Comparative Effectiveness of Rituximab in Treatment-Naïve vs. Switch Patients with Multiple Sclerosis

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Almatrafi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7118548/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Journal of Neuroimmune Pharmacology → Version 1 posted 12 You are reading this latest preprint version Abstract Background Rituximab, an anti-CD20 monoclonal antibody, is increasingly used off-label in multiple sclerosis (MS), particularly where access to approved B-cell therapies is limited. This retrospective cohort study assessed its real-world effectiveness and safety in treatment-naïve versus switch patients at a single center in Saudi Arabia. Methods We retrospectively analyzed data from 34 MS patients treated with rituximab at Security Forces Hospital, Makkah, between January 2018 and December 2024. Patients were categorized as treatment-naïve (n = 16) or treatment-switch (n = 18). Outcomes included annualized relapse rate (ARR), Expanded Disability Status Scale (EDSS), MRI activity, adverse events, and no evidence of disease activity (NEDA) status at 12-month follow-up. Results Rituximab significantly reduced ARR in both groups (naïve: 1.44 ± 0.73 to 0.06 ± 0.25, p < 0.001; switch: 2.67 ± 1.46 to 0.17 ± 0.38, p < 0.001). NEDA was achieved in 93.8% of naïve and 83.3% of switch patients (p = 0.60). EDSS remained stable or improved in most cases (p = 0.94). No new T2 lesions were observed on MRI in any patient. Adverse events were minimal and manageable, with one mild infusion reaction and one case of asymptomatic lymphopenia. Post-treatment lymphocyte counts were lower in switch patients (p = 0.03), but no severe infections occurred. Conclusion In this retrospective cohort, rituximab demonstrated significant short-term efficacy in relapse reduction and disease stabilization in both treatment-naïve and switch MS patients, with a favorable safety profile. However, these findings must be interpreted cautiously due to the small sample size, retrospective design, and baseline group differences. Larger prospective studies are warranted to confirm long-term outcomes. Registration: The study protocol was approved by the Institutional Review Board (IRB) of Security Forces Hospital Makkah (SFHM), registered with the National BioMedical Ethics Committee under King Abdulaziz City for Science and Technology (Registration number: HAP-02-K-052). IRB approval number: [0749-081024], dated November 2024. Multiple sclerosis Rituximab Retrospective cohort Treatment-naïve Disease-modifying therapy Figures Figure 1 Introduction Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disorder affecting approximately 2.8 million people globally, with rising prevalence particularly in women and high-income countries [1]. Clinically, MS is characterized by episodes of neurological dysfunction—such as motor weakness, visual disturbances, and sensory loss—as well as progressive disability and cognitive impairment that can significantly impact daily functioning and quality of life [2]. Although MS was traditionally viewed as a T-cell-mediated condition, growing evidence has underscored the central role of B cells in its pathogenesis. These cells infiltrate the central nervous system (CNS), accumulate in lesions and cerebrospinal fluid (CSF), and form oligoclonal bands and ectopic lymphoid follicles, particularly in progressive MS [3]. B cells in MS patients exhibit a proinflammatory profile, producing cytokines like IL-6, GM-CSF, and LTα3, which drive disease activity—profiles that tend to normalize following B-cell-depleting therapy (BCDT) [3]. Clonal links between peripheral and CNS B cells suggest that most B-cell maturation occurs outside the CNS before CD20+ B cells migrate into perivascular and subarachnoid spaces, supporting the rationale for BCDT [4]. BCDT has emerged as a highly effective strategy in relapsing MS, offering strong suppression of disease activity with a favorable safety profile. Traditionally, MS therapy followed an escalation model—beginning with lower-efficacy disease-modifying drugs (DMDs) and moving to higher-efficacy options if needed. Recently, however, a top-down approach has gained favor, emphasizing early use of potent therapies to limit long-term disability [1]. BCDT is