Objectives: The aim was to develop a prediction model of sustained remission after cessation of biologic or targeted synthetic DMARD (b/tsDMARD) in RA.

Methods: We conducted an explorative cohort study among b/tsDMARD RA treatment episode courses stopped owing 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 selection, 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 the 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 the 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 eight variables (measured at b/tsDMARD stop): RA duration, b/tsDMARD duration, other pain/anti-inflammatory drug use, quality of life (EuroQol), DAS28-ESR score, 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.