AI Forecasts Treatment Outcomes for Psoriatic and Axial Spondyloarthritis

Published Date : 25/10/2025 

A new study reveals how artificial intelligence can predict treatment outcomes in patients with psoriatic arthritis (PsA) and axial spondyloarthritis (axSpA), offering a step toward personalized care. 

Artificial intelligence (AI) is set to revolutionize the way rheumatologists make treatment decisions for patients with psoriatic arthritis (PsA) and axial spondyloarthritis (axSpA). According to a groundbreaking study, machine learning (ML) models can predict which patients are most likely to achieve low disease activity (LDA) and high health-related quality of life (HRQoL) after 16 weeks of treatment with secukinumab, a biologic therapy that targets interleukin-17A.

The findings come from the AQUILA study, a large, ongoing, multicentre, real-world investigation involving 1,961 patients with active PsA or axSpA. The AI-driven approach analyzed baseline clinical, demographic, and laboratory data to forecast outcomes before treatment began.

The models used binary machine learning algorithms combined with explainable artificial intelligence (XAI) tools. This not only predicted outcomes but also interpreted how individual factors influenced the results. This level of transparency is crucial for clinicians to understand the reasoning behind the predictions.

For PsA, the strongest predictors of achieving LDA included patient and physician global assessments, prior biologic treatment, tender joint count, and age. Predictors for high HRQoL included disease impact scores, depression levels, height, tender joint count, and body mass index (BMI).

In patients with axSpA, the most influential factors for LDA were the disease activity index (BASDAI), prior biologic use, C-reactive protein (CRP) levels, ASAS Health Index, and height. High HRQoL was linked to better baseline functional scores, lower depression, and lower BMI.

The researchers emphasized that XAI added transparency to model predictions, allowing clinicians to understand why certain patients are more or less likely to respond to treatment. This transparency is essential for building trust and ensuring that the AI models are used effectively in clinical settings.

The study represents a significant step toward AI-assisted clinical decision support systems that could refine therapy choices in chronic inflammatory diseases. By leveraging AI, healthcare providers can move closer to personalized care, tailoring treatments to individual patient needs and improving outcomes. 

Frequently Asked Questions (FAQS): 

Q: What is the AQUILA study?

A: The AQUILA study is a large, ongoing, multicentre, real-world investigation involving 1,961 patients with active psoriatic arthritis (PsA) or axial spondyloarthritis (axSpA). It uses AI to predict treatment outcomes.

Q: What is secukinumab?

A: Secukinumab is a biologic therapy that targets interleukin-17A, used to treat psoriatic arthritis and axial spondyloarthritis.

Q: What are the key predictors for achieving low disease activity in PsA?

A: The key predictors for achieving low disease activity in PsA include patient and physician global assessments, prior biologic treatment, tender joint count, and age.

Q: What role does explainable artificial intelligence (XAI) play in this study?

A: XAI adds transparency to model predictions, allowing clinicians to understand why certain patients are more or less likely to respond to treatment.

Q: How does this study contribute to personalized care?

A: This study represents a step toward AI-assisted clinical decision support systems that can refine therapy choices in chronic inflammatory diseases, moving closer to personalized care. 

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