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 study.

INTRODUCTION

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 non-remission in 222 disease-modifying anti-rheumatic drug (DMARD)-naïve RA patients initiating methotrexate monotherapy, using baseline patient characteristics from the ARCTIC trial.

METHODS

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 cross-validation. The predictive power of each feature was assessed using a composite measure derived from individual algorithm estimates.

RESULTS

The model demonstrated 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 non-remission. A simplified model using RAID score alone demonstrated comparable performance to the full-feature model.

CONCLUSION

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.