POS1528 PREDICTION OF LOW DISEASE ACTIVITY IN PATIENTS WITH PSORIATIC ARTHRITIS TREATED WITH SECUKINUMAB IN REAL WORLD – DATA FROM THE GERMAN AQUILA STUDY

Background Secukinumab (SEC) proved to be an effective treatment for patients with psoriatic arthritis (PsA) in previous randomized clinical trials [1]. There is only limited knowledge on prediction of low disease activity (LDA) and strategy PsA under SEC routine care. Objectives Using real-world data from the German non-interventional study AQUILA [2], main objectives were (1) predict LDA individual treated through machine learning methods (2) identify most important predictors their influence using explainable artificial intelligence (XAI). Methods Data 1041 used. Thirty-three demographic, parameters at baseline (BL) served as input develop models. was defined physician global assessment (PhGA) ≤ 2 week (w) 16 (+/- 6 w). Samples divided into training (70%) validation (30%) cohorts. Ten different models applied compared. Model performance measured area receiver operating characteristic curve (AUROC) which represents probability that a randomly selected patient will have higher achieve than moderate/high activity. Additionally, sensitivity specificity model computed express proportion correctly identified who reach or don’t w16, respectively. Shapley XAI estimated importance impact each predictor based how it affected change [3]. Results The influencing (PGA) BL, followed by PhGA, number pretreatments biologics, tender joint count age (Figure 1 A). AUROC best 0.68 cohort. Sensitivity 0.62 0.64, Applied approach showed lower BL values all reaching w16. highest evident biologic-naive also provided visual explanations patient-individual predictions: Variables shown green color increased whereas red ones opposite effect B). Conclusion A promising accuracy could reached validated. Identified such PGA direction mostly match existing [4]. analysis can provide useful insights predictions, potentially guiding decisions future. References [1]Mease P. et al., 2015 [2]Kiltz 2019 [3]Molnar C., (2022). [4]Kiltz Ann Rheum Dis, volume 78, supplement 2, year 2019, page A889 (FRI405) Figure 1: Main results : [3] B Explanation 79% American College Rheumatology (ACR), TJC (tender count), (PGA), (PhGA), Psoriatic Arthritis Impact Disease (PsAID), Additive exPlanations (SHAP) Acknowledgements: NIL. Disclosure Interests Asmir Vodencarevic Employee of: Novartis Pharma GmbH, Jan Brandt-Juergens Consultant AbbVie, Affibody, BMS, Eli Lilly, Gilead, Janssen, Medac, MSD, Novartis, Pfizer, Roche, Sanofi-Aventis, UCB, Daniel Peterlik Benjamin Gmeiner Uta Kiltz Amgen, Biogen, Chugai, GSK, Grünenthal, Hexal, Grant/research support from: UCB.