OP0114 PREDICTING RHEUMATOID ARTHRITIS USING THE SYMPTOMS IN PERSONS AT RISK OF RHEUMATOID ARTHRITIS (SPARRA) QUESTIONNAIRE
Authors: L. Van Boheemen, M. Ter Wee, M. Falahee, M. H. Van Beers-Tas, A. Finckh, A. Hensvold, K. Raza, D. Van Schaardenburg
Journal: Annals of the Rheumatic Diseases (2020)

Background: Accurate prediction of rheumatoid arthritis (RA) development in persons at risk of RA can help to select individuals for early intervention trials. Currently, RA prediction mostly relies on biomarkers such as genetic factors, autoantibodies and imaging abnormalities, with symptoms being only a minor component 1-3 . However, at-risk individuals exhibit a high prevalence of diverse and often severe symptoms 4,5 and information on the predictive ability of individual symptoms or symptom complexes is still largely lacking. Objectives: We investigated the prevalence and predictive ability of symptoms in persons at-risk of RA, using the validated ‘Symptoms in Persons At Risk of Rheumatoid Arthritis’ (SPARRA) questionnaire. Methods: Individuals at-risk of RA from four different cohorts from the Netherlands (n=122), United Kingdom (N=90), Sweden (N=13) and Switzerland (N=20), were asked to fill out the SPARRA questionnaire, consisting of 69 questions (previously described 6 ). Individuals were either defined as persons with anticitrullinated protein antibodies (ACPA) and/or rheumatoid factor (RF; n=193), having relevant symptoms (i.e. clinically suspect arthralgia with or without RA-specific antibodies, n=70) or being first degree relatives (FDR, n=20) of RA patients. All were followed for a minimum of 24 months or until clinical arthritis development. Univariable analyses were performed for possible predictor selection (p&lt;0.2), followed by stepwise forward selection (by Cox regression, p&lt;0.1) to create a multivariable prediction model. Results: The mean age of all participants was 49 years and 69% was female. In total, 56 persons (23%) developed clinical arthritis (n=22, 25, 7, 2 respectively in the 4 groups) after a median of 11.9 months (IQR 5.3 - 17.8). In total, 23 SPARRA questions were selected from the univariable analyses and entered in the stepwise forward selection procedure. Time to development of RA was predicted by the following questions: pain moving from joint to joint, having moderate or severe swelling in joints, feeling ≥1 days fatigue per month and feeling stiffness in joints of one and both feet (table 1). Table 1. Multivariable prediction model of SPARRA questions to predict clinical arthritis HR (95% CI), p Does your joint pain move from joint to joint? No; from arms to legs; from legs to arms (ref) 1 From ons side to the other 2.96 (1.57; 5.57), p = 0.001 Over the past month how much joint swelling have you had? None or mild (ref) 1 Moderate or severe 3.04 (1.48; 6.25), p = 0.003 Over the past month how many days of the month have you had fatigue? 0 (ref) 1 ≥ 1 0.32 (0.15; 0.67), p = 0.003 Where did you feel joint stiffness? Neither feet (ref) One foot 0.93 (0.42; 2.08), p = 0.865 Both feet 0.40 (0.17; 0.93), p = 0.032 Conclusion: Asking persons at-risk of RA about joint pain, swelling and stiffness is common clinical practice. However, specific details such as pain moving from one side to the other or degree of joint swelling may provide useful additional information to estimate a person’s RA risk. The protective effect noted for fatigue and stiff feet may reflect an underlying pain syndrome rather than RA risk. We are currently performing analyses of the potential added value of SPARRA questions over the clinical prediction model by van der Stadt et al 3 which will help determine the final format of the SPARRA questionnaire. References: [1]de Hair MJ et al. Ann Rheum Dis. 2013;72(10):1654-8. [2]Rakieh C et al. Ann Rheum Dis. 2015;74(9):1659-66. [3]van de Stadt LA et al. Ann Rheum Dis. 2013;72(12):1920-6. [4]Smolik I et al. J Rheumatol. 2013;40(6):818-24. [5]Stack RJ et al. Arthritis Care Res (Hoboken). 2013;65(12):1916-26. [6]van Beers-Tas MH et al. RMD Open. 2018;4(1):e000641. Acknowledgments: Supported by EULAR Disclosure of Interests: Laurette van Boheemen: None declared, Marieke ter Wee: None declared, M. Falahee: None declared, M.H. van Beers - Tas: None declared, Axel Finckh Grant/research support from: Pfizer: Unrestricted research grant, Eli-Lilly: Unrestricted research grant, Consultant of: Sanofi, AB2BIO, Abbvie, Pfizer, MSD, Speakers bureau: Sanofi, Pfizer, Roche, Thermo Fisher Scientific, Aase Hensvold: None declared, Karim Raza Grant/research support from: KR has received research funding from AbbVie and Pfizer, Consultant of: KR has received honoraria and/or consultancy fees from AbbVie, Sanofi, Lilly, Bristol-Myers Squibb, UCB, Pfizer, Janssen and Roche Chugai, Speakers bureau: KR has received honoraria and/or consultancy fees from AbbVie, Sanofi, Lilly, Bristol-Myers Squibb, UCB, Pfizer, Janssen and Roche Chugai, Dirkjan van Schaardenburg: None declared