Five-year drug survival and discontinuation reasons for eight biological disease-modifying antirheumatic drugs for rheumatoid arthritis: A retrospective analysis of 1,182 patients from the Niigata Orthopedic Surgery Rheumatoid Arthritis Database (NOSRAD)

Five-year drug survival and discontinuation reasons for eight biological disease-modifying antirheumatic drugs for rheumatoid arthritis: A retrospective analysis of 1,182 patients from the Niigata Orthopedic Surgery Rheumatoid Arthritis Database (NOSRAD) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Five-year drug survival and discontinuation reasons for eight biological disease-modifying antirheumatic drugs for rheumatoid arthritis: A retrospective analysis of 1,182 patients from the Niigata Orthopedic Surgery Rheumatoid Arthritis Database (NOSRAD) Nariaki Hao, Naoki Kondo, Katsumitsu Arai, Naoko Kudo, Takehiro Murai, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7930489/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Continuity of care of patients with rheumatoid arthritis, afforded by treatment in a single institution under the same attending physician, reduces outpatient attrition and enables a stable collection of long-term clinical data. However, our prefecture, like many regional areas in Japan, faces a chronic shortage of board-certified rheumatologists. Therefore, we aimed to determine the five-year drug-survival and discontinuation reasons for eight biological disease-modifying antirheumatic drugs (bDMARDs) for rheumatoid arthritis using Japan’s Niigata Orthopedic Surgery Rheumatoid Arthritis Database. Methods Between May 2001 and August 2022, 1,182 patients were retrospectively analyzed. Naïve (n = 784) and switch (n = 398) patients initiated their first or subsequent bDMARD, respectively. The primary end-point was five-year drug-survival per bDMARD while the secondary analyses assessed methotrexate (MTX) co-therapy, discontinuation risk factors, and cumulative incidence of discontinuation reasons. Kaplan–Meier curves, Cox (stratified by drug), and Fine & Gray models were applied. Results Naïve cohort showed significant inter-drug differences in sex, age, disease duration, 28-joint Disease Activity Score with erythrocyte-sedimentation-rate (DAS28-ESR), MTX or prednisolone (PSL) co-therapy, and PSL dose. Switch cohort differed by age, disease duration, DAS28-ESR, MTX co-therapy, PSL dose, and treatment line. Five-year drug-survival in naïve cohort ranged from tocilizumab (50.8%) to golimumab (22.6%); in switch cohort, from abatacept (42.6%) to infliximab (10.0%). Cox analysis in naïve cohort found male sex, lower baseline DAS28-ESR, and no MTX predicted discontinuation, explained by inadequate response (27.1%), adverse events (17.3%), and non-adverse events (25.3%). Conclusions Early, individualized drug selection and dose optimization are crucial to maximize long-term bDMARD effectiveness before switching. Biological disease-modifying antirheumatic drugs Discontinuation Drug survival Japan Niigata Orthopedic Surgery Rheumatoid Arthritis database Retrospective study Rheumatoid arthritis Cohort study Inadequate response Adverse events Figures Figure 1 Figure 2 Figure 3 Background Rheumatoid arthritis (RA), a systemic autoimmune disease characterized by synovial inflammation, culminates in progressive joint destruction [1]. The prevalence of RA varies from approximately 0.2–0.5% among many nations [2]. Recent advances in the elucidation of the immunological mechanisms involved in the onset and progression of RA—particularly the roles of pro-inflammatory cytokines—have substantially deepened our understanding of its pathophysiology [3, 4]. In Japan, biological disease-modifying antirheumatic drugs (bDMARDs) were introduced in 2003, ushering in a paradigm shift in RA management [5–7]. Although bDMARDs exhibit high efficacy, their drug survival and the reasons for discontinuation may be influenced by multiple factors, including patient characteristics, regional contexts, and health-care delivery systems. While numerous multicenter studies have addressed this topic [8, 9], long-term, consistent real-world, single institution data remain limited [10]. Continuity of care of patients with RA, afforded by treatment in a single institution under the same attending physician, reduces outpatient attrition and enables a stable collection of long-term clinical data. However, our prefecture, like many regional areas in Japan, faces a chronic shortage of board-certified rheumatologists [11, 12]. In this study, we utilized the Niigata Orthopedic Surgery Rheumatoid Arthritis Database (NOSRAD) to retrospectively analyze the drug survival and discontinuation reasons of eight bDMARDs administered to patients with RA treated in our department. By clarifying real-world treatment patterns, we aim to inform the development of more effective therapeutic strategies. Materials and Methods Study design, setting, and population The NOSRAD is an ongoing, observational, single-center registry that prospectively collects clinical data on every patient with RA treated in the Department of Orthopedic Surgery at Niigata University. For this retrospective cohort study, we screened all consecutive patients registered in NOSRAD between 1 May 2001 and 31 August 2022 (n = 1,517). Inclusion criteria Age ≥ 18 years at the index date. Fulfilment of either the 1987 revised American College of Rheumatology (ACR) criteria or the 2010 ACR/European League Against Rheumatism (EULAR) classification criteria for RA [13, 14]. Receipt of at least one approved dose of any of the following eight bDMARDs: infliximab (IFX), etanercept (ETN), tocilizumab (TCZ), adalimumab (ADA), abatacept (ABT), golimumab (GLM), certolizumab pegol (CZP) or sarilumab (SAR); intravenous and subcutaneous formulations were analyzed together. Complete data on baseline demographics, disease characteristics, previous conventional synthetic DMARD (csDMARD) exposure, and comorbidities. At least one follow-up visit recorded after the index bDMARD administration. Exclusion criteria Current treatment using any targeted synthetic DMARD (n = 230); Diagnosis of spondyloarthritis or other inflammatory arthritides (n = 14); Missing values on any key variable (n = 91); Off-label bDMARD dosing or single, unconfirmed exposure lasting < 1 month. After the exclusions, the final analytic cohort comprised 1,182 patients. Patients who initiated their first bDMARD constituted the naïve cohort (n = 784) and those who switched from at least one prior bDMARD formed the switch cohort (n = 398). Follow-up continued from the first documented dose until permanent drug discontinuation, death, loss to follow-up (> 12 months without contact), or 31 August 2022, whichever occurred first. Treatment and follow-up Therapy was administered in accordance with the clinical practice guidelines of the Japan College of Rheumatology and was managed by six board-certified rheumatologists (NK, KA, NK, TM, JF, and KY). After bDMARD initiation, patients attended the orthopedic outpatient clinic every 4–12 weeks. Outcome measures The primary endpoint was the five-year drug survival rate for each bDMARD. Drug survival was defined retrospectively as the interval from the first administration of a bDMARD to permanent discontinuation. Secondary endpoints included (i) comparison of drug survival according to concomitant methotrexate (MTX) use and (ii) identification of factors associated with treatment discontinuation. Explanatory variables Candidate variables were sex, age, disease duration, baseline 28-joint Disease Activity Score using erythrocyte sedimentation rate (DAS28-ESR), concomitant MTX, and concomitant prednisolone (PSL). Reasons for discontinuation were categorized as inadequate response (including primary and secondary); adverse events (infection, pulmonary, liver, skin disorders, cardiovascular disease, malignant tumor, and others); or non-adverse events (remission or good response, patient preference, transfer to another hospital, other reasons, and unknown). Physicians were allowed to cite only one reason for the discontinuation. Statistical analysis Baseline characteristics were summarized using descriptive statistics. Categorical variables were compared using the chi-square test; continuous variables were compared using the Kruskal–Wallis test, with multiple comparisons adjusted by the Steel–Dwass method. Drug survival curves were estimated by the Kaplan–Meier method and compared with the log-rank test. Cumulative incidence functions for each discontinuation reason were derived from a competing-risks model and compared with the Fine & Gray test. We used Cox proportional hazards model, stratified by drug type, to analyze factors influencing treatment discontinuation. Explanatory variables were sex, age, disease duration, DAS28-ESR, and concomitant use of MTX and PSL. The proportional hazards assumption was assessed with Schoenfeld residuals. Bonferroni adjustment was applied to control the family-wise error rate arising from multiple comparisons. All statistical analyses were performed by using EZR (Saitama Medical Centre, Jichi Medical University, Saitama, Japan) [15], and a two-sided p value < 0.05 was considered statistically significant. Results Baseline characteristics Baseline characteristics of the naïve cohort (n = 784) are summarized in Table 1 . Significant inter-drug differences were observed for sex, age, disease duration, DAS28-ESR, concomitant MTX use, concomitant PSL use, and PSL dose (p = .040, p < .001, p < .001, p < .001, p < .001, p = .002, and p = .012, respectively; Kruskal–Wallis test). The mean age of patients receiving ABT was 70.4 years, significantly higher than that of patients treated with IFX (55.1 years; p < .001), ETN (56.4 years; p = .002), or TCZ (56.7 years; p = .006), according to the Steel–Dwass test. MTX dose did not differ among the patients regardless of the drugs. Table 1 Baseline characteristics of naïve patients (n = 784) IFX ( n =64) ETN ( n =217) TCZ ( n =253) ADA ( n =62) ABT ( n =89) GLM ( n =62) CZP ( n =26) SAR ( n =11) p-value Women, n (%) 51 (79.7) 183 (84.3) 194 (76.7) 43 (69.4) 69 (77.5) 54 (87.1) 20 (76.9) 6 (54.5) .040 Age (years), mean ± SD 55.1 ± 14.9 56.4 ± 15.1 56.7 ± 16.3 57.8 ± 13.3 70.4 ± 10.5 67.4 ± 13.2 56.8 ± 14.0 68.6 ± 9.3 < .001 Disease duration (years), mean ± SD 9.8 ± 10.0 10.5 ± 9.6 7.1 ± 9.1 10.9 ± 10.5 11.2 ± 12.5 14.6 ± 12.0 7.6 ± 7.5 16.0 ± 16.0 < .001 DAS28-ESR, mean ± SD 4.22 ± 1.29 3.89 ± 1.31 3.74 ± 1.50 3.28 ± 1.19 4.65 ± 1.39 2.93 ± 0.76 2.39 ± 0.86 3.62 ± 0.94 < .001 MTX use, n (%) 59 (92.2) 134 (62.2) 104 (41.1) 48 (77.4) 32 (36.0) 47 (67.7) 17 (65.4) 6 (54.5) < .001 MTX dose (mg/week), mean ± SD 7.4 ± 1.8 7.2 ± 2.1 7.7 ± 2.2 8.1 ± 2.5 7.3 ± 3.2 7.9 ± 2.3 7.4 ± 2.3 6.7 ± 0.9 .248 PSL use, n (%) 28 (43.8) 113 (52.1) 97 (38.3) 22 (35.5) 54 (60.7) 29 (46.8) 10 (38.5) 7 (63.6) .002 PSL dose (mg/day), mean ± SD 7.9 ± 9.5 6.9 ± 5.5 8.0 ± 8.2 5.2 ± 2.6 5.7 ± 3.4 4.2 ± 2.1 6.5 ± 2.6 6.1 ± 2.9 .012 IFX, infliximab; ETN, etanercept; TCZ, tocilizumab; ADA, adalimumab; ABT, abatacept; GLM, golimumab; CZP, certolizumab pegol; SAR, sarilumab; DAS28-ESR, the 28-joint Disease Activity Score with erythrocyte sedimentation rate; MTX, methotrexate; PSL, prednisolone; SD, standard deviation. Baseline characteristics of the switch cohort (n = 398) are shown in Table 2 . Significant differences were found among the patients based on the drugs in age, disease duration, DAS28-ESR, concomitant MTX use, PSL dose, and use as a second- or fourth-line agent (p < .001, p = .020, p = .002, p < .001, p = .032, p = .006, and p = .028, respectively; Kruskal–Wallis test). No significant differences were detected in sex, MTX dose, concomitant PSL use, or use as a third-line agent. The mean age of ABT-treated patients was 70.1 years, significantly exceeding that of patients receiving ETN (55.1 years; p < .001); TCZ (59.2 years; p < .001); ADA (54.6 years; p < .001); or CZP (56.3 years; p = .010), according to the Steel–Dwass test. GLM users were also older (67.3 years) than those on ETN (55.1 years; p = .019) or ADA (54.6 years; p = .047), according to the Steel–Dwass test. Table 2 Baseline characteristics of the switch patients (n = 398) IFX ( n =10) ETN ( n =63) TCZ ( n =137) ADA ( n =24) ABT ( n =56) GLM ( n =33) CZP ( n =27) SAR ( n =48) p-value Women, n (%) 6 (60.0) 53 (84.1) 111 (81.0) 17 (70.8) 48 (85.7) 23 (69.7) 24 (88.9) 37 (77.1) .219 Age (years), mean ± SD 58.7 ± 15.7 55.1 ± 16.3 59.2 ± 14.8 54.6 ± 14.5 70.1 ± 12.9 67.3 ± 14.0 56.3 ± 14.8 61.6 ± 14.9 < .001 Disease duration (years), mean ± SD 7.0 ± 5.7 12.5 ± 9.5 12.4 ± 9.9 10.8 ± 8.1 16.0 ± 11.3 16.5 ± 9.3 10.1 ± 10.4 11.4 ± 7.8 .020 DAS28-ESR, mean ± SD 4.50 ± 1.38 3.57 ± 1.38 3.37 ± 1.16 3.35 ± 1.16 4.50 ± 1.32 3.03 ± 1.32 2.94 ± 1.25 3.64 ± 1.54 .002 MTX use, n (%) 9 (90.0) 43 (68.3) 77 (56.2) 19 (79.2) 22 (39.3) 22 (66.7) 13 (48.1) 19 (39.6) < .001 MTX dose (mg/week), mean ± SD 8.2 ± 2.0 7.6 ± 1.7 7.5 ± 2.5 7.9 ± 2.2 6.8 ± 2.5 6.5 ± 3.1 7.8 ± 2.1 7.0 ± 3.1 .296 PSL use, n (%) 6 (60.0) 29 (46.0) 63 (46.0) 13 (54.2) 30 (53.6) 21 (63.6) 11 (40.7) 25 (52.1) .517 PSL dose (mg/day), mean ± SD 5.7 ± 3.3 5.5 ± 3.4 7.2 ± 4.9 5.0 ± 2.1 7.3 ± 7.1 5.0 ± 2.8 3.8 ± 1.2 5.2 ± 3.5 .032 2nd bio, n (%) 2 (20.0) 34 (54.0) 90 (65.7) 10 (41.7) 32 (57.1) 23 (69.7) 13 (48.1) 20 (41.7) .006 3rd bio, n (%) 3 (30.0) 16 (25.4) 29 (21.2) 7 (29.2) 13 (23.2) 5 (15.2) 5 (18.5) 15 (31.3) .724 ≧4th bio, n (%) 5 (50.0) 13 (20.6) 18 (13.1) 7 (29.2) 11 (19.6) 5 (15.2) 9 (33.3) 13 (27.1) .028 IFX, infliximab; ETN, etanercept; TCZ, tocilizumab; ADA, adalimumab; ABT, abatacept; GLM, golimumab; CZP, certolizumab pegol; SAR, sarilumab; DAS28-ESR, the 28-joint Disease Activity Score with erythrocyte sedimentation rate; MTX, methotrexate; PSL, prednisolone; bio, biologic agent; SD, standard deviation. In the overall cohort (n = 1,182), significant inter-drug differences were detected in age, disease duration, DAS28-ESR, concomitant MTX use, concomitant PSL use, PSL dose, and use as first-, second-, third-, or fourth-line therapy (all p < .001; however, PSL use p = .005 and PSL dose p = .001; Kruskal–Wallis test), whereas sex and MTX dose did not differ (Supplementary table S1 ). The mean age of ABT users was 70.3 years, significantly higher than that of patients treated with IFX (55.6 years; p = .004); ETN (56.1 years; p < .001); TCZ (57.6 years; p < .001); or ADA (56.9 years; p = .040), according to the Steel–Dwass test. Drug survival rates In the overall cohort, TCZ showed the highest five-year drug survival (46.3%), followed by ABT (45.0%), ETN (32.2%), ADA (28.0%), CZP (25.2%), IFX (23.0%), and GLM (18.9%) (Fig. 1 A). TCZ survival was significantly superior to that of IFX, ETN, ADA, GLM, and CZP (all p ≤ .001, Bonferroni-adjusted log-rank test). No significant difference was observed between TCZ and ABT. In the naïve cohort, TCZ again demonstrated the highest five-year survival (50.8%), followed by ABT (46.6%), ETN (36.6%), CZP (33.0%), ADA (32.3%), IFX (25.0%), and GLM (22.6%) (Fig. 1 B). TCZ survival exceeded that of IFX, ETN, and GLM (all p < .001, Bonferroni-adjusted log-rank test). In the switch cohort, ABT achieved the best 5-year survival (42.6%), followed by TCZ (38.2%), ADA (18.3%), ETN (15.1%), CZP (17.3%), GLM (14.8%), and IFX (10.0%) (Fig. 1 C). TCZ survival was higher than that of IFX and GLM (both p < .001, Bonferroni-adjusted log-rank test), and ABT survival was higher than IFX (p < .001, Bonferroni-adjusted log-rank test). Stratified analyses revealed generally better survival in MTX-combination therapy than in monotherapy (Supplementary Figures S1 and S2). Notably, in the naïve cohort, ADA survival was significantly higher with MTX combination therapy (p < .002, Bonferroni-adjusted log-rank test) (Fig. 2 ). Risk factors for drug discontinuation In the Cox proportional hazards model stratified by drug type (strata), significant predictors of discontinuation in the naïve cohort were: sex (male vs female: hazard ratio [HR] = 1.49, 95% confidence interval [CI] = 1.09–2.02, p = .011); baseline DAS28-ESR (per unit decrease: HR = 0.90, 95% CI = 0.82–0.99, p = .039); and concomitant MTX use (yes vs no: HR = 0.73, 95% CI = 0.55–0.97, p = .028) (Table 3 ). Table 3 Multivariable Cox proportional hazards model for treatment discontinuation in naïve patients, stratified by drug Multivariable HR 95% CI p-value Sex 1.49 1.09–2.02 .011 Age 1.00 0.99–1.01 .638 Disease duration 1.00 0.99–1.02 .553 DAS28-ESR 0.90 0.82–0.99 .039 MTX 0.73 0.55–0.97 .028 PSL 1.10 0.85–1.41 .483 Differences between drugs were assessed using the Cox-P value. HR, hazard ratio; 95%CI, 95% confidence interval; DAS28-ESR, the 28-joint Disease Activity Score with erythrocyte sedimentation rate; MTX, methotrexate; PSL, prednisolone. In the overall cohort, only baseline DAS28-ESR remained significant (HR = 0.91, 95% CI = 0.84–0.98, p = .015; Supplementary Table S2). No significant predictors emerged in the switch cohort (Supplementary Table S3). The proportional hazards assumption was satisfied by the Schoenfeld residuals. Reasons for drug discontinuation Overall, 823 patients (69.6%) discontinued bDMARD. The reasons were inadequate response (n = 320, 27.1%); adverse events (n = 204, 17.3%); and non-adverse events (n = 299, 25.3%) (Table 4 ). Adverse events comprised infections (n = 79, 6.7%); pulmonary disorders (n = 12, 1.0%); liver disorders (n = 9, 0.8%); skin disorders (n = 28, 2.4%); cardiovascular disease (n = 7, 0.6%); malignant tumor (n = 15, 1.3%); and others (n = 54, 4.6%). Non-adverse events included remission or good response (n = 43, 3.6%); patient preference (n = 33, 2.8%); transfer to another hospital (n = 167, 14.1%); other reasons (n = 11, 0.9%); and unknown (n = 45, 3.8%) (Supplementary Table S4) Table 4 Reasons for discontinuation of each bDMARDs among 1,182 patients IFX ( n =74) ETN ( n =280) TCZ ( n =390) ADA ( n =86) ABT ( n =145) GLM ( n =95) CZP ( n =53) SAR ( n =59) p-value Inadequate response 30 (40.5) 96 (34.3) 65 (16.7) 33 (38.4) 33 (22.8) 27 (28.4) 22 (41.5) 14 (23.7) < .001 Adverse events 15 (20.3) 56 (20.0) 65 (16.7) 15 (17.4) 19 (13.1) 16 (16.8) 13 (24.5) 5 (8.5) .256 Non-adverse events 22 (29.7) 96 (34.3) 99 (25.4) 16 (18.6) 27 (18.6) 24 (25.3) 7 (13.2) 8 (13.6) < .001 Values are presented as n (percent). bDMARDs, biological disease-modifying antirheumatic drugs; IFX, infliximab; ETN, etanercept; TCZ, tocilizumab; ADA, adalimumab; ABT, abatacept; GLM, golimumab; CZP, certolizumab pegol; SAR, sarilumab. The drug-by-drug comparison revealed a significant difference in discontinuations attributable to inadequate response (p < .001, chi-square test). The proportions were highest for CZP (n = 22, 41.5%); IFX (n = 30, 40.5%); and ADA (n = 33, 38.4%); followed by ETN (n = 96, 34.3%); GLM (n = 27, 28.4%); SAR (n = 14, 23.7%); ABT (n = 33, 22.8%); and TCZ (n = 65, 16.7%). The absolute numbers and percentages of discontinuations of the following drugs due to adverse events were: CZP (n = 13, 24.5%), IFX (n = 15, 20.3%), ETN (n = 56, 20.0%), ADA (n = 15, 17.4%), GLM (n = 16, 16.8%), TCZ (n = 65, 16.7%), ABT (n = 19, 13.1%), and SAR (n = 5, 8.5%). Although a global chi-square test did not reach significance level, the distribution indicates relatively fewer adverse-event discontinuations with SAR and ABT. Discontinuations for non-adverse events—including remission, patient preference, and transfer to another hospital—also differed significantly among drugs (p < .001, chi-square). This was highest for ETN (n = 96, 34.3%), followed by IFX (n = 22, 29.7%), TCZ (n = 99, 25.4%), GLM (n = 24, 25.3%), ABT (n = 27, 18.6%), ADA (n = 16, 18.6%), SAR (n = 8, 13.6%), and CZP (n = 7, 13.2%). Competing-risk analysis demonstrated that discontinuation owing to inadequate response rose steeply during the first 2 years and more gradually thereafter (Fig. 3 A). At 5 years, the cumulative incidence was highest for CZP (42.2%), followed by ADA (35.9%), IFX (37.8%), GLM (28.0%), ETN (27.9%), ABT (24.5%), and lowest for TCZ (9.5%). The rate of TCZ was significantly lower than those of IFX (p = .004), ETN (p = .003), ADA (p < .001), GLM (p = .009), CZP (p < .001), and SAR (p = .026), based