POS1434 WHICH PROGNOSTIC FACTORS MIGHT PREDICT THE NEED FOR TREATMENT ADAPTATION IN EARLY RHEUMATOID ARTHRITIS? A COMPARISON OF MACHINE LEARNING, SURVIVAL ANALYSIS AND REGRESSION METHODS
Authors: G. Verhavert, T. Verdonck, S. Pazmino, V. Stouten, D. Bertrand, M. Doumen, R. Westhovens, P. Verschueren, D. De Cock
Journal: Annals of the Rheumatic Diseases (2021)

Background: Current EULAR guidelines recommend treating RA early, intensively and to-target. A data-driven tool for planning the optimal moment for subsequent visits might adapt visit schedules more to the patient’s needs, without losing treatment quality. Objectives: To determine the optimal statistical model and clinical factors to predict the time to a treatment adaptation in early RA patients. Methods: This study included 379 patients from the treat-to-target Care in Rheumatoid Arthritis (CareRA) trial. The CareRA protocol included 2 predefined treatment adaptation steps for patients not reaching low disease activity (DAS28CRP&lt;3.2). The 1st adaptation was an MTX dose increase and the second one was adding/increasing the dose of a 2nd csDMARD. Three predictive models (Cox Proportional Hazards, Linear Multi-Task Regression and Random Survival Forest) were trained and validated to predicting time until these 2 adaptations. Factor selection for these models was performed by applying Cox Proportional Hazards with LASSO penalty to each set of demographic and clinical variables recorded at baseline, w4 and w8. Models used these factors at these 3 time points to predict future treatment adaptations. Model performance was estimated by the Uno Concordance Index with five-fold cross-validation. Missing data were imputed by interpolation or mean score. Results: Factors selected to predict the first per protocol change included TJC, SJC, HAQ, CRP, pain and morning stiffness&gt;15min. Factors selected to predict the second per protocol change included TJC, SJC, PGA, PhGA. Uno Concordance indices showed similar scores per different statistical model but higher scores at w4 and w8 compared to baseline indicating a better predictive performance (Table 1. next page). Table 1. Uno Concordance Scores. Format ‘mean (min-max)’. Baseline Week 4 Week 8 Cox Proportional Hazards Change 1 0.63 (0.59-0.69) 0.72 (0.68-0.80) 0.75 (0.69-0.82) Change 2 0.58 (0.50-0.66) 0.75 (0.65-0.87) 0.78 (0.65-0.93) Linear Multi-Task Regression Change 1 0.65 (0.62-0.68) 0.72 (0.68-0.78) 0.75 (0.69-0.79) Change 2 0.58 (0.51-0.68) 0.72 (0.67-0.82) 0.77 (0.68-0.89) Random Survival Forest Change 1 0.64 (0.58-0.67) 0.71 (0.66-0.78) 0.76 (0.68-0.80) Change 2 0.56 (0.50-0.68) 0.72 (0.60-0.82) 0.77 (0.64-0.92) Conclusion: Our data-driven approach identified predictive clinical factors with a high face validity including joint counts, functionality scores and global health indicators. The different model approaches did not seem to increase the predictive capacity performance. However, our results underline that not so much the baseline disease status but rather the early response to initial treatment reflected in the selected predictive factors can be used for prediction of the need for further treatment adaptation. These models will have to be enriched with patient reported outcomes to further improve predictive performance. Disclosure of Interests: None declared