Predicting Treatment Outcomes in Patients With Psoriatic Arthritis or Axial Spondyloarthritis: An Artificial Intelligence-Driven Approach
Vodenčarević A, Brandt-Juergens J, Bär S, Kästner P, Köhm M, Simon D, Behrens F, Glassen T, Gmeiner B, Peterlik D, Kiltz U (2026)
Publication Type: Journal article
Publication year: 2026
Journal
Book Volume: 53
Pages Range: 152-161
Journal Issue: 2
Abstract
OBJECTIVE: To develop machine learning (ML) models to predict the probability at baseline of achieving low disease activity (LDA) and high health-related quality of life (HRQOL) in patients with psoriatic arthritis (PsA) or axial spondyloarthritis (axSpA) treated with secukinumab (SEC). METHODS: AQUILA is an ongoing multicenter, prospective, noninterventional study assessing the effectiveness and safety of SEC in patients with active PsA or axSpA in Germany. Data from 1961 participants were used to develop ML models for predicting treatment outcomes. We investigated baseline prediction of achieving LDA and high HRQOL at week 16 using binary ML algorithms, identifying main predictors for LDA and high HRQOL and their direction of influence. In addition, explainable artificial intelligence (XAI) estimated the importance and impact of each predictor based on how it affected the change in individual patient predictions. RESULTS: In PsA, the main LDA predictors were patient global assessment, physician global assessment, pretreatment with biologic disease-modifying antirheumatic drugs (bDMARDs), tender joint count (TJC), and age; high HRQOL predictors were PsA Impact of Disease, Beck Depression Inventory (BDI), height, TJC, and BMI (kg/m2). In axSpA, the main LDA predictors were Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), pretreatment with bDMARDs, C-reactive protein, Assessment of SpondyloArthritis international Society Health Index (ASAS HI), and height; high HRQOL predictors were ASAS HI, BDI, BMI, height, and age. CONCLUSION: XAI provides significant value by enabling explanations of individual patient predictions and their visualizations. This modeling approach may help in the development of a clinical decision support system for patient management.
Involved external institutions
MVZ Ambulantes Rheumazentrum Erfurt
Germany (DE)
Klinikum der Goethe-Universität
Germany (DE)
Charité - Universitätsmedizin Berlin
Germany (DE)
Rheumazentrum Ruhrgebiet
Germany (DE)
Rheumatologische Schwerpunktpraxis Privatdozent Dr. med. Brandt-Jürgens
Germany (DE)
Germany (DE)
Klinikum der Goethe-Universität
Germany (DE)
Charité - Universitätsmedizin Berlin
Germany (DE)
Rheumazentrum Ruhrgebiet
Germany (DE)
Rheumatologische Schwerpunktpraxis Privatdozent Dr. med. Brandt-Jürgens
Germany (DE)
How to cite
APA:
Vodenčarević, A., Brandt-Juergens, J., Bär, S., Kästner, P., Köhm, M., Simon, D.,... Kiltz, U. (2026). Predicting Treatment Outcomes in Patients With Psoriatic Arthritis or Axial Spondyloarthritis: An Artificial Intelligence-Driven Approach. Journal of Rheumatology, 53(2), 152-161. https://doi.org/10.3899/jrheum.2025-0327
MLA:
Vodenčarević, Asmir, et al. "Predicting Treatment Outcomes in Patients With Psoriatic Arthritis or Axial Spondyloarthritis: An Artificial Intelligence-Driven Approach." Journal of Rheumatology 53.2 (2026): 152-161.
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