on Bonferroni-adjusted log-rank test (p < .001 overall, Fine & Gray test). Discontinuations attributed to adverse events increased during the first year and then plateaued (Fig. 3 B). At 5 years, the cumulative incidence was highest for CZP (21.3%), followed by GLM (18.3%), IFX (17.6%), ETN (15.8%), ADA (15.7%), TCZ (14.5%), ABT (12.8%), and lowest for SAR (8.8%). For non-adverse events, the 5-year cumulative incidence was highest for GLM (31.1%), followed by TCZ (24.8%), ETN (24.2%), IFX (21.6%), ABT (17.7%), ADA (17.4%), CZP (11.4%) (Fig. 3 C). Discussion In this large, single-center retrospective cohort study, we systematically analyzed the five-year drug survival and discontinuation reasons for eight bDMARDs in 1,182 patients with RA in Japan. Although numerous multicenter studies and database analyses of drug persistence have been published [8, 9], investigations enrolling more than 1,000 patients at a single institution—who were all managed by specialists in the same field (orthopedic rheumatology)—and that examined the detailed discontinuation motives for each agent, are exceedingly rare [10]. This is considered the key novelty of the present work. In the overall and naïve cohorts, TCZ demonstrated the highest drug survival, and ABT also maintained favorable persistence. These trends are partly consistent with previous reports [10, 16–22]. A notable clinical advantage of TCZ and ABT is that they remain effective without concomitant MTX, benefitting older adult patients or those unsuitable for combination therapy [19, 23]. Among tumor necrosis factor inhibitors (TNFi), ETN outlasts IFX and ADA [24, 25], with IFX often showing the poorest survival [24, 26], similar to the pattern observed in the present study. Conversely, certain TNFi such as IFX and GLM were often discontinued early owing to insufficient efficacy, suggesting that earlier switching to agents with different mechanisms may be beneficial. The unexpectedly high survival of ABT in the switch cohort indicates retained efficacy in refractory cases previously exposed to multiple bDMARDs, supporting mechanism-based switching strategies. In naïve patients on ADA, concomitant MTX significantly improved survival. Monoclonal-antibody TNFi are susceptible to anti-drug antibody formation, which undermines therapeutic efficacy [27, 28–30]. ADA is a fully human monoclonal antibody; however, it does not completely escape immunogenicity. These findings underscore the importance of MTX-mediated suppression of anti-drug antibodies [16, 18, 21, 25, 31–42]. In the naïve cohort, Cox analysis identified male sex, lower baseline DAS28-ESR, and absence of MTX co-therapy as independent predictors of discontinuation. Unlike previous studies showing poorer persistence in females [43–45], male sex remained a risk factor even after censoring discontinuations due to hospital transfer or patient preference (HR = 1.54, 95% CI = 1.09–2.17, p = .013). Possible explanations for the early discontinuation in males include inefficacy or adverse events, relatively lower per-kilogram dosing with fixed-dose subcutaneous regimens in individuals with higher body mass index [46, 47], and enhanced immunogenicity associated with higher smoking rates [48, 49]. The higher discontinuation among patients with low disease activity likely reflects intentional cessation after achieving remission rather than therapeutic failure. As in previous studies, no MTX is a negative predictor of persistence [16, 24, 50, 51], and low MTX dosage has likewise been implicated [8, 16, 24, 51]. Japanese cohorts, with a mean MTX dose (7.5 ± 2.3 mg/week in this study), often benefit from lower MTX doses than Western populations [52]. PSL co-therapy had no influence on survival [53]. Although inadequate effectiveness was the leading cause of discontinuation (27.1%), adverse events (17.3%) and non-adverse events (25.3%) were also substantial. Discontinuations due to hospital transfer or patient preference may reflect regional specialist maldistribution and referral patterns in the provincial areas. ABT had the lowest adverse-event discontinuation rate, consistent with previous reports of fewer episodes of severe infections and infusion reactions, suggesting suitability for older patients [16, 54, 55]. Inadequate response discontinuations clustered within 2 years of initiation, while adverse events peaked in the first year, highlight the first two years as critical for bDMARD success and underscore the necessity for a treat-to-target strategy with timely assessment and switching [56]. This study has limitations. First, it was a retrospective analysis of an observational registry data from a single tertiary center; therefore, selection bias, information bias, and limited generalizability to other institutions or regions cannot be excluded. Second, discontinuation decisions and explanations were clinician-dependent, not standardized. Third, baseline characteristics differed among drugs, and unmeasured confounding may persist despite adjustment. Fourth, minor dose modifications of bDMARDs, MTX, or PSL were not captured. Fifth, differences between intravenous and subcutaneous preparations and concomitant csDMARDs could not be fully ascertained. Sixth, CZP and SAR were approved relatively recently in Japan (2013 and 2017, respectively), explaining the smaller sample sizes that may have affected the estimates. Despite these constraints, this study offers high-resolution real-world data through long-term follow-up, detailed discontinuation causes, and stratified analyses in a uniform treatment setting. Future multicenter prospective studies—and, in particular, large-scale cohorts with greater sample sizes—are essential to validate our findings, clarify dose–response relationships, and incorporate immunological or biomarker data to guide personalized therapy. Conclusion In this single-center, long-term retrospective cohort of 1,182 patients with RA, we compared five-year drug survival and discontinuation reasons for eight bDMARDs. TCZ achieved the highest survival in biologic-naïve patients, whereas ABT ranked first among the switch patients. Transitioning from one bDMARD to another markedly reduced persistence overall. ABT—used more frequently in older patients—exhibited a comparatively low rate of adverse-event discontinuations, underscoring its clinical utility in older adult populations. Concomitant MTX significantly improved the survival of ADA. Male sex, lower baseline disease activity, and absence of MTX co-therapy emerged as potential risk factors for treatment cessation. Inadequate response was the leading cause of discontinuation, clustering within the first 2 years after initiation. These findings highlight the need to tailor both drug selection and continuation strategies to individual patient characteristics and emphasize vigilant management during the early treatment phase. Building on these real-world data, future multidimensional assessments and prospective studies are warranted to refine personalized therapeutic approaches in RA. Abbreviations ABT, abatacept ACR, American College of Rheumatology ADA, adalimumab bDIMARDs, biological disease-modifying antirheumatic drugs CZP, certolizumab pegol ETN, etanercept EULAR, European League Against Rheumatism GLM, golimumab IFX, infliximab MTX, methotrexate NOSRAD, Niigata Orthopaedic Surgery Rheumatoid Arthritis Database PSL, prednisolone RA, rheumatoid arthritis SAR, sarilumab TCZ, tocilizumab Declarations Ethical approval and consent to participate This study was approved by the institutional ethics review board (approval number: 2018 − 0377) and complied with the principles set forth in the Declaration of Helsinki. Human ethics and consent to participate declarations This retrospective study was approved by the institutional ethics review board of Niigata University (approval number: 2018–0377) and conducted in accordance with the Declaration of Helsinki. The requirement for informed consent was waived by the ethics committee because of the retrospective design of the study. Consent for publication Not applicable. Competing interest The authors declare that they have no competing interests. Funding Not applicable. Authors’ contributions N.H. and N.K. wrote the main manuscript text. N.H. prepared Tables 1–4 and Figures 1–3. N.H. and Y.K. collected the patient data. All authors read and approved the final version of the manuscript. Acknowledgments Not applicable. References Wu Y. Early detection of rheumatoid arthritis in rats and humans with 99mTc-3PRGD2 scintigraphy: imaging synovial neoangiogenesis. Oncotarget. 2017;8:5753–60. Carmona L, Cross M, Williams B, Lassere M, March L. Rheumatoid arthritis. Best Pract Res Clin Rheumatol. 2010;24:733–45. Kondo N, Kuroda T, Kobayashi D. Cytokine networks in the pathogenesis of rheumatoid arthritis. Int J Mol Sci. 2021;22:10922. Smolen JS, Aletaha D, Mclnnes IB. Rheumatoid arthritis. 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Supplementary Files GraphicalAbstractAug142025.png SupplementaryTableFigures.251023.docx Additional File 1 legends File name: Additional File 1 File format: docx Title and description of data: Supplementary Table S1. Baseline characteristics of all patients (n = 1,184) Supplementary Table S2. Multivariable Cox proportional hazards model for treatment discontinuation in all patients, stratified by drug Supplementary Table S3. Multivariable Cox proportional hazards model for treatment discontinuation in switch patients, stratified by drug. Supplementary Table S4. Detailed reasons for treatment discontinuation of each bDMARDs in 1,182 patients Supplementary Figure S1. Kaplan–Meier drug survival curves stratified by MTX co-treatment status (0 = without MTX, 1 = with MTX) in all patients for each bDMARDs: (a) IFX, (b) ETN, (c) TCZ, (d) ADA, (e) ABT, (f) CZP, (g) GLM, and (h) SAR. MTX status (0 or 1) is indicated in the legends of each graph. Log-rank p-values are provided in each panel. Supplementary Figure S2. Kaplan–Meier drug survival curves stratified by MTX co-treatment status (0 = without MTX, 1 = with MTX) in switch patients for each bDMARDs: (a) IFX, (b) ETN, (c) TCZ, (d) ADA, (e) ABT, (f) CZP, (g) GLM, and (h) SAR. MTX status (0 or 1) is indicated in the legends of each graph. Log-rank p-values are provided in each panel. