Efficacy of Risankizumab across distinct PsA phenotypes identified with machine learning analytics using data from biologic DMARD-Naive patients in two phase 3 clinical trials.

ABSTRACT:

Background

The development of personalized approaches in psoriatic arthritis (PsA) is challenging due to unclear patient phenotypes and trajectories. Machine learning (ML) could help to identify homogeneous patient groups. The objective of this analysis was to classify patients with PsA into distinct phenotypes using ML.

Methods

A post hoc analysis of PsA patients treated with risankizumab for ≤ 4 years (196 weeks) from KEEPsAKE 1 and KEEPsAKE 2. The phenotypes were based on baseline demographics and clinical characteristics using unsupervised ML (a finite mixture model). Response to risankizumab at 4 years was defined as minimal disease activity (MDA) and Disease Activity in PsA (DAPSA) low disease activity (LDA).

Results

A total of 1119 patients were classified into

SUBMITTER: Gossec L 

PROVIDER: S-EPMC12771703 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature