Spondyloarthritis & psoriatic arthritis
Patient clusters identified by machine learning from a pooled analysis of the clinical development programme of secukinumab in psoriatic arthritis, ankylosing spondylitis and psoriatic arthritis with axial manifestations
Authors' conclusion
PsA clusters obtained by ML in pooled dataset indicate phenotypical heterogeneity of patients with PsA and axial manifestations and overlapping features across the spondyloarthritis spectrum.
Study context
- Population
- Secukinumab-treated patients from ten phase III studies in psoriatic arthritis (PsA) (FUTURE 1–5, MAXIMISE) and ankylosing spondylitis (AS) (MEASURE 1–4)
- Participants
- 3,907 patients10 phase III studies
- Comparison
- Secukinumab 300 mg vs 150 mg across patient clusters
- Outcome
- Patient clusters based on baseline demographics and clinical indicators
- Follow-up
- Baseline
Methodological detailspooled baseline data, machine learning clustering, longitudinal response analysis
- Pooled baseline data
- Machine learning clustering
- Longitudinal response analysis
Pooled result
As reported in the abstract
Patient clusters by machine learning (ML)
Eight distinct clusters identified
8
Further pooled results
Longitudinal response, secukinumab 300 mg vs 150 mg, by cluster
- Improvements with 300 mg vs 150 mg in clusters 6 and 8 for tender joint counts
- Improvements with 300 mg vs 150 mg in cluster 7 for swollen joint counts
Original publication
Patient clusters identified by machine learning from a pooled analysis of the clinical development programme of secukinumab in psoriatic arthritis, ankylosing spondylitis and psoriatic arthritis with axial manifestations. Clin Exp Rheumatol. Published 28 June 2023. doi:10.
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