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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09:58:14","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":156752,"visible":true,"origin":"","legend":"","description":"","filename":"220854093ad949a19e7ba95df4af04f11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7930489/v1/810b9be85b041ed129fc8330.xml"},{"id":98762166,"identity":"417f7cd1-e203-4edf-b5e1-9effb54c4151","added_by":"auto","created_at":"2025-12-22 09:58:19","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":178494,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7930489/v1/29cba236999c98988f623cd3.html"},{"id":98762147,"identity":"9f6e2b0e-1b3c-4f6d-815d-197b051290a2","added_by":"auto","created_at":"2025-12-22 09:58:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":180128,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier drug survival curves for each bDMARDs: (a) all 1,182 patients, (b) 784 naïve patients, and (c) 398 switch patients.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7930489/v1/c9f7f5c06e8780e11e6a1aa3.png"},{"id":98762146,"identity":"e59e3186-cfae-412e-bf67-502889bfcf87","added_by":"auto","created_at":"2025-12-22 09:58:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94936,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier drug survival curves stratified by MTX co-treatment status (0 = without MTX, 1 = with MTX) in naïve patients for each bDMARDs: (a) IFX, (b) ETN, (c) TCZ, (d) ADA, (e) ABT, (f) CZP, (g) GLM, and (h) SAR. MTX status (0 or 1) is indicated in the legends of each graph. Log-rank p-values are provided in each panel.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7930489/v1/af11e27bf1a1d5d7a8db204f.png"},{"id":98778257,"identity":"baa983ce-929e-41ba-bf63-674ec66c320a","added_by":"auto","created_at":"2025-12-22 12:29:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102502,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence of treatment discontinuation by reason for each bDMARDs: (a) inadequate response, (b) adverse events, and (c) non-adverse events. Cumulative incidence was estimated using the Fine \u0026amp; Gray competing risks model.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7930489/v1/0c8b24d8c3493f6dcf53318e.png"},{"id":99215829,"identity":"9f2422d7-ab97-4bf8-b4d5-00e52d260b91","added_by":"auto","created_at":"2025-12-30 08:56:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1300429,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7930489/v1/d56358c6-0b40-4e25-92f9-18311555f666.pdf"},{"id":98762149,"identity":"12d684f3-c63c-4a15-8094-93c31c77c27a","added_by":"auto","created_at":"2025-12-22 09:58:13","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":551835,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstractAug142025.png","url":"https://assets-eu.researchsquare.com/files/rs-7930489/v1/834d6d3f50b9df578d1166f4.png"},{"id":98780589,"identity":"44eac6d8-3a28-4157-8aaf-0bcb51769e2d","added_by":"auto","created_at":"2025-12-22 12:31:29","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":654993,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional File 1 legends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile name: Additional File 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFile format: docx\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTitle and description of data:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Table S1. Baseline characteristics of all patients (n = 1,184)\u003c/p\u003e\n\u003cp\u003eSupplementary Table S2. Multivariable Cox proportional hazards model for treatment discontinuation in all patients, stratified by drug\u003c/p\u003e\n\u003cp\u003eSupplementary Table S3. Multivariable Cox proportional hazards model for treatment discontinuation in switch patients, stratified by drug.\u003c/p\u003e\n\u003cp\u003eSupplementary Table S4. Detailed reasons for treatment discontinuation of each bDMARDs in 1,182 patients\u003c/p\u003e\n\u003cp\u003eSupplementary Figure S1. Kaplan–Meier drug survival curves stratified by MTX co-treatment status (0 = without MTX, 1 = with MTX) in all patients for each bDMARDs: (a) IFX, (b) ETN, (c) TCZ, (d) ADA, (e) ABT, (f) CZP, (g) GLM, and (h) SAR. MTX status (0 or 1) is indicated in the legends of each graph. Log-rank p-values are provided in each panel.\u003c/p\u003e\n\u003cp\u003eSupplementary Figure S2. Kaplan–Meier drug survival curves stratified by MTX co-treatment status (0 = without MTX, 1 = with MTX) in switch patients for each bDMARDs: (a) IFX, (b) ETN, (c) TCZ, (d) ADA, (e) ABT, (f) CZP, (g) GLM, and (h) SAR. MTX status (0 or 1) is indicated in the legends of each graph. Log-rank p-values are provided in each panel.\u003c/p\u003e","description":"","filename":"SupplementaryTableFigures.251023.docx","url":"https://assets-eu.researchsquare.com/files/rs-7930489/v1/c07940940bbf7be35718d663.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Five-year drug survival and discontinuation reasons for eight biological disease-modifying antirheumatic drugs for rheumatoid arthritis: A retrospective analysis of 1,182 patients from the Niigata Orthopedic Surgery Rheumatoid Arthritis Database (NOSRAD)","fulltext":[{"header":"Background","content":"\u003cp\u003eRheumatoid arthritis (RA), a systemic autoimmune disease characterized by synovial inflammation, culminates in progressive joint destruction [1]. The prevalence of RA varies from approximately 0.2\u0026ndash;0.5% among many nations [2]. Recent advances in the elucidation of the immunological mechanisms involved in the onset and progression of RA\u0026mdash;particularly the roles of pro-inflammatory cytokines\u0026mdash;have substantially deepened our understanding of its pathophysiology [3, 4].\u003c/p\u003e \u003cp\u003eIn Japan, biological disease-modifying antirheumatic drugs (bDMARDs) were introduced in 2003, ushering in a paradigm shift in RA management [5\u0026ndash;7]. Although bDMARDs exhibit high efficacy, their drug survival and the reasons for discontinuation may be influenced by multiple factors, including patient characteristics, regional contexts, and health-care delivery systems. While numerous multicenter studies have addressed this topic [8, 9], long-term, consistent real-world, single institution data remain limited [10].\u003c/p\u003e \u003cp\u003eContinuity of care of patients with RA, afforded by treatment in a single institution under the same attending physician, reduces outpatient attrition and enables a stable collection of long-term clinical data. However, our prefecture, like many regional areas in Japan, faces a chronic shortage of board-certified rheumatologists [11, 12].\u003c/p\u003e \u003cp\u003eIn this study, we utilized the Niigata Orthopedic Surgery Rheumatoid Arthritis Database (NOSRAD) to retrospectively analyze the drug survival and discontinuation reasons of eight bDMARDs administered to patients with RA treated in our department. By clarifying real-world treatment patterns, we aim to inform the development of more effective therapeutic strategies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy design, setting, and population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NOSRAD is an ongoing, observational, single-center registry that prospectively collects clinical data on every patient with RA treated in the Department of Orthopedic Surgery at Niigata University. For this retrospective cohort study, we screened all consecutive patients registered in NOSRAD between 1 May 2001 and 31 August 2022 (n = 1,517).\u003c/p\u003e\n\u003cp\u003eInclusion criteria\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eAge \u0026ge; 18 years at the index date.\u003c/li\u003e\n \u003cli\u003eFulfilment of either the 1987 revised American College of Rheumatology (ACR) criteria or the 2010 ACR/European League Against Rheumatism (EULAR) classification criteria for RA [13, 14].\u003c/li\u003e\n \u003cli\u003eReceipt of at least one approved dose of any of the following eight bDMARDs: infliximab (IFX), etanercept (ETN), tocilizumab (TCZ), adalimumab (ADA), abatacept (ABT), golimumab (GLM), certolizumab pegol (CZP) or sarilumab (SAR); intravenous and subcutaneous formulations were analyzed together.\u003c/li\u003e\n \u003cli\u003eComplete data on baseline demographics, disease characteristics, previous conventional synthetic DMARD (csDMARD) exposure, and comorbidities.\u003c/li\u003e\n \u003cli\u003eAt least one follow-up visit recorded after the index bDMARD administration.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eExclusion criteria\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eCurrent treatment using any targeted synthetic DMARD (n = 230);\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDiagnosis of spondyloarthritis or other inflammatory arthritides (n = 14);\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMissing values on any key variable (n = 91);\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOff-label bDMARD dosing or single, unconfirmed exposure lasting \u0026lt; 1 month.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAfter the exclusions, the final analytic cohort comprised 1,182 patients. Patients who initiated their first bDMARD constituted the na\u0026iuml;ve cohort (n = 784) and those who switched from at least one prior bDMARD formed the switch cohort (n = 398). Follow-up continued from the first documented dose until permanent drug discontinuation, death, loss to follow-up (\u0026gt; 12 months without contact), or 31 August 2022, whichever occurred first.