Pos0600 Dose Reduction Strategy of Biologic Drugs in Ra Patients in Remission: A Model to Predictive Flare

Background: Rheumatoid arthritis (RA) is a chronic systemic inflammatory autoimmune disease characterized by diarthrodial joint damage. Currently, there is no consensus on how to treat a patient in sustained remission. There are still questions about whether we should consider dose de-escalation strategies or should continue with standard therapy. Based in that, discovery of a model to predict flares or remission in RA patients in clinical remission is justified. Objectives: To evaluate, in patients with RA in clinical remission and under biological therapy, the proportion of patients who, at the end of the 1st, 2nd and 3rd year with treatment, remain in remission or have a joint flare with a strategy of gradual tapering of biological therapy in comparison to patients receiving standard treatment. Determine which variables act as a risk factor for the development of joint flares and develop a flare prediction model of disease activity. Methods: Patients were selected from the OPTIBIO protocol (Eudra-CT code 2012-004482-40) and from the REMRABIT Project (PMP15/00032). Experimental Design: Multicenter, open-label, randomized, controlled, phase IV clinical trial comparing two treatment strategies in patients with RA in remission. Patients over 18 years of age with RA who met the 1987 ACR classification criteria and who were in remission for 6 months and on biologic therapy (TNFi and Tocilizumab) were included. Clinical-demographic variables, indices of disease activity, and serological markers were collected. Two treatment regimens were compared, a control group that continued treatment according to the drug label and an optimization group in which a standardized protocol of drug dose reduction was used. A descriptive analysis of baseline characteristics was performed in each arm. The quantitative variables were expressed as mean and standard deviation and the qualitative variables as absolute and relative frequencies. The comparison of means between treatment arms was carried out using the Student's T test or Mann-Whitney U test as appropriate after normality analysis (Kolmogorov-Smirnov Test). For the association between qualitative variables, Pearson's chi-square test or Fisher's exact test was used. Cox regression models were used to study the factors associated with the appearance of flare in each arm. The Kaplan-Meier method was used to estimate flare-free survival. The comparison between survival curves was analyzed using the Log-Rank test. The statistical software SPSS v28 and R v.4.3.1 were used. Results: A total of 196 patients were included, 99 in the control group and 96 in the optimization group. There was no statistical significance between the variables analyzed in both groups, so we determined a homogeneous sample. The flares in the 1st, 2nd and 3rd year were: 16 (53.3%), 12 (40%) and 2 (7%) in the control group and 24 (53.3%), 20 (44.4%) and 1 (2%) in the optimization group, respectively. Only the comparison of total flares in both arms were statistically significant, control group 30 (40%) vs optimization 45 (60%) p=0.02. However, the flare-free survival curve at 18 and 24 months showed statistic difference between bout groups (Figure 1). The variables associated with flare were in the control group: ESR, CRP, DAS28, DAS28-ESR, DAS28-CRP, DAS28 (3V-PCR) and HAQ and in the optimization group: BMI, CRP, DAS28, DAS28-CRP, DAS28 (3V-PCR), DAS28 (3V-ESR), SDAI and HAQ (Table 1). The best flare prediction model has an AUC of 0,75, sensitivity of 70% and specificity of 77%. Conclusion: The dose-reduction strategy of biologic therapy in patients with RA in remission used in this study was associated with increased risk of disease activity flare at 18 months. From all variables analyzed, CRP, DAS28-CRP, and a higher HAQ were associated with increased risk of flare in both groups. In this study we have developed a predictive model of joint flare that may be useful for using personalized medicine in RA. Inclusion of molecular data (genetic and proteomic biomarkers) could improve the prediction of this clinical model. REFERENCES: NIL. Acknowledgements: NIL. Disclosure of Interests: Francisco J. Blanco funding from Gedeon Richter Plc., Bristol-Myers Squibb International Corporation (BMSIC), Sun Pharma Global FZE, Celgene Corporation, Janssen Cilag International N.V, Janssen Research & Development, Viela Bio, Inc., Astrazeneca AB, UCB BIOSCIENCES GMBH, UCB BIOPHARMA SPRL, AbbVie Deutschland GmbH & Co.KG, Merck KGaA, Amgen, Inc., Novartis Farmacéutica, S.A., Boehringer Ingelheim España, S.A, CSL Behring, LLC, Glaxosmithkline Research & Development Limited, Pfizer Inc, Lilly S.A., Corbus Pharmaceuticals Inc., Biohope Scientific Solutions for Human Health S.L., Centrexion Therapeutics Corp., Sanofi, TEDEC-MEIJI FARMA S.A., KiniksaPharmaceuticals, Ltd. Grunenthal, Laura Galindo: None declared, Belen Acasuso: None declared, Vanesa Balboa-Barreiro: None declared, Juan Fernández-Tajes: None declared, Juan de Dios Cañete Crespillo: None declared, Benjamin Fernández-Gutiérrez: None declared, Isidoro González-Álvaro: None declared, José Luis Pablos Álvarez: None declared, Carmen Bejerano: None declared, Maite Silva: None declared, Francisco Javier de-Toro-Santos: None declared, Natividad Oreiro: None declared.