Development of Machine Learning Models for Predicting Non-Remission in Early RA Highlights the Robust Predictive Importance of the RAID Score-Evidence from the ARCTIC StudyThe RAID score robustly predicts non-remission in early RA -Evidence from machine learning analysis of the ARCTIC study

Gaoyang Li ¹ Shrikant Kolan ¹ Franco Grimolizzi ¹ Joseph Sexton ² Giulia Malachin ¹ Guro Goll ² Tore K Kvien ² Nina Paulshus Sundlisaeter ² Manuela Zucknick ¹ Siri Lillegraven ² Espen A Haavardsholm ² Bjørn Steen Skålhegg ^1*

• ¹ University of Oslo, Oslo, Norway
• ² Oslo University Hospital, Oslo, Nordland, Norway

Achieving remission is a critical therapeutic goal in the management of rheumatoid arthritis (RA). Despite methotrexate being the cornerstone of early RA treatment, a significant proportion of patients fail to achieve remission. This study aims to predict 6-month nonremission in 222 disease-modifying anti-rheumatic drug (DMARD)-naïve RA patients initiating methotrexate monotherapy, using baseline patient characteristics from the ARCTIC trial. Machine learning models were developed utilizing twenty-one baseline demographic, clinical and laboratory features to predict non-remission according to ACR/EULAR Boolean, SDAI and CDAI criteria. The model employed a super learner algorithm that combine three base algorithms of elastic net, random forest and support vector machine. The model performance was evaluated through five independent unseen tests with nested 5-fold crossvalidation, achieving a mean AUC-ROC of 0.75-0.76, with mean sensitivity of 0.77-0.81, precision (also referred to as Positive Predictive Value) of 0.77-0.79 and specificity of 0.63-0.66 across the criteria. Predictive power analysis of each feature identified the baseline Rheumatoid Arthritis Impact of Disease (RAID) score as the strongest predictor of nonremission. A simplified model using RAID score alone demonstrated comparable performance to the full-feature model. These findings highlight the potential utility of baseline RAID score-based model as an effective tool for early identification of patients at risk of non-remission in clinical practise.