Objectives
To develop a prediction model of sustained remission following biologic or targeted synthetic disease modifying antirheumatic drug (b/tsDMARD) stop in rheumatoid arthritis (RA).
Methods
We conducted an explorative cohort study among b/tsDMARD RA treatment episodes courses stopped due to remission in the Swiss Clinical Quality Management registry (SCQM) [2008–2019]. The outcome was sustained b/tsDMARD free remission of ≥ 12 months. We applied logistic regression model selection algorithms using stepwise, forward, backward selection, and penalized regression to identify patient characteristics predictive of sustained b/tsDMARD free remission. We compared c-statistics corrected for optimism between models. The three models with highest c-statistics were validated in new SCQM data until 2020 (validation dataset).
Results
We identified 302 eligible episodes of which 177 episodes (59%) achieved sustained b/tsDMARD free remission. Two backward and one forward selection model with eight, four, and seven variables, respectively, obtained highest c-statistics corrected for optimism of c = 0·72, c = 0·70, and c = 0·69, respectively. In the validation dataset (47 eligible episodes), the models performed with c = 0·99, c = 0·80, and c = 0·74, respectively, and excellent calibration. The best model included the following 8 variables (measured at b/tsDMARD stop): RA duration, b/tsDMARD duration, other pain/anti-inflammatory drug use, quality of life (EuroQol), DAS28-erythrocyte sedimentation rate score, health assessment questionnaire (HAQ) score, education, and interactions of RA duration and other pain/anti-inflammatory drug use, and of b/tsDMARD duration and HAQ score.
Conclusion
Our results suggest that models with up to eight unique variables may predict sustained b/tsDMARD free remission with good efficiency. External validation is warranted.