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    • br Funding Sources br Role of

    2018-11-14


    Funding Sources
    Role of Funding Source
    Conflicts of Interests
    Author contributions
    Introduction The effect of disease modifying antirheumatic drugs (DMARDs) in rheumatoid arthritis (RA) on disease activity is generally presented using population means (Combe et al., 2015; Gabay et al., 2015; Littlejohn et al., 2015). The use of biologic DMARDs (bDMARD) has revolutionized the therapy of severe RA (Sanmarti et al., 2015). However, the response to treatment is heterogeneous, both to cDMARDs (Aga et al., 2015), and to the various bDMARD agents (Kiely, 2015). As a major aim in the new era of precision medicine is to make liquiritin anti-rheumatic therapy more personalized, identifying and predicting distinct treatment responses trajectories to DMARDs has major implications for clinical practice. Studies (range n: 568–2752) focused on identifying types of patients with similar evolutions in disease activity (Siemons et al., 2014), physical activity (Demmelmaier et al., 2016), functional limitation (Norton et al., 2013), or psychological distress (Norton et al., 2011) and found subsets of patients with less favorable trajectories. The identification of predictors of response type trajectories could enable an early identification of patients needing a distinct treatment strategy. In RA, disease activity measures are the main clinical outcome used by practitioners to appraise the liquiritin of RA (Finckh et al., 2007), to modify and adapt treatment, and to determine if patients have reached a state of low disease activity (Inoue et al., 2007) or remission (Mohammed et al., 2015). The Disease Activity Score based on 28 joints (DAS28) is a well-established instrument to assess disease activity (Prevoo et al., 1995). A study of early RA patients (Siemons et al., 2014) (n=568) found three types of trajectories during the first year after treatment initiation: the most frequent type (82·6% of patients) was a good responder group, the second type (14·1%) comprised patients with a slower response to treatment, and the third one was composed of a very small group (3·3%) of patients who showed no improvement after 1year. However, the trajectories of disease activity in patients initiating a specific bDMARD or in patients with established disease have not been studied.
    Materials and Methods
    Results A total of 3898 patients initiated ABA with a mean number of 3.94 DAS28 assessments. Follow-up time ranged from 1month to 11.7years. Trajectory analysis of the entire sample identified three types of disease activity trajectories with low misclassification (for goodness of fit indices, see Appendix 1). The largest group (3576 patients, 91·7%) can be labeled as the ‘gradual responders’ (GR) type, with a mean DAS28 at baseline of 4·1 and a progressive improvement over time. Fig. 1 presents the observed means for patients based on assigned types of trajectories. Estimated mean trajectories were quite similar (data not shown). The second group (219 patients, 5·6%) can be described as the ‘rapid responders’ (RR) type, with higher DAS28 values at baseline, and a rapid improvement in disease activity. The third group (103 patients, 2·6%) can be identified as ‘inadequate responders’ (IR) type, with higher DAS28 values at baseline, a short improvement during the first 6months, followed by a return to initial disease activity level (for exact estimates of the DAS28 trajectories for gap junctions three types of patients, see Appendix 2). The three types were similar in age, sex, BMI distributions, disease duration, and comorbidities (Table 1). ‘Gradual responders’ group had less disability at baseline (mean HAQ score: GR, 1·1; RR, 1·7; IR, 1·3, p<0·001), and less previous treatment failures with cDMARDs and bDMARDs. Groups differed in mean DAS28 at baseline (p<0·001), with ‘gradual responders’ generally presenting lower disease activity at baseline. However, these differences were not the main determinant of group membership since the variability of DAS28 at baseline was large, and there was a large overlap of DAS28 values between groups (Fig. 2). Groups also differed in the components of the DAS28 score (i.e., tender joints, swollen joints, ESR or CRP, and patient global assessment). The sensitivity analysis using probability-weighted regression accounting for uncertainty in classification of patients into three groups found similar results. In particular, significant and non-significant results remained the same.