particularly advantageous for patients who cannot tolerate conventional high-efficacy agents or those at elevated risk for progressive multifocal leukoencephalopathy (PML), a concern with treatments like natalizumab and fingolimod. Rituximab and similar agents offer a high-efficacy alternative with a comparatively lower PML risk, making them suitable for younger patients with high anti-JC virus antibody titers but otherwise low infection risk [5]. Rituximab, a chimeric monoclonal antibody targeting CD20, was initially developed for hematologic malignancies and autoimmune disorders. Despite not being formally approved for MS, rituximab has been increasingly used off-label due to its demonstrated efficacy in reducing relapse rates, MRI lesion burden, and disability progression [4]. Approved anti-CD20 agents such as ocrelizumab and ofatumumab have since entered clinical use for relapsing MS, offering regulatory-approved alternatives with similar mechanisms of action [6]. However, rituximab remains widely used in real-world settings due to its lower cost, established efficacy, and comparable safety profile, particularly in regions or healthcare systems where access to newer agents may be limited or financially prohibitive. The growing off-label use of rituximab raises important regulatory and ethical considerations, especially given the availability of approved alternatives [6]. Nonetheless, in many healthcare environments, rituximab remains a practical and effective choice. This study focuses on rituximab to evaluate its real-world performance at a center where it is routinely used, aiming to clarify its utility across treatment-naïve and switch patient populations. Methods Study Design and Setting: This was a retrospective cohort study conducted at Security Forces Hospital, Makkah (SFHM), evaluating medical records of patients with multiple sclerosis (MS) treated with rituximab between January 2018 and December 2024. The study adhered to Good Clinical Practice (GCP) guidelines, the Declaration of Helsinki, and followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist [7]. Patient Selection: Eligible patients were diagnosed with MS based on the 2017 revised McDonald criteria [8] and received at least one dose of rituximab during the study period. Inclusion criteria required complete clinical and radiological data, defined as having full documentation of baseline demographics, clinical evaluations (including EDSS), relapse history, and at least two MRIs (pre- and post-treatment) available for review. Patients were excluded if they had less than 12 months of follow-up, incomplete records, or concurrent systemic autoimmune or severe illnesses (e.g., SLE, sarcoidosis, malignancy). Patient Grouping Participants were divided into two groups: Treatment-Naïve Group : Patients who received rituximab as first-line therapy. Switch Group : Patients who switched to rituximab from another disease-modifying therapy (DMT) due to inadequate response, side effects, or patient preference. Rituximab Administration Protocol: Rituximab was administered according to institutional protocols: 1000 mg IV on days 0 and 14, followed by maintenance doses of 1000 mg every 6–12 months depending on clinical and MRI findings. Premedications included corticosteroids and antihistamines. Patients were monitored during and after infusion for adverse events. MRI Protocol and Evaluation All patients underwent brain and spinal MRI at baseline and after 12 months using a standardized protocol on a 1.5 Tesla scanner. Sequences included T2-weighted, FLAIR, and post-contrast T1-weighted imaging. Spinal MRIs were routinely performed in both groups. All imaging was evaluated by a single board-certified neuroradiologist blinded to clinical data to reduce inter-observer variability. Evaluations included presence of new or enhancing lesions, changes in T2 lesion load, and descriptive assessment of brain atrophy by ventricular enlargement or sulcal widening. Brain atrophy was not included as a formal outcome due to the lack of volumetric tools limiting more granular insight into neurodegeneration. Clinical and Safety Data Collection Demographic and clinical data collected included age, sex, disease duration, comorbidities, and reasons for initiating rituximab, prior DMTs (type, duration, reason for