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 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 (swoll

SUBMITTER: Pournara E 

PROVIDER: S-EPMC8603280 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature