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.

Abstract

To identify patient clusters based on baseline demographics and clinical indicators. Pooled baseline demographics and clinical data of secukinumab-treated patients from ten Phase III studies in psoriatic arthritis (PsA; FUTURE 1-5 and MAXIMISE), ankylosing spondylitis (AS; MEASURE 1-4), were analysed by machine learning (ML) algorithms. The longitudinal responses of secukinumab 300 mg versus 150 mg were investigated across the clusters and three clinical indicators of tender joints, swollen joints and enthesitis. 3907 patients were grouped into eight distinct clusters based on patient demographics and baseline clinical characteristics. Patients with PsA and axial manifestations (MAXIMISE) were overrepresented in clusters 6-8. Patients in cluster 6 (mean age 48 years; 46% male) were overweight with pronounced psoriasis, higher articular burden in knees, shoulders, elbows and wrists. Patients in cluster 7 (mean age 47 years; 53% male) were less overweight with lower polyarticular joint counts and tenderness of the joints of the feet, wrists and hands. Patients in cluster 8 were predominantly with AS (mean age 43 years; 64% male) with a mean body mass index of 27.3 kg/m2, oligoarthritis and high prevalence of spinal pain. Patients with PsA (FUTURE) were overrepresented in clusters 1-5. Longitudinal analysis showed improvements with secukinumab 300 mg versus 150 mg in clusters 6 and 8 for tender joint counts, and cluster 7 for swollen joint counts. 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.