switch). Clinical outcomes included: EDSS scores at baseline and 12 months post-treatment. Annualized relapse rate (ARR) over 12 months before and after rituximab initiation. Achievement of NEDA: absence of relapses, EDSS worsening (≥1.0 point increase if baseline ≤5.5 or ≥0.5 point if >5.5), and new/enhancing MRI lesions. Safety outcomes included infusion reactions, infections (including opportunistic), lymphocyte counts, and ESR levels. Immunoglobulin levels and vaccination status were not systematically monitored. Outcome Definitions Treatment Success: Defined as achieving NEDA at 12 months. Treatment Failure: Defined as occurrence of relapse, EDSS worsening, or new/enhancing MRI lesions. Statistical Analysis: Normality of continuous variables was tested using the Shapiro–Wilk test. Paired t-tests or Wilcoxon signed-rank tests were used for within-group comparisons. Chi-square or Fisher’s exact tests were used for categorical comparisons, depending on sample size. Between-group differences were assessed using t-tests or Mann–Whitney U tests. Although propensity score matching was not feasible due to small sample size, multivariate logistic regression was employed to adjust for disease duration, EDSS, ARR, and treatment group, identifying predictors of NEDA. Receiver operating characteristic (ROC) curves assessed the predictive utility of baseline EDSS and ARR for treatment success. No adjustment was made for multiple comparisons, which may increase type I error. All statistical tests were two-tailed with a significance threshold of p < 0.05. Analyses were performed using SPSS (v25.0). Ethical Approval The study protocol was reviewed and approved by the Institutional Review Board (IRB) of Security Forces Hospital Makkah, registered under the National Committee of Biomedical Ethics (HAP-02-K-052), with approval number 0749-081024. Although the IRB approval occurred in late 2024, all patient data were retrospectively collected and de-identified, with consent waived by the IRB due to the non-interventional, observational design. Results This retrospective cohort study included 34 MS patients: 16 treatment-naïve and 18 treatment-switch. Baseline characteristics and clinical data completeness are summarized in Table 1. Baseline Characteristics The treatment-naïve and switch groups were comparable in age and sex distribution. However, the switch group had a significantly longer disease duration (6.56 ± 3.67 vs. 3.71 ± 2.33 years; p = 0.02) and a higher prevalence of comorbidities, including hypothyroidism, which approached significance (p = 0.09) [Table 1]. Hypothyroidism in three patients was secondary to autoimmune thyroiditis as per endocrinology consultation. These cases were considered stable and well-managed on levothyroxine, without systemic autoimmune overlap. These baseline imbalances underscore potential confounding factors influencing treatment outcomes. Efficacy Outcomes Both groups experienced significant reductions in ARR (naïve: from 1.44 ± 0.73 to 0.06 ± 0.25; switch: from 2.67 ± 1.46 to 0.17 ± 0.38; both p < 0.001). All naïve patients and 94.4% of switch patients were relapse-free post-treatment. The proportion achieving NEDA was 93.8% in the naïve group and 83.3% in the switch group (p = 0.60). Despite a numerical difference, this was not statistically significant. EDSS remained stable or improved in most patients at follow-up (p = 0.94). Individual improvements included reduction from EDSS 6 to 5 in one naïve patient and 3 to 2 in a switch patient, demonstrating clinical recovery potential [Table 2]. MRI Outcomes : No new T2 lesions were observed post-treatment in any patient. Baseline spinal lesions were more frequent in the switch group (83.3% vs. 68.8%, p = 0.43), but follow-up showed no radiological progression. Gadolinium-enhancing lesions were significantly reduced in both groups (p < 0.001). Safety Outcomes Rituximab was well tolerated. One patient in each group experienced a mild infusion reaction. Post-treatment lymphocyte counts were significantly lower in the switch group (1.82 ± 0.68 vs. 2.40 ± 0.69; p = 0.03). No cases of severe lymphopenia or opportunistic infections occurred. One switch patient developed a non-serious infection, and no patient discontinued treatment [Table 3]. Associations with Sociodemographic and Clinical Variables