Spondyloarthritis & psoriatic arthritis
Predicting Treatment Outcomes in Patients With Psoriatic Arthritis or Axial Spondyloarthritis: An Artificial Intelligence–Driven Approach
Authors' conclusion
XAI provides significant value by enabling explanations of individual patient predictions and their visualizations.
Study context
- Population
- Patients with active PsA or axial spondyloarthritis (axSpA) treated with secukinumab (SEC) in Germany (AQUILA study)
- Participants
- 1,961 patients
- Comparison
- PsA vs axSpA
- Outcome
- Baseline prediction of achieving LDA and high HRQOL; main predictors and their direction of influence
- Follow-up
- Week 16
Methodological detailsmulticenter, noninterventional, binary machine learning (ML) algorithms, explainable artificial intelligence (XAI)
- Multicenter
- Noninterventional
- Binary ML algorithms
- Explainable artificial intelligence (XAI)
Performance
As reported in the abstract
- PsA, low disease activity (LDA) predictors: PGA, physician global assessment, pretreatment with biologic disease-modifying antirheumatic drugs (bDMARDs), tender joint count (TJC), age
- PsA, high health-related quality of life (HRQOL) predictors: PsA Impact of Disease, Beck Depression Inventory (BDI), height, TJC, BMI
- axSpA, LDA predictors: Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), pretreatment with bDMARDs, CRP, ASAS HI, height
- axSpA, high HRQOL predictors: ASAS HI, BDI, BMI, height, age
Original publication
Predicting Treatment Outcomes in Patients With Psoriatic Arthritis or Axial Spondyloarthritis: An Artificial Intelligence–Driven Approach. J Rheumatol. Published 15 October 2025. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →