POS0591 TREATMENTS TO PREVENT RHEUMATOID ARTHRITIS IN FIRST DEGREE RELATIVES: DEMOGRAPHIC AND PSYCHOLOGICAL PREDICTORS OF RISK TOLERANCES

Autor: G. Simons, E. Janssen, J. Veldwijk, R. Disantostefano, M. Englbrecht, C. Radawski, L. Valor, J. Humphreys, I. N. Bruce, B. Hauber, K. Raza, M. Falahee
Rok vydání: 2022
Předmět:
Zdroj: Annals of the Rheumatic Diseases. 81:562.1-562
ISSN: 1468-2060
0003-4967
DOI: 10.1136/annrheumdis-2022-eular.1611
Popis: BackgroundThere is a growing research focus on the development of interventions to reduce risk of rheumatoid arthritis (RA) in at-risk groups.(1) RA patients’ first-degree relatives (FDRs) have an elevated risk of developing RA and are potential candidates for preventive interventions. Recent studies have quantified the preferences of at risk groups for preventive treatments.(2-4) Little is known about predictors of preference heterogeneity in this context.ObjectivesAssess the extent to which FDR characteristics and beliefs predict risk tolerances for preventive treatments.MethodsAdult FDRs of patients with confirmed RA in the UK were invited to take part in a web-based survey. FDRs enrolled in a UK prospective cohort (PREVeNT-RA) were also invited. Survey development, including attribute selection and presentation, was informed by qualitative research, ranking surveys, literature review, and expert opinion including patient research partners. Respondents received information about RA, questions to check comprehension, and an introduction to the survey. Participants were asked to imagine they were experiencing arthralgia and had positive autoantibody tests indicating a 60% chance of developing RA within two years. Using a probabilistic threshold technique, participants made choices between no treatment (no benefit and no risks) or a preventive treatment option. Treatment options were defined by a fixed level of benefit (reduction in risk of RA from 60% to 20%) and varying levels of risks (Table 1). For each treatment risk, participants made a series of choices where the risk was systematically increased or decreased until they switched their choice. This procedure was repeated for each of the remaining risks. Participants also completed items assessing demographics, perceived risk of developing RA, health literacy, subjective numeracy, the Brief Illness Perception Questionnaire (IPQ) and the Beliefs about Medicines Questionnaire General (BMQ-G). The maximum acceptable risk (MAR) respondents were willing to accept for a 40% (60% to 20%) point risk reduction in developing RA was summarized across participants using descriptive statistics. Associations between MARs and participants’ characteristics and illness/medication beliefs were assessed using interval regression. Independent variables were dichotomized and effects coded.Table 1.Attributes and levels of treatment optionsTreatment attributeLevels describing no treatment optionLevels describing treatment optionChance of developing RA60%20%Chance of mild side effects0%2%; 4%; 5%; 7% or 10%Chance of a serious infection due to treatment0%1%; 1.5%; 2%; 3% or 5%Chance of a serious side effect that is potentially irreversible0%0.001%; 0.01%; 0.02%; 0.05% or 0.1%Results289 FDRs (80 male) responded. The mean (SE) MAR for mild side effects, serious infection, and serious side effects was 29.08 (1.52), 9.09 (0.60) and 0.85 (0.27), respectively. Participants aged over 60 years were less tolerant of risk of serious infection than average (mean MAR - 2.06 (0.78)) and younger participants were more tolerant of risk of serious infection than average (mean MAR + 2.06 (0.78)). Risk of mild side effects was less acceptable to participants who perceived they were likely/very likely to develop RA (mean MAR - 3.34 (1.55)) than to those who did not (mean MAR + 3.34 (1.55)). Education level, health literacy, numeracy, IPQ and BMQ-G subscales were not predictors of risk tolerance.ConclusionAge and perceived risk of RA had a significant impact on FDRs’ tolerance for specific, but not all, included risks. Cognitive ability and beliefs about RA/medicine did not explain preference heterogeneity. This is informative for drug development and the development of tailored risk communication resources to support preventive approaches.References[1]Mankia et al. Ann Rheum Dis. 2021;80(10):1286-98.[2]Simons et al. Ann Rheum Dis. 2021;80:96-7.[3]Harrison et al. Plos One. 2009; 14(4): e0216075.[4]Finckh et al. Curr Rheumatol Rep. 2016;18: 51.AcknowledgementsOn behalf of the PREFER project. PREFER received funding from the IMI 2 Joint Undertaking (grant No. 115966), which receives support from the EU’s Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations (EFPIA). K. Raza is supported by the NIHR Birmingham Biomedical Research Centre.Disclosure of InterestsGwenda Simons: None declared, Ellen Janssen Shareholder of: Johnson & Johnson, Employee of: Janssen Research and Development, Jorien Veldwijk: None declared, Rachael DiSantostefano Shareholder of: Johnson & Johnson, Employee of: Janssen Research and Development, Matthias Englbrecht Speakers bureau: Abbvie, Chugai, Eli Lilly, Novartis, Roche, Sanofi, Mundipharma, Paid instructor for: Abbvie, Chugai, Roche, Consultant of: Abbvie, Novartis, Roche, Sanofi, Grant/research support from: Roche, Chugai, Christine Radawski Shareholder of: Eli Lilly, Employee of: Eli Lilly, Larissa Valor: None declared, Jenny Humphreys: None declared, Ian N. Bruce: None declared, Brett Hauber Shareholder of: Pfizer Inc., Employee of: Pfizer Inc., Karim Raza Consultant of: Abbvie, Sanofi, Grant/research support from: Bristol Myers Squibb, Marie Falahee: None declared.
Databáze: OpenAIRE
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