Lower baseline ARR was significantly associated with treatment success (p = 0.026). Other variables, including age, sex, disease duration, and MRI activity, did not significantly predict outcomes. A trend toward better outcomes in females was noted but did not reach statistical significance (p = 0.16) [Table 4]. Correlation Between Baseline Characteristics and Treatment Response Baseline EDSS negatively correlated with change in EDSS (ρ = -0.40, p = 0.01), suggesting greater improvements in patients with higher initial disability. Similarly, higher baseline ARR predicted greater reductions in relapse activity (ρ = -0.45, p = 0.008) [Table 5]. Predictors of Treatment Response Lower baseline ARR and EDSS were associated with higher likelihood of NEDA. Multivariate regression identified baseline ARR (OR 2.93, p = 0.03) and EDSS (OR 2.78, p = 0.04) as significant predictors. ROC analysis showed good discrimination for ARR (AUC = 0.80, p = 0.037), but EDSS was not predictive (AUC = 0.61, p = 0.437). No significant difference in outcomes was observed based on age group (<35 vs. ≥35 years). Naïve status and disease duration were not significant after adjustment, highlighting the predictive value of disease activity markers at treatment initiation [Table 6]. Subgroup and Sensitivity Analyses Subgroup analysis confirmed comparable outcomes across age brackets [Table 7]. There was no significant effect of disease duration or MRI baseline characteristics on treatment success. The definition of treatment success as NEDA was consistently applied, and statistical models did not identify treatment group (naïve vs. switch) as an independent predictor of outcome. The ROC curve for baseline ARR (AUC = 0.80) demonstrated good predictive ability. However, the ROC for EDSS was not statistically significant (p = 0.437), indicating limited standalone utility for predicting response [Table 8, Figure 1]. Discussion This retrospective cohort study provides real-world evidence on the short-term effectiveness and safety of rituximab in patients with multiple sclerosis (MS), comparing outcomes between treatment-naïve individuals and those who switched from other disease-modifying therapies (DMTs). The significant reduction in annualized relapse rate (ARR), stability of Expanded Disability Status Scale (EDSS), and absence of new T2 lesions across both groups underscore rituximab’s clinical utility in diverse patient populations. However, findings must be interpreted with caution given the study's methodological limitations, including small sample size and retrospective design. Interpretation of Key Findings Both treatment-naïve and switch patients exhibited significant reductions in ARR and maintained clinical stability during follow-up. Notably, 93.8% of treatment-naïve patients achieved NEDA compared to 83.3% in the switch group, although this difference was not statistically significant. This aligns with previous observational studies suggesting rituximab’s superior performance when used as an early intervention [11,12], possibly due to limited immune reprogramming from prior therapies. The consistent suppression of radiological activity, with no new T2 lesions reported in either group, emphasizes rituximab’s potent anti-inflammatory effect [13, 14]. A single patient in the switch group experienced a relapse without radiological change, underscoring the complexity of defining treatment failure in MS and the need for multidimensional outcome metrics and suggesting possible residual inflammatory activity despite adequate radiological suppression. This dissociation supports the concept that subclinical disease progression may persist in patients with prior treatment exposure, highlighting the advantage of earlier B-cell-targeted intervention [15]. Imaging and Predictive Outcomes MRI data revealed that most patients, particularly those in the treatment-naïve group, remained free from new or enhancing lesions. Although brain atrophy progression was descriptively assessed, the primary focus was on active lesion formation, which rituximab effectively suppressed. These imaging findings complement functional outcomes, with EDSS remaining stable or improving in most patients. Notably, individual patients in both groups experienced clinically meaningful improvements in EDSS, suggesting not only