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTreatment and follow-up\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTherapy was administered in accordance with the clinical practice guidelines of the Japan College of Rheumatology and was managed by six board-certified rheumatologists (NK, KA, NK, TM, JF, and KY). After bDMARD initiation, patients attended the orthopedic outpatient clinic every 4\u0026ndash;12 weeks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOutcome measures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary endpoint was the five-year drug survival rate for each bDMARD. Drug survival was defined retrospectively as the interval from the first administration of a bDMARD to permanent discontinuation. Secondary endpoints included (i) comparison of drug survival according to concomitant methotrexate (MTX) use and (ii) identification of factors associated with treatment discontinuation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExplanatory variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCandidate variables were sex, age, disease duration, baseline 28-joint Disease Activity Score using erythrocyte sedimentation rate (DAS28-ESR), concomitant MTX, and concomitant prednisolone (PSL). Reasons for discontinuation were categorized as inadequate response (including primary and secondary); adverse events (infection, pulmonary, liver, skin disorders, cardiovascular disease, malignant tumor, and others); or non-adverse events (remission or good response, patient preference, transfer to another hospital, other reasons, and unknown). Physicians were allowed to cite only one reason for the discontinuation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics were summarized using descriptive statistics. Categorical variables were compared using the chi-square test; continuous variables were compared using the Kruskal\u0026ndash;Wallis test, with multiple comparisons adjusted by the Steel\u0026ndash;Dwass method. Drug survival curves were estimated by the Kaplan\u0026ndash;Meier method and compared with the log-rank test. Cumulative incidence functions for each discontinuation reason were derived from a competing-risks model and compared with the Fine \u0026amp; Gray test.\u003c/p\u003e\n\u003cp\u003eWe used Cox proportional hazards model, stratified by drug type, to analyze factors influencing treatment discontinuation. Explanatory variables were sex, age, disease duration, DAS28-ESR, and concomitant use of MTX and PSL. The proportional hazards assumption was assessed with Schoenfeld residuals. Bonferroni adjustment was applied to control the family-wise error rate arising from multiple comparisons.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed by using EZR (Saitama Medical Centre, Jichi Medical University, Saitama, Japan) [15], and a two-sided p value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eBaseline characteristics of the na\u0026iuml;ve cohort (n\u0026thinsp;=\u0026thinsp;784) are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Significant inter-drug differences were observed for sex, age, disease duration, DAS28-ESR, concomitant MTX use, concomitant PSL use, and PSL dose (p\u0026thinsp;=\u0026thinsp;.040, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, p\u0026thinsp;=\u0026thinsp;.002, and p\u0026thinsp;=\u0026thinsp;.012, respectively; Kruskal\u0026ndash;Wallis test). The mean age of patients receiving ABT was 70.4 years, significantly higher than that of patients treated with IFX (55.1 years; p\u0026thinsp;\u0026lt;\u0026thinsp;.001), ETN (56.4 years; p\u0026thinsp;=\u0026thinsp;.002), or TCZ (56.7 years; p\u0026thinsp;=\u0026thinsp;.006), according to the Steel\u0026ndash;Dwass test. MTX dose did not differ among the patients regardless of the drugs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of na\u0026iuml;ve patients (n\u0026thinsp;=\u0026thinsp;784)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIFX\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=64)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eETN\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=217)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTCZ\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=253)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADA\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eABT\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=89)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCZP\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSAR\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51 (79.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e183 (84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e194 (76.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43 (69.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69 (77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54 (87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20 (76.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e56.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e68.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAS28-ESR, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX use, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59 (92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134 (62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48 (77.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32 (36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47 (67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17 (65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX dose (mg/week), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSL use, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113 (52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSL dose (mg/day), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIFX, infliximab; ETN, etanercept; TCZ, tocilizumab; ADA, adalimumab; ABT, abatacept; GLM, golimumab; CZP, certolizumab pegol; SAR, sarilumab; DAS28-ESR, the 28-joint Disease Activity Score with erythrocyte sedimentation rate; MTX, methotrexate; PSL, prednisolone; SD, standard deviation.\u003c/p\u003e \u003cp\u003eBaseline characteristics of the switch cohort (n\u0026thinsp;=\u0026thinsp;398) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Significant differences were found among the patients based on the drugs in age, disease duration, DAS28-ESR, concomitant MTX use, PSL dose, and use as a second- or fourth-line agent (p\u0026thinsp;\u0026lt;\u0026thinsp;.001, p\u0026thinsp;=\u0026thinsp;.020, p\u0026thinsp;=\u0026thinsp;.002, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, p\u0026thinsp;=\u0026thinsp;.032, p\u0026thinsp;=\u0026thinsp;.006, and p\u0026thinsp;=\u0026thinsp;.028, respectively; Kruskal\u0026ndash;Wallis test). No significant differences were detected in sex, MTX dose, concomitant PSL use, or use as a third-line agent. The mean age of ABT-treated patients was 70.1 years, significantly exceeding that of patients receiving ETN (55.1 years; p\u0026thinsp;\u0026lt;\u0026thinsp;.001); TCZ (59.2 years; p\u0026thinsp;\u0026lt;\u0026thinsp;.001); ADA (54.6 years; p\u0026thinsp;\u0026lt;\u0026thinsp;.001); or CZP (56.3 years; p\u0026thinsp;=\u0026thinsp;.010), according to the Steel\u0026ndash;Dwass test. GLM users were also older (67.3 years) than those on ETN (55.1 years; p\u0026thinsp;=\u0026thinsp;.019) or ADA (54.6 years; p\u0026thinsp;=\u0026thinsp;.047), according to the Steel\u0026ndash;Dwass test.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the switch patients (n\u0026thinsp;=\u0026thinsp;398)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIFX\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eETN\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=63)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTCZ\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=137)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADA\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eABT\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCZP\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSAR\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53 (84.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111 (81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17 (70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23 (69.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37 (77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.1\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e56.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e61.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAS28-ESR, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX use, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43 (68.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19 (79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX dose (mg/week), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSL use, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30 (53.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25 (52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSL dose (mg/day), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd bio, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90 (65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23 (69.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd bio, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≧4th bio, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIFX, infliximab; ETN, etanercept; TCZ, tocilizumab; ADA, adalimumab; ABT, abatacept; GLM, golimumab; CZP, certolizumab pegol; SAR, sarilumab; DAS28-ESR, the 28-joint Disease Activity Score with erythrocyte sedimentation rate; MTX, methotrexate; PSL, prednisolone; bio, biologic agent; SD, standard deviation.\u003c/p\u003e \u003cp\u003eIn the overall cohort (n\u0026thinsp;=\u0026thinsp;1,182), significant inter-drug differences were detected in age, disease duration, DAS28-ESR, concomitant MTX use, concomitant PSL use, PSL dose, and use as first-, second-, third-, or fourth-line therapy (all p\u0026thinsp;\u0026lt;\u0026thinsp;.001; however, PSL use p\u0026thinsp;=\u0026thinsp;.005 and PSL dose p\u0026thinsp;=\u0026thinsp;.001; Kruskal\u0026ndash;Wallis test), whereas sex and MTX dose did not differ (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The mean age of ABT users was 70.3 years, significantly higher than that of patients treated with IFX (55.6 years; p\u0026thinsp;=\u0026thinsp;.004); ETN (56.1 years; p\u0026thinsp;\u0026lt;\u0026thinsp;.001); TCZ (57.6 years; p\u0026thinsp;\u0026lt;\u0026thinsp;.001); or ADA (56.9 years; p\u0026thinsp;=\u0026thinsp;.040), according to the Steel\u0026ndash;Dwass test.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDrug survival rates\u003c/h3\u003e\n\u003cp\u003eIn the overall cohort, TCZ showed the highest five-year drug survival (46.3%), followed by ABT (45.0%), ETN (32.2%), ADA (28.0%), CZP (25.2%), IFX (23.0%), and GLM (18.9%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). TCZ survival was significantly superior to that of IFX, ETN, ADA, GLM, and CZP (all p\u0026thinsp;\u0026le;\u0026thinsp;.001, Bonferroni-adjusted log-rank test). No significant difference was observed between TCZ and ABT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the na\u0026iuml;ve cohort, TCZ again demonstrated the highest five-year survival (50.8%), followed by ABT (46.6%), ETN (36.6%), CZP (33.0%), ADA (32.3%), IFX (25.0%), and GLM (22.6%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). TCZ survival exceeded that of IFX, ETN, and GLM (all p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Bonferroni-adjusted log-rank test).\u003c/p\u003e \u003cp\u003eIn the switch cohort, ABT achieved the best 5-year survival (42.6%), followed by TCZ (38.2%), ADA (18.3%), ETN (15.1%), CZP (17.3%), GLM (14.8%), and IFX (10.0%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). TCZ survival was higher than that of IFX and GLM (both p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Bonferroni-adjusted log-rank test), and ABT survival was higher than IFX (p\u0026thinsp;\u0026lt;\u0026thinsp;.001, Bonferroni-adjusted log-rank test).\u003c/p\u003e \u003cp\u003eStratified analyses revealed generally better survival in MTX-combination therapy than in monotherapy (Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). Notably, in the na\u0026iuml;ve cohort, ADA survival was significantly higher with MTX combination therapy (p\u0026thinsp;\u0026lt;\u0026thinsp;.002, Bonferroni-adjusted log-rank test) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRisk factors for drug discontinuation\u003c/h2\u003e \u003cp\u003eIn the Cox proportional hazards model stratified by drug type (strata), significant predictors of discontinuation in the na\u0026iuml;ve cohort were: sex (male vs female: hazard ratio [HR]\u0026thinsp;=\u0026thinsp;1.49, 95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;1.09\u0026ndash;2.02, p\u0026thinsp;=\u0026thinsp;.011); baseline DAS28-ESR (per unit decrease: HR\u0026thinsp;=\u0026thinsp;0.90, 95% CI\u0026thinsp;=\u0026thinsp;0.82\u0026ndash;0.99, p\u0026thinsp;=\u0026thinsp;.039); and concomitant MTX use (yes vs no: HR\u0026thinsp;=\u0026thinsp;0.73, 95% CI\u0026thinsp;=\u0026thinsp;0.55\u0026ndash;0.97, p\u0026thinsp;=\u0026thinsp;.028) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Cox proportional hazards model for treatment discontinuation in na\u0026iuml;ve patients, stratified by drug\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09\u0026ndash;2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAS28-ESR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDifferences between drugs were assessed using the Cox-P value.\u003c/p\u003e \u003cp\u003eHR, hazard ratio; 95%CI, 95% confidence interval; DAS28-ESR, the 28-joint Disease Activity Score with erythrocyte sedimentation rate; MTX, methotrexate; PSL, prednisolone.\u003c/p\u003e \u003cp\u003eIn the overall cohort, only baseline DAS28-ESR remained significant (HR\u0026thinsp;=\u0026thinsp;0.91, 95% CI\u0026thinsp;=\u0026thinsp;0.84\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;.015; Supplementary Table S2). No significant predictors emerged in the switch cohort (Supplementary Table S3). The proportional hazards assumption was satisfied by the Schoenfeld residuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eReasons for drug discontinuation\u003c/h2\u003e \u003cp\u003eOverall, 823 patients (69.6%) discontinued bDMARD. The reasons were inadequate response (n\u0026thinsp;=\u0026thinsp;320, 27.1%); adverse events (n\u0026thinsp;=\u0026thinsp;204, 17.3%); and non-adverse events (n\u0026thinsp;=\u0026thinsp;299, 25.3%) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Adverse events comprised infections (n\u0026thinsp;=\u0026thinsp;79, 6.7%); pulmonary disorders (n\u0026thinsp;=\u0026thinsp;12, 1.0%); liver disorders (n\u0026thinsp;=\u0026thinsp;9, 0.8%); skin disorders (n\u0026thinsp;=\u0026thinsp;28, 2.4%); cardiovascular disease (n\u0026thinsp;=\u0026thinsp;7, 0.6%); malignant tumor (n\u0026thinsp;=\u0026thinsp;15, 1.3%); and others (n\u0026thinsp;=\u0026thinsp;54, 4.6%). Non-adverse events included remission or good response (n\u0026thinsp;=\u0026thinsp;43, 3.6%); patient preference (n\u0026thinsp;=\u0026thinsp;33, 2.8%); transfer to another hospital (n\u0026thinsp;=\u0026thinsp;167, 14.1%); other reasons (n\u0026thinsp;=\u0026thinsp;11, 0.9%); and unknown (n\u0026thinsp;=\u0026thinsp;45, 3.8%) (Supplementary Table S4)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReasons for discontinuation of each bDMARDs among 1,182 patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIFX\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=74)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eETN\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=280)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTCZ\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=390)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADA\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=86)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eABT\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=145)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGLM\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=95)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCZP\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSAR\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e=59)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInadequate response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdverse events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-adverse events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eValues are presented as n (percent).\u003c/p\u003e \u003cp\u003ebDMARDs, biological disease-modifying antirheumatic drugs; IFX, infliximab; ETN, etanercept; TCZ, tocilizumab; ADA, adalimumab; ABT, abatacept; GLM, golimumab; CZP, certolizumab pegol; SAR, sarilumab.\u003c/p\u003e \u003cp\u003eThe drug-by-drug comparison revealed a significant difference in discontinuations attributable to inadequate response (p\u0026thinsp;\u0026lt;\u0026thinsp;.001, chi-square test). The proportions were highest for CZP (n\u0026thinsp;=\u0026thinsp;22, 41.5%); IFX (n\u0026thinsp;=\u0026thinsp;30, 40.5%); and ADA (n\u0026thinsp;=\u0026thinsp;33, 38.4%); followed by ETN (n\u0026thinsp;=\u0026thinsp;96, 34.3%); GLM (n\u0026thinsp;=\u0026thinsp;27, 28.4%); SAR (n\u0026thinsp;=\u0026thinsp;14, 23.7%); ABT (n\u0026thinsp;=\u0026thinsp;33, 22.8%); and TCZ (n\u0026thinsp;=\u0026thinsp;65, 16.7%).\u003c/p\u003e \u003cp\u003eThe absolute numbers and percentages of discontinuations of the following drugs due to adverse events were: CZP (n\u0026thinsp;=\u0026thinsp;13, 24.5%), IFX (n\u0026thinsp;=\u0026thinsp;15, 20.3%), ETN (n\u0026thinsp;=\u0026thinsp;56, 20.0%), ADA (n\u0026thinsp;=\u0026thinsp;15, 17.4%), GLM (n\u0026thinsp;=\u0026thinsp;16, 16.8%), TCZ (n\u0026thinsp;=\u0026thinsp;65, 16.7%), ABT (n\u0026thinsp;=\u0026thinsp;19, 13.1%), and SAR (n\u0026thinsp;=\u0026thinsp;5, 8.5%). Although a global chi-square test did not reach significance level, the distribution indicates relatively fewer adverse-event discontinuations with SAR and ABT.\u003c/p\u003e \u003cp\u003eDiscontinuations for non-adverse events\u0026mdash;including remission, patient preference, and transfer to another hospital\u0026mdash;also differed significantly among drugs (p\u0026thinsp;\u0026lt;\u0026thinsp;.001, chi-square). This was highest for ETN (n\u0026thinsp;=\u0026thinsp;96, 34.3%), followed by IFX (n\u0026thinsp;=\u0026thinsp;22, 29.7%), TCZ (n\u0026thinsp;=\u0026thinsp;99, 25.4%), GLM (n\u0026thinsp;=\u0026thinsp;24, 25.3%), ABT (n\u0026thinsp;=\u0026thinsp;27, 18.6%), ADA (n\u0026thinsp;=\u0026thinsp;16, 18.6%), SAR (n\u0026thinsp;=\u0026thinsp;8, 13.6%), and CZP (n\u0026thinsp;=\u0026thinsp;7, 13.2%).\u003c/p\u003e \u003cp\u003eCompeting-risk analysis demonstrated that discontinuation owing to inadequate response rose steeply during the first 2 years and more gradually thereafter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). At 5 years, the cumulative incidence was highest for CZP (42.2%), followed by ADA (35.9%), IFX (37.8%), GLM (28.0%), ETN (27.9%), ABT (24.5%), and lowest for TCZ (9.5%). The rate of TCZ was significantly lower than those of IFX (p\u0026thinsp;=\u0026thinsp;.004), ETN (p\u0026thinsp;=\u0026thinsp;.003), ADA (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), GLM (p\u0026thinsp;=\u0026thinsp;.009), CZP (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and SAR (p\u0026thinsp;=\u0026thinsp;.026), based on Bonferroni-adjusted log-rank test (p\u0026thinsp;\u0026lt;\u0026thinsp;.001 overall, Fine \u0026amp; Gray test).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDiscontinuations attributed to adverse events increased during the first year and then plateaued (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). At 5 years, the cumulative incidence was highest for CZP (21.3%), followed by GLM (18.3%), IFX (17.6%), ETN (15.8%), ADA (15.7%), TCZ (14.5%), ABT (12.8%), and lowest for SAR (8.8%).