Predicting Treatment Outcomes in Patients With Psoriatic Arthritis or Axial Spondyloarthritis: An Artificial Intelligence-Driven Approach.

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.