prevention of progression but potential functional recovery [13, 16]. Baseline ARR and EDSS were explored as potential predictors of achieving NEDA. While baseline ARR demonstrated significant predictive value, baseline EDSS yielded mixed findings: in some analyses it appeared predictive, but in others (e.g., AUC = 0.61, p = 0.437), its utility was limited. This discrepancy underscores the need for caution in interpreting these variables as robust predictors of treatment success [17]. Comparison with Other Anti-CD20 Agents and Justification for Rituximab Use Although ocrelizumab and ofatumumab are approved anti-CD20 therapies for relapsing MS, rituximab remains widely used off-label due to several pragmatic factors. First, its lower cost and broader global availability make it a practical option in resource-limited settings [18]. Second, its established role in treating other autoimmune diseases has resulted in a well-documented safety record. Spelman et al. (2018) and Andersen et al. (2022) showed that rituximab and ocrelizumab have comparable effectiveness in real-world and registry-based studies [14, 19]. While rituximab and ocrelizumab share a CD20-depleting mechanism, differences in molecular structure (chimeric vs. humanized antibodies) and immune reconstitution profiles may affect outcomes. Ocrelizumab offers regulatory approval and standardized dosing, but rituximab’s longer history and cost-effectiveness support its off-label use in appropriate settings. Differential B-cell repopulation kinetics between naïve and switch patients may underlie some response heterogeneity. While not formally licensed for MS, rituximab provides a cost-effective, accessible, and clinically validated alternative, particularly when access to approved agents is limited due to economic or regulatory constraints. Regulatory considerations, as discussed by Laurson-Doube et al. (2021), stress that off-label rituximab use must be ethically justified by clinical need, cost-benefit analyses, and local health policy [19]. Recent real-world studies have reinforced rituximab’s efficacy and safety, positioning it as a viable alternative to newer anti-CD20 agents [16, 17, 20]. Additionally, a pooled analysis by Alcalá et al. (2022) highlighted that rituximab shares similar disease control metrics with ocrelizumab across a wide MS population, although direct comparative trials remain limited [18]. Safety and Tolerability of Rituximab Consistent with prior literature, our study confirms rituximab's favorable safety profile. Only two adverse events were reported: a mild infusion-related rash and a case of lymphopenia, neither of which necessitated treatment discontinuation. Post-treatment lymphocyte levels were significantly lower in the switch group compared to the naïve group (p = 0.03), though no severe lymphopenia-related complications were observed. Luna et al. (2020) and Salzer et al. (2016) highlighted a lower rate of infection-related complications with rituximab compared to other high-efficacy agents [16, 13]. Winkelmann et al. (2016) and Jalkh et al. (2021) also noted that infection risks are manageable, particularly with appropriate monitoring and patient selection [20, 21]. Monitoring of immunoglobulin levels or vaccine response was not performed, which remains a limitation in safety profiling and should be prioritized in future studies. Clinical Implications Despite its limitations, this study reinforces the role of rituximab as a viable treatment option for relapsing MS in real-world settings, particularly where regulatory or financial barriers restrict access to approved anti-CD20 therapies [19]. The enhanced efficacy seen in treatment-naïve patients suggests a potential advantage for early B-cell depletion [11,13]. However, these findings should be confirmed through larger, prospective, multicenter studies incorporating long-term follow-up, immunologic monitoring, and direct comparisons with ocrelizumab or other high-efficacy agents. Future research should also evaluate rituximab’s role in progressive MS and assess patient-reported outcomes, quality of life, and cost-effectiveness metrics. Establishing clear criteria for treatment success—including radiological and functional recovery endpoints—may further guide individualized therapy choices in MS care. Limitations