• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Journal articles
    • View Item
    • Home
    • Griffith Research Online
    • Journal articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

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

    View/Open
    Nash524931-Published.pdf (2.036Mb)
    File version
    Version of Record (VoR)
    Author(s)
    Pournara, Effie
    Kormaksson, Matthias
    Nash, Peter
    Ritchlin, Christopher T
    Kirkham, Bruce W
    Ligozio, Gregory
    Pricop, Luminita
    Ogdie, Alexis
    Coates, Laura C
    Schett, Georg
    McInnes, Iain B
    Griffith University Author(s)
    Nash, Peter
    Year published
    2021
    Metadata
    Show full item record
    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 ...
    View more >
    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.
    View less >
    Journal Title
    RMD Open
    Volume
    7
    Issue
    3
    DOI
    https://doi.org/10.1136/rmdopen-2021-001845
    Copyright Statement
    © Author(s) (or their employer(s)) 2021. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
    Subject
    Clinical sciences
    Science & Technology
    Life Sciences & Biomedicine
    Rheumatology
    arthritis
    biological therapy
    Publication URI
    http://hdl.handle.net/10072/410888
    Collection
    • Journal articles

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander