complex, machine learning methods have become increasingly popular tools for classifying patients into subgroups [9,10,11,12]. The representative unsupervised learning methods include hierarchical clustering and k-means clustering. These methods have been implemented to divide patients with SpA into subgroups using various clinical information. Previous studies have described the differences in disease activity [14, 15], radiographic progression [16], and functional assessment [17] between the subgroups classified according to distinctive features. Unsupervised learning is a useful tool for classifying patients with SpA
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