\u003c/p\u003e \u003cp\u003eFor non-adverse events, the 5-year cumulative incidence was highest for GLM (31.1%), followed by TCZ (24.8%), ETN (24.2%), IFX (21.6%), ABT (17.7%), ADA (17.4%), CZP (11.4%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, single-center retrospective cohort study, we systematically analyzed the five-year drug survival and discontinuation reasons for eight bDMARDs in 1,182 patients with RA in Japan. Although numerous multicenter studies and database analyses of drug persistence have been published [8, 9], investigations enrolling more than 1,000 patients at a single institution\u0026mdash;who were all managed by specialists in the same field (orthopedic rheumatology)\u0026mdash;and that examined the detailed discontinuation motives for each agent, are exceedingly rare [10]. This is considered the key novelty of the present work.\u003c/p\u003e \u003cp\u003eIn the overall and na\u0026iuml;ve cohorts, TCZ demonstrated the highest drug survival, and ABT also maintained favorable persistence. These trends are partly consistent with previous reports [10, 16\u0026ndash;22]. A notable clinical advantage of TCZ and ABT is that they remain effective without concomitant MTX, benefitting older adult patients or those unsuitable for combination therapy [19, 23]. Among tumor necrosis factor inhibitors (TNFi), ETN outlasts IFX and ADA [24, 25], with IFX often showing the poorest survival [24, 26], similar to the pattern observed in the present study.\u003c/p\u003e \u003cp\u003eConversely, certain TNFi such as IFX and GLM were often discontinued early owing to insufficient efficacy, suggesting that earlier switching to agents with different mechanisms may be beneficial. The unexpectedly high survival of ABT in the switch cohort indicates retained efficacy in refractory cases previously exposed to multiple bDMARDs, supporting mechanism-based switching strategies.\u003c/p\u003e \u003cp\u003eIn na\u0026iuml;ve patients on ADA, concomitant MTX significantly improved survival. Monoclonal-antibody TNFi are susceptible to anti-drug antibody formation, which undermines therapeutic efficacy [27, 28\u0026ndash;30]. ADA is a fully human monoclonal antibody; however, it does not completely escape immunogenicity. These findings underscore the importance of MTX-mediated suppression of anti-drug antibodies [16, 18, 21, 25, 31\u0026ndash;42].\u003c/p\u003e \u003cp\u003eIn the na\u0026iuml;ve cohort, Cox analysis identified male sex, lower baseline DAS28-ESR, and absence of MTX co-therapy as independent predictors of discontinuation. Unlike previous studies showing poorer persistence in females [43\u0026ndash;45], male sex remained a risk factor even after censoring discontinuations due to hospital transfer or patient preference (HR\u0026thinsp;=\u0026thinsp;1.54, 95% CI\u0026thinsp;=\u0026thinsp;1.09\u0026ndash;2.17, p\u0026thinsp;=\u0026thinsp;.013). Possible explanations for the early discontinuation in males include inefficacy or adverse events, relatively lower per-kilogram dosing with fixed-dose subcutaneous regimens in individuals with higher body mass index [46, 47], and enhanced immunogenicity associated with higher smoking rates [48, 49]. The higher discontinuation among patients with low disease activity likely reflects intentional cessation after achieving remission rather than therapeutic failure. As in previous studies, no MTX is a negative predictor of persistence [16, 24, 50, 51], and low MTX dosage has likewise been implicated [8, 16, 24, 51]. Japanese cohorts, with a mean MTX dose (7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3 mg/week in this study), often benefit from lower MTX doses than Western populations [52]. PSL co-therapy had no influence on survival [53].\u003c/p\u003e \u003cp\u003eAlthough inadequate effectiveness was the leading cause of discontinuation (27.1%), adverse events (17.3%) and non-adverse events (25.3%) were also substantial. Discontinuations due to hospital transfer or patient preference may reflect regional specialist maldistribution and referral patterns in the provincial areas. ABT had the lowest adverse-event discontinuation rate, consistent with previous reports of fewer episodes of severe infections and infusion reactions, suggesting suitability for older patients [16, 54, 55].\u003c/p\u003e \u003cp\u003eInadequate response discontinuations clustered within 2 years of initiation, while adverse events peaked in the first year, highlight the first two years as critical for bDMARD success and underscore the necessity for a treat-to-target strategy with timely assessment and switching [56].\u003c/p\u003e \u003cp\u003eThis study has limitations. First, it was a retrospective analysis of an observational registry data from a single tertiary center; therefore, selection bias, information bias, and limited generalizability to other institutions or regions cannot be excluded. Second, discontinuation decisions and explanations were clinician-dependent, not standardized. Third, baseline characteristics differed among drugs, and unmeasured confounding may persist despite adjustment. Fourth, minor dose modifications of bDMARDs, MTX, or PSL were not captured. Fifth, differences between intravenous and subcutaneous preparations and concomitant csDMARDs could not be fully ascertained. Sixth, CZP and SAR were approved relatively recently in Japan (2013 and 2017, respectively), explaining the smaller sample sizes that may have affected the estimates.\u003c/p\u003e \u003cp\u003eDespite these constraints, this study offers high-resolution real-world data through long-term follow-up, detailed discontinuation causes, and stratified analyses in a uniform treatment setting. Future multicenter prospective studies\u0026mdash;and, in particular, large-scale cohorts with greater sample sizes\u0026mdash;are essential to validate our findings, clarify dose\u0026ndash;response relationships, and incorporate immunological or biomarker data to guide personalized therapy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this single-center, long-term retrospective cohort of 1,182 patients with RA, we compared five-year drug survival and discontinuation reasons for eight bDMARDs. TCZ achieved the highest survival in biologic-na\u0026iuml;ve patients, whereas ABT ranked first among the switch patients. Transitioning from one bDMARD to another markedly reduced persistence overall. ABT\u0026mdash;used more frequently in older patients\u0026mdash;exhibited a comparatively low rate of adverse-event discontinuations, underscoring its clinical utility in older adult populations. Concomitant MTX significantly improved the survival of ADA. Male sex, lower baseline disease activity, and absence of MTX co-therapy emerged as potential risk factors for treatment cessation. Inadequate response was the leading cause of discontinuation, clustering within the first 2 years after initiation.\u003c/p\u003e \u003cp\u003eThese findings highlight the need to tailor both drug selection and continuation strategies to individual patient characteristics and emphasize vigilant management during the early treatment phase. Building on these real-world data, future multidimensional assessments and prospective studies are warranted to refine personalized therapeutic approaches in RA.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABT,\u0026nbsp;abatacept\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eACR,\u0026nbsp;American College of Rheumatology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eADA,\u0026nbsp;adalimumab\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ebDIMARDs,\u0026nbsp;biological disease-modifying antirheumatic drugs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCZP,\u0026nbsp;certolizumab pegol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eETN,\u0026nbsp;etanercept\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEULAR,\u0026nbsp;European League Against Rheumatism\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGLM,\u0026nbsp;golimumab\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIFX,\u0026nbsp;infliximab\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMTX,\u0026nbsp;methotrexate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNOSRAD,\u0026nbsp;Niigata Orthopaedic Surgery Rheumatoid Arthritis Database\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"\"\u003ePSL, prednisolone\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"\"\u003eRA, rheumatoid arthritis\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"\"\u003eSAR, sarilumab\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"\"\u003eTCZ, tocilizumab\u003c/span\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the institutional ethics review board (approval number: 2018 − 0377) and complied with the principles set forth in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman ethics and consent to participate declarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by the institutional ethics review board of Niigata University (approval number: 2018–0377) and conducted in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe requirement for informed consent was waived by the ethics committee because of the retrospective design of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.H. and N.K. wrote the main manuscript text. N.H. prepared Tables 1–4 and Figures 1–3. N.H. and Y.K. collected the patient data. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWu Y. Early detection of rheumatoid arthritis in rats and humans with 99mTc-3PRGD2 scintigraphy: imaging synovial neoangiogenesis. Oncotarget. 2017;8:5753\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarmona L, Cross M, Williams B, Lassere M, March L. Rheumatoid arthritis. Best Pract Res Clin Rheumatol. 2010;24:733\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKondo N, Kuroda T, Kobayashi D. Cytokine networks in the pathogenesis of rheumatoid arthritis. 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PLoS ONE. 2021;16:e0250877.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManeiro JR, Salgado E, Gomez-Reino JJ. Immunogenicity of monoclonal antibodies against tumor necrosis factor used in chronic immune-mediated Inflammatory conditions: systematic review and meta-analysis. JAMA Intern Med. 2013;173:1416\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen DY, Chen YM, Tsai WC, Tseng JC, Chen YH, Hsieh CW, et al. Significant associations of antidrug antibody levels with serum drug trough levels and therapeutic response of adalimumab and etanercept treatment in rheumatoid arthritis. Ann Rheum Dis. 2015;74:e16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlejandro Balsa R, Sanmarti Jos\u0026eacute;, Rosas, Martin V, Cabez A, G\u0026oacute;mez S, et al. Drug immunogenicity in patients with inflammatory arthritis and secondary failure to tumour necrosis factor inhibitor therapies: the REASON study. Rheumatology. 