This study has several important limitations. First, its retrospective design introduces inherent risks of selection and information bias, potentially affecting the accuracy and completeness of clinical and radiological data. Second, the small sample size (n = 34), along with the modest imbalance between the treatment-naïve and switch groups (16 vs. 18), limits statistical power and may increase the risk of type II errors, as illustrated by the non-significant difference in NEDA rates (p = 0.60). Third, the 12-month follow-up duration, while adequate for assessing short-term efficacy and safety, may be insufficient to capture long-term outcomes, including sustained disease control, delayed adverse events, opportunistic infections, or malignancy risk. Although MRI assessments were standardized and conducted by a single radiologist, subtle discrepancies in protocol timing and image quality could affect outcome sensitivity. Fourth, while multivariate analysis was used to adjust for key confounders, residual confounding likely remains. Notably, naïve and switch groups differed significantly in disease duration and comorbidity profiles, which may have influenced observed effects. No adjustment for multiple comparisons was made, which may increase the likelihood of type I error. Fifth, the absence of longitudinal immunoglobulin data, vaccine response tracking, and extended safety follow-up limits the scope of safety assessment. Finally, the single-center nature and exclusion of patients with uncontrolled comorbidities constrain external validity. Conclusion Rituximab demonstrated significant effectiveness in reducing relapse rates, suppressing radiological disease activity, and stabilizing or improving functional status in both treatment-naïve and previously treated patients with multiple sclerosis. Its short-term safety profile was favorable, and no serious adverse events were observed. These real-world findings support rituximab's potential as a viable therapeutic option, particularly in settings where access to approved anti-CD20 therapies is limited. Nonetheless, the study’s retrospective design, small sample size, and baseline heterogeneity warrant cautious interpretation. Prospective, multicenter studies with longer follow-up and larger cohorts are needed to validate these preliminary observations and better define rituximab’s long-term safety and effectiveness in MS management. Abbreviations Adverse Event ARR: Annualized Relapse Rate CI: Confidence Interval CTCAE: Common Terminology Criteria for Adverse Events DMT: Disease-Modifying Therapy EDSS: Expanded Disability Status Scale ESR: Erythrocyte Sedimentation Rate FU: Follow-Up Gd+: Gadolinium-Enhancing (lesions) HTN: Hypertension MRI: Magnetic Resonance Imaging MS: Multiple Sclerosis NEDA: No Evidence of Disease Activity OR: Odds Ratio PKD: Polycystic Kidney Disease RR: Relapsing-Remitting (MS phenotype) SD: Standard Deviation Declarations Funding Declaration: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution H.E: Study design, Manuscript writing, literature review, editingM. A. A, M. U. A, R.M, M. A, T. A: Data CollectionR. A, R. E, A. A, A.T: Study design, Manuscript review Data Availability the datasets used and analyzed during the current study are available from the corresponding author on reasonable request. 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Nat Rev Neurol 12(4):217–233 Jalkh G, Abi Nahed R, Macaron G, Rensel M (2021) Safety of Newer Disease Modifying Therapies in Multiple Sclerosis. Vaccines 9(1):12 Tables Table 1. Baseline Characteristics of MS Patients by Treatment Group Characteristic Naïve Group (n = 16) Switch Group (n = 18) Statistical Test p-value Age, mean ± SD (years) 34.88 ± 10.26 38.72 ± 9.31 t-test=1.15 0.26 Sex, n (%) Chi-square 0.11 0.75 Male 8 (50.0%) 8 (44.4%) Female 8 (50.0%) 10 (55.6%) Disease duration, mean ± SD (years) 3.71 ± 2.33 6.56 ± 3.67 U 2.57 0.02* Rituximab duration, mean ± SD (months) 21.38 ± 18.56 15.67 ± 8.38 U 0.23 0.82 Baseline EDSS, mean ± SD 1.81 ± 1.56 1.67 ± 1.61 U = 0.50 0.61 Comorbidities, n (%) FE No reported comorbidity 13 (81.3%) 10 (55.6%) 2.56 0.11 Hypothyroidism 0 (0.0%) 3 (16.7%) 2.93 0.09* - Bronchial Asthma 0 (0.0) 1 (5.6) 0.92 1.00 - Sjögren’s Syndrome 0 (0.0) 1 (5.6) 0.92 1.00 - Hepatitis B 1 (6.3) 0 (0.0) 1.16 0.47 - Obesity 