Autoantibody status according to multiparametric assay accurately estimates connective tissue disease classification and identifies clinically relevant disease clusters
Authors' conclusion
Extended multiparametric autoantibody assay allowed an accurate classification of CTD patients, independently of clinical features. Clustering according to autoantibody titres is able to identify clusters of CTD subjects with similar clinical features, independently of their final diagnosis.
Study context
- Population
- Patients with systemic lupus erythematosus (SLE), systemic sclerosis (SSc), Sjogren's syndrome (SjS), undifferentiated connective tissue disease (UCTD), idiopathic inflammatory myopathies (IIM) and healthy controls
- Participants
- 908 patients
- Outcome
- Classification accuracy of multiparametric autoantibody assay for connective tissue disease (CTDs) and healthy controls (HC)
Methodological detailsXGBoost classification model, hierarchical clustering
- XGBoost classification model
- Hierarchical clustering
Performance
As reported in the abstract
Classification of CTD patients and HC by XGBoost model based on autoantibody titres
Mean accuracy
60.84±4.05%
Mean AUC
88.99±2.50%
Further findings
Hierarchical clustering by autoantibody titres
- Four clusters identified
- 4
- CL1: patients with typical features of SLE
- CL2: most patients with SjS, plus some SLE and UCTD patients with SjS-like features
- CL3: largest and most heterogeneous, all remaining subjects, low titre or lower-prevalence autoantibodies
- CL4: anti-Jo1 patients only
Original publication
Autoantibody status according to multiparametric assay accurately estimates connective tissue disease classification and identifies clinically relevant disease clusters. RMD Open. Published 21 September 2023. doi:10.
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