Identification of PsA phenotypes with machine learning analytics using data from two phase III clinical trials of guselkumab in a bio-naïve population of patients with PsA

Pascal Richette, Marijn Vis, Sarah Ohrndorf, William Tillett, Julio Ramírez, Marlies Neuhold, Michel van Speybroeck, Elke Theander, Wim Noel, Miriam Zimmermann, May Shawi, Alexa Kollmeier, Alen Zabotti

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)

Abstract

OBJECTIVES: Psoriatic arthritis (PsA) phenotypes are typically defined by their clinical components, which may not reflect patients' overlapping symptoms. This post hoc analysis aimed to identify hypothesis-free PsA phenotype clusters using machine learning to analyse data from the phase III DISCOVER-1/DISCOVER-2 clinical trials.

METHODS: Pooled data from bio-naïve patients with active PsA receiving guselkumab 100 mg every 8/4 weeks were retrospectively analysed. Non-negative matrix factorisation was applied as an unsupervised machine learning technique to identify PsA phenotype clusters; baseline patient characteristics and clinical observations were input features. Minimal disease activity (MDA), disease activity index for psoriatic arthritis (DAPSA) low disease activity (LDA) and DAPSA remission at weeks 24 and 52 were evaluated.

RESULTS: Eight clusters (n=661) were identified: cluster 1 (feet dominant), cluster 2 (male, overweight, psoriasis dominant), cluster 3 (hand dominant), cluster 4 (dactylitis dominant), cluster 5 (enthesitis, large joints), cluster 6 (enthesitis, small joints), cluster 7 (axial dominant) and cluster 8 (female, obese, large joints). At week 24, MDA response was highest in cluster 2 and lowest in clusters 3, 5 and 6; at week 52, it was highest in cluster 2 and lowest in cluster 5. At weeks 24 and 52, DAPSA LDA and remission were highest in cluster 2 and lowest in clusters 4 and 6, respectively. All clusters improved with guselkumab treatment over 52 weeks.

CONCLUSIONS: Unsupervised machine learning identified eight PsA phenotype clusters with significant differences in demographics, clinical features and treatment responses. In the future, such data could help support individualised treatment decisions.

Original languageEnglish
Article numbere002934
JournalRMD Open
Volume9
Issue number1
Early online date31 Mar 2023
DOIs
Publication statusPublished - 31 Mar 2023

Bibliographical note

Funding Information:
The authors would like to thank OPEN Health Communications for providing medical writing support, which was funded by Janssen-Cilag Ltd. This work was first presented as a poster at ACR 2021, and encored at EULAR 2022, SIR 2022 and SER 2022.

Data availability statement
No data are available. Not applicable.

Keywords

  • Male
  • Humans
  • Female
  • Arthritis, Psoriatic/diagnosis
  • Treatment Outcome
  • Retrospective Studies
  • Severity of Illness Index
  • Phenotype
  • Machine Learning

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