2018;57:688\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartelds GM, Wijbrandts CA, Nurmohamed MT, Stapel S, Lems WF, Aarden L, et al. Anti-infliximab and anti-adalimumab antibodies in relation to response to adalimumab in infliximab switchers and anti-tumour necrosis factor naive patients: a cohort study. Ann Rheum Dis. 2010;69:817\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh JA, Saag KG, Bridges SL Jr, Akl EA, Bannuru RR, Sullivan MC, et al. 2015 American College of Rheumatology Guideline for the treatment of rheumatoid arthritis. Arthritis Rheumatol. 2016;68:1\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka Y, Takeuchi T, Mimori T, Saito K, Nawata M, Kameda H, et al. Discontinuation of infliximab after attaining low disease activity in patients with rheumatoid arthritis: RRR (remission induction by Remicade in RA) study. Ann Rheum Dis. 2010;69:1286\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirata S, Saito K, Kubo S, Fukuyo S, Mizuno Y, Iwata S, et al. Discontinuation of adalimumab after attaining disease activity score 28-erythrocyte sedimentation rate remission in patients with rheumatoid arthritis (HONOR study): an observational study. Arthritis Res Ther. 2013;15:R135.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka Y, Hirata S, Kubo S, Fukuyo S, Hanami K, Sawamukai N, et al. Discontinuation of adalimumab after achieving remission in patients with established rheumatoid arthritis: 1-year outcome of the HONOR study. Ann Rheum Dis. 2015;74:389\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka Y, Yamanaka H, Ishiguro N, Miyasaka N, Kawana K, Hiramatsu K, et al. Adalimumab discontinuation in patients with early rheumatoid arthritis who were initially treated with methotrexate alone or in combination with adalimumab: 1 year outcomes of the HOPEFUL-2 study. RMD Open. 2016;2:e000189.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtsumi T, Tanaka Y, Yamamoto K, Takeuchi T, Yamanaka H, Ishiguro N, et al. Clinical benefit of 1-year certolizumab pegol (CZP) add-on therapy to methotrexate treatment in patients with early rheumatoid arthritis was observed following CZP discontinuation: 2-year results of the C-OPERA study, a phase III randomised trial. Ann Rheum Dis. 2017;76:1348\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamanaka H, Nagaoka S, Lee SK, Bae SC, Kasama T, Kobayashi H, et al. Discontinuation of etanercept after achievement of sustained remission in patients with rheumatoid arthritis who initially had moderate disease activity-results from the ENCOURAGE study, a prospective, international, multicenter randomized study. Mod Rheumatol. 2016;26:651\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmolen JS, Landew\u0026eacute; RBM, Bijlsma JWJ, Burmester GR, Dougados M, Kerschbaumer A, et al. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2019 update. Ann Rheum Dis. 2020;79:685\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFavalli EG, Pregnolato F, Biggioggero M, Becciolini A, Penatti AE, Marchesoni A, et al. Twelve-year retention rate of first-line tumor necrosis factor inhibitors in rheumatoid arthritis: Real-life data from a local registry. Arthritis Care Res (Hoboken). 2016;68:432\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGomez-Reino JJ, Rodriguez-Lozano C, Campos-Fernandez C, Montoro M, Descalzo M\u0026Aacute;, Carmona L, BIOBADASER 2.0 Study Group. Change in the discontinuation pattern of tumour necrosis factor antagonists in Rheumatoid arthritis over 10 years: data from the Spanish registry BIOBADASER 2.0. Ann Rheum Dis. 2012;71:382\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003el\u0026rsquo;Ami MJ, Kneepkens EL, Nurmohamed MT, Krieckaert CL, Visman IM, Wolbink GJ. Long-term treatment response in Rheumatoid arthritis patients starting adalimumab or etanercept with or without concomitant methotrexate. Clin Exp Rheumatol. 2017;35:431\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLauridsen KB, Duch KS, Mortensen AS, Cordtz R, Kristensen S, Lund ML, et al. 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Body mass index and treatment survival in patients with RA starting treatment with TNFα-inhibitors: long-term follow-up in the real-life METEOR registry. RMD Open. 2020;6(2):e001203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Wolff L, Arends S, Brouwer E, Bootsma H, Spoorenberg A. High BMI is associated with lower TNF-α inhibitor serum trough levels and higher disease activity in patients with axial spondyloarthritis. Arthritis Res Ther. 2023;25(1):202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. WHO report on the global tobacco epidemic 2023 \u0026ndash; Country profile: Japan.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichelsen B, Berget KT, Kavanaugh A, Haugeberg G. Association between TNFi anti-drug antibodies, smoking, and disease activity in patients with inflammatory arthritis: Results from a Norwegian cross-sectional observational study. 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RMD Open. 2017;3:e000363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSafy-Khan M, Jacobs JWG, dehair MJH, Welsing PM, Edwardes MD, Teitsma XM, et al. Effect on efficacy and safety trial outcomes of also enrolling patients on ongoing glucocorticoid therapy in Rheumatoid arthritis clinical trials of tocilizumab or adalimumab or methotrexate monotherapy. Ann Rheum Dis. 2020;79:460\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzen G, Pedro S, Schumacher R, Simon TA, Michaud K. Safety of abatacept compared with other bio- logic and conventional synthetic disease-modifying antirheumatic drugs in patients with rheumatoid arthritis: data from an observational study. Arthritis Res Ther. 2019;21:141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYun H, Xie F, Delzell E, Levitan EB, Chen L, Lewis JD, et al. Comparative risk of hospitalized infection associated with biologic agents in rheumatoid arthritis patients enrolled in Medicare. Arthritis Rheumatol. 2016;68:56\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmolen JS, Aletaha D, Bijlsma JWJ, Breedveld FC, Boumpas D, et al. Treating rheumatoid arthritis to target: recommendations of an international task force. Ann Rheum Dis. 2010;69:631\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biological disease-modifying antirheumatic drugs, Discontinuation, Drug survival, Japan, Niigata Orthopedic Surgery Rheumatoid Arthritis database, Retrospective study, Rheumatoid arthritis, Cohort study, Inadequate response, Adverse events","lastPublishedDoi":"10.21203/rs.3.rs-7930489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7930489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eContinuity of care of patients with rheumatoid arthritis, afforded by treatment in a single institution under the same attending physician, reduces outpatient attrition and enables a stable collection of long-term clinical data. However, our prefecture, like many regional areas in Japan, faces a chronic shortage of board-certified rheumatologists. Therefore, we aimed to determine the five-year drug-survival and discontinuation reasons for eight biological disease-modifying antirheumatic drugs (bDMARDs) for rheumatoid arthritis using Japan\u0026rsquo;s Niigata Orthopedic Surgery Rheumatoid Arthritis Database.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBetween May 2001 and August 2022, 1,182 patients were retrospectively analyzed. Na\u0026iuml;ve (n\u0026thinsp;=\u0026thinsp;784) and switch (n\u0026thinsp;=\u0026thinsp;398) patients initiated their first or subsequent bDMARD, respectively. The primary end-point was five-year drug-survival per bDMARD while the secondary analyses assessed methotrexate (MTX) co-therapy, discontinuation risk factors, and cumulative incidence of discontinuation reasons. Kaplan\u0026ndash;Meier curves, Cox (stratified by drug), and Fine \u0026amp; Gray models were applied.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNa\u0026iuml;ve cohort showed significant inter-drug differences in sex, age, disease duration, 28-joint Disease Activity Score with erythrocyte-sedimentation-rate (DAS28-ESR), MTX or prednisolone (PSL) co-therapy, and PSL dose. Switch cohort differed by age, disease duration, DAS28-ESR, MTX co-therapy, PSL dose, and treatment line. Five-year drug-survival in na\u0026iuml;ve cohort ranged from tocilizumab (50.8%) to golimumab (22.6%); in switch cohort, from abatacept (42.6%) to infliximab (10.0%). Cox analysis in na\u0026iuml;ve cohort found male sex, lower baseline DAS28-ESR, and no MTX predicted discontinuation, explained by inadequate response (27.1%), adverse events (17.3%), and non-adverse events (25.3%).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eEarly, individualized drug selection and dose optimization are crucial to maximize long-term bDMARD effectiveness before switching.\u003c/p\u003e","manuscriptTitle":"Five-year drug survival and discontinuation reasons for eight biological disease-modifying antirheumatic drugs for rheumatoid arthritis: A retrospective analysis of 1,182 patients from the Niigata Orthopedic Surgery Rheumatoid Arthritis Database (NOSRAD)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 09:58:08","doi":"10.21203/rs.3.rs-7930489/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8848d75-5eb8-46a2-872d-671946a478ba","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T08:55:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 09:58:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7930489","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7930489","identity":"rs-7930489","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}