Clinically relevant patient clusters identified by machine learning from the clinical development programme of secukinumab in psoriatic arthritis

Abstract

ObjectivesIdentify distinct clusters of psoriatic arthritis (PsA) patients based on their baseline articular, entheseal and cutaneous disease manifestations and explore their clinical and therapeutic value.MethodsPooled baseline data in PsA patients (n=1894) treated with secukinumab across four phase 3 studies (FUTURE 2–5) were analysed to determine phenotypes based on clusters of clinical indicators. Finite mixture models methodology was applied to generate clinical clusters and mean longitudinal responses were compared between secukinumab doses (300 vs 150 mg) across identified clusters and clinical indicators through week 52 using machine learning (ML) techniques.ResultsSeven distinct patient clusters were identified. Cluster 1 (very-high (VH) – SWO/TEN (swollen/tender); n=187) was characterised by VH polyarticular burden for both tenderness and swelling of joints, while cluster 2 (H (high) – TEN; n=251) was marked by high polyarticular burden in tender joints and cluster 3 (H – Feet – Dactylitis; n=175) by high burden in joints of feet and dactylitis. For cluster 4 (L (Low) – Nails – Skin; n=209), cluster 5 (L – skin; n=283), cluster 6 (L – Nails; n=294) and cluster 7 (L; n=495) articular burden was low but nail and skin involvement was variable, with cluster 7 marked by mild disease activity across all domains. Greater improvements in the longitudinal responses for enthesitis in cluster 2, enthesitis and Psoriasis Area and Severity Index (PASI) in cluster 4 and PASI in cluster 6 were shown for secukinumab 300 mg compared with 150 mg.ConclusionsPsA clusters identified by ML follow variable response trajectories indicating their potential to predict precise impact on patients’ outcomes.Trial registration numbersNCT01752634, NCT01989468, NCT02294227, NCT02404350