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

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 lon...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1136/rmdopen-2021-001845

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More by this author
Role:
Author
ORCID:
0000-0002-2571-788X
More by this author
Role:
Author
ORCID:
0000-0002-2602-1219
Publisher:
BMJ Publishing Group
Journal:
RMD Open More from this journal
Volume:
7
Issue:
3
Article number:
e001845
Publication date:
2021-11-18
Acceptance date:
2021-10-29
DOI:
EISSN:
2056-5933
Pmid:
34795065
Language:
English
Keywords:
Pubs id:
1210787
Local pid:
pubs:1210787
Deposit date:
2021-12-02

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