Clinically relevant patient clusters identified by machine learning from the clinical development programme of secukinumab in psoriatic arthritis
Pournara E, Kormaksson M, Nash P, Ritchlin CT, Kirkham BW, Ligozio G, Pricop L, Ogdie A, Coates LC, Schett G, Mcinnes IB (2021)
Publication Type: Journal article
Publication year: 2021
Journal
Book Volume: 7
Journal Issue: 3
DOI: 10.1136/rmdopen-2021-001845
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.
Authors with CRIS profile
Involved external institutions
Novartis AG
Switzerland (CH)
Griffith University
Australia (AU)
University of Rochester (UR)
United States (USA) (US)
Guy's and St Thomas' (NHS Foundation Trust)
United Kingdom (GB)
University of Pennsylvania
United States (USA) (US)
University of Oxford
United Kingdom (GB)
University of Glasgow
United Kingdom (GB)
Switzerland (CH)
Griffith University
Australia (AU)
University of Rochester (UR)
United States (USA) (US)
Guy's and St Thomas' (NHS Foundation Trust)
United Kingdom (GB)
University of Pennsylvania
United States (USA) (US)
University of Oxford
United Kingdom (GB)
University of Glasgow
United Kingdom (GB)
How to cite
APA:
Pournara, E., Kormaksson, M., Nash, P., Ritchlin, C.T., Kirkham, B.W., Ligozio, G.,... Mcinnes, I.B. (2021). Clinically relevant patient clusters identified by machine learning from the clinical development programme of secukinumab in psoriatic arthritis. RMD Open, 7(3). https://doi.org/10.1136/rmdopen-2021-001845
MLA:
Pournara, Effie, et al. "Clinically relevant patient clusters identified by machine learning from the clinical development programme of secukinumab in psoriatic arthritis." RMD Open 7.3 (2021).
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