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

Pournara, E. et al. (2021) Clinically relevant patient clusters identified by machine learning from the clinical development programme of secukinumab in psoriatic arthritis. RMD Open, 7(3), e001845. (doi: 10.1136/rmdopen-2021-001845)

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Abstract

Objectives: Identify distinct clusters of psoriatic arthritis (PsA) patients based on their baseline articular, entheseal and cutaneous disease manifestations and explore their clinical and therapeutic value. Methods: Pooled 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. Results: Seven 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. Conclusions: PsA clusters identified by ML follow variable response trajectories indicating their potential to predict precise impact on patients’ outcomes. Trial registration numbers: NCT01752634, NCT01989468, NCT02294227, NCT02404350.

Item Type:Articles
Additional Information:The research study was funded by Novartis Pharma AG, Basel, Switzerland.
Keywords:Psoriatic arthritis, 1506, biological therapy, arthritis, psoriatic, tumor necrosis factor inhibitors, inflammation, t-lymphocyte subsets.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:McInnes, Professor Iain
Authors: Pournara, E., Kormaksson, M., Nash, P., Ritchlin, C. T., Kirkham, B. W., Ligozio, G., Pricop, L., Ogdie, A., Coates, L. C., Schett, G., and McInnes, I. B.
College/School:College of Medical Veterinary and Life Sciences > School of Infection & Immunity
Research Centre:College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Immunobiology
Journal Name:RMD Open
Publisher:BMJ Publishing Group
ISSN:2056-5933
ISSN (Online):2056-5933
Published Online:18 November 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in RMD Open 7(3): e001845
Publisher Policy:Reproduced under a Creative Commons License

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