Publication details

Time in remission as an alternative outcome measure for rheumatoid arthritis: a 10-year prospective study of 2618 new users of anti-TNF



Year of publication 2022
Type Article in Periodical
Magazine / Source RHEUMATOLOGY
MU Faculty or unit

Faculty of Medicine

Keywords RA; outcome measure; disease activity; remission; biological therapy; anti-TNF; interpolation; prediction; registry; Czech Republic
Description Objective. Achieving targeted disease activity (DA) is the primary therapeutic strategy in RA. Point measurements of DA are done at out-patient visits, however true DA between visits remains unobserved. This study sought to describe and validate a new outcome measure, i.e. time in remission (TIR). Methods. Patients were enrolled in the Czech ATTRA-RA registry. TIR was calculated using linear interpolation of the DAS28-ESR determined at outpatient visits. Correlation coefficients were computed between TIR and DAS28-CRP, HAQ, Simple Disease Activity Index (SDAI), patient global assessment (PGA) and physician global assessment (PhGA). Using logistic regression, TIR was used as a predictor of remission (SDAI <= 3.3) and non-disability (HAQ <0.5). The predictive value of TIR was compared with point and sustained remission using the cross-validated area under receiver-operating curves. Results. Since 2010, 2618 RA patients started anti-TNF therapy and were followed until 2020 or until treatment discontinuation. During the first 6months of therapy, 56% of patients had no remission (TIR =0), and 22% of patients reached sustained remission (TIR =1), while 22% of patients had point remissions with 0< TIR < 1. EULAR good responders and moderate/non-responders spent 64 +/- 42% and 6 +/- 18% of time in remission, respectively. The mean TIR grew during the follow-up and was correlated with DAS28-CRP, SDAI, HAQ, PGA, and PhGA (P < 0.0001). TIR at 3 and 6months predicted remission (SDAI <= 3.3) and non-disability (HAQ <0.5) at 13 and 19 months better than point or sustained remission. Conclusions. TIR is an intuitive way of estimating unobserved DA between scheduled visits; its calculation only requires two consecutive DA values

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