0 (0.0) 1 (5.6) 0.92 1.00 - Hypertension 1 (6.3) 0 (0.0) 1.16 0.47 - Epilepsy 1 (6.3) 1 (5.6) 0.007 1.00 - Uterine Fibroma 0 (0.0) 1 (5.6) 0.92 1.00 - Severe Cervical Stenosis 0 (0.0) 1 (5.6) 0.92 1.00 - Depression 0 (0.0) 1 (5.6) 0.92 1.00 - Polycystic Kidney Disease 0 (0.0) 1 (5.6) 0.92 1.00 Note: EDSS = Expanded Disability Status Scale; SD = Standard Deviation. *Bolded p-values indicate statistical significance (p < 0.05), Mann–Whitney U = U Test, FE = Fisher’s Exact Test Table 2. Efficacy Outcomes Before and After Rituximab Treatment Outcome Naïve Group (n = 16) Switch Group (n = 18) Statistical Test p-value Relapse Rate Pre-treatment (mean ± SD) 1.44 ± 0.73 2.67 ± 1.46 U 2.69 0.007* Post-treatment (mean ± SD) 0.06 ± 0.25 0.17 ± 0.38 U 0.93 0.35 Within-group change (p-value) <0.001 <0.001 Wilcoxon EDSS Score Baseline (mean ± SD) 1.81 ± 1.56 1.67 ± 1.61 U 0.50 0.61 Follow-up (mean ± SD) 1.44 ± 1.50 1.39 ± 1.54 U 0.07 0.94 Within-group change (p-value) 0.03 0.24 Wilcoxon MRI Findings Spinal lesions (%) 11 (68.8%) 15 (83.3%) FE 1.0 0.43 New T2 lesions (%) 0 (0.0%) 0 (0.0%) Not Applicable — Within-group change (p-value) <0.001 <0.001 McNemar NEDA (%) 15 (93.8%) 15 (83.3%) FE = 0.88 0.60 Note: EDSS = Expanded Disability Status Scale; NEDA = No Evidence of Disease Activity. *Bolded p-values indicate significance (p < 0.05), Mann–Whitney U = U Test, FE = Fisher’s Exact Test Table 3: Safety Outcomes Safety Parameter Naïve Group (n = 16) Switch Group (n = 18) Statistical Test p-value Lymphocytes - Pre-treatment (mean ± SD) 2.23 ± 0.83 1.77 ± 0.62 U 1.86 0.06 - Post-treatment (mean ± SD) 2.40 ± 0.69 1.82 ± 0.68 U 2.16 *0.03 ESR (Normal/High), n (%) 13 (81.3%)/3 (18.7%) 13 (72.2%)/5 (27.8%) FE 0.48 0.69 Infusion Reactions, n (%) 1 (6.3%) 1 (5.6%) FE 0.007 1.00 Infections, n (%) 0 (0.0%) 1 (5.6%) FE = 0.92 0.34 Treatment Discontinuations, n (%) 0 (0.0%) 0 (0.0%) Not Applicable — Note: ESR = Erythrocyte Sedimentation Rate, Mann–Whitney U = U Test, FE = Fisher’s Exact Test, *Bolded p-values indicate statistical significance (p < 0.05), Table 4: Improvement of Cases on Rituximab by Sociodemographic and Clinical Data Variable Treatment Success (n = 29) Treatment Failure (n = 5) Statistical Test (p-value) Age (years) - Mean ± SD 36.86 ± 10.36 37.20 ± 6.53 t-test: 0.0 (p = 1.0) - Range 18–54 28–44 Sex FE Test - Male 12 (41.4%) 4 (80.0%) 2.55 (p = 0.16) - Female 17 (58.6%) 1 (20.0%) Disease Duration (years) U Test - Mean ± SD 5.39 ± 3.58 4.20 ± 1.79 U = 0.44 (p = 0.66) - Range 1–15 2–6 Duration of Rituximab (months) U Test - Mean ± SD 18.34 ± 15.11 18.40 ± 7.80 U = 0.72 (p = 0.47) - Range 6–66 8–24 Treatment Type FE Test - Naïve 15 (51.7%) 1 (20.0%) 1.72 (p = 0.34) - Switched 14 (48.3%) 4 (80.0%) MRI Activity - Baseline Spinal Affection FE Test - Yes 21 (72.4%) 5 (100.0%) 1.80 (p = 0.31) - No 8 (27.6%) 0 (0.0%) - Baseline Enhancement FE Test - Yes 16 (55.2%) 2 (40.0%) χ² = 0.39 (p = 0.35) - No 13 (44.8%) 3 (60.0%) Relapse Rate (Pre-Treatment) U Test - Mean ± SD 1.80 ± 1.23 3.20 ± 1.30 U = 2.23 (* p = 0.026 ) - Median (Range) 1 (1–5) 3 (2–5) Lymphocytes (Pre-Treatment) U Test - Mean ± SD 1.95 ± 0.76 2.17 ± 0.78 U = 0.66 (p = 0.51) - Median (Range) 1.9 (0.55–4.25) 2.2 (1.14–3.19) Note: SD = Standard Deviation, Mann–Whitney U = U Test, χ² = Chi-Square Test, FE = Fisher’s Exact Test , MRI = Magnetic Resonance Imaging, *Bolded p-values indicate statistical significance (p < 0.05), . Table 5: Correlation Between Baseline Variables and Treatment Response Variable EDSS Change r (p) ARR Change r (p) NEDA Achievement r(p) Disease Duration (years) 0.26 (p = 0.14) 0.12 (p = 0.51) 0.01 (p = 0.94) Baseline EDSS 0.32 (p = 0.06) -0.12 (0.49) 0.32 (0.06) Baseline Relapse Rate -0.25 (p = 0.15) 0.97 (p < 0.001) 0.40 (* p = 0.02 ) Baseline Lymphocytes 0.29 (p = 0.10) -0.33 (p = 0.05) -0.02 (p = 0.92) Note: EDSS = Expanded Disability Status Scale, ARR = Annualized Relapse Rate, NEDA = No Evidence of Disease Activity, ρ = Spearman’s Rank Correlation Coefficient, *Bolded p-values indicate statistical significance (p < 0.05). Table 6: Multivariate Logistic Regression for Predictors of NEDA Variable OR (95% CI) p-value Naïve Status 1.18 (0.4–13.2) 0.50 Disease Duration (years) 0.87 (0.72–3.68) 0.51 Baseline EDSS 2.78 (1.56–6.57) 0.04* Baseline ARR 2.93 (1.66–7.91) 0.03* Note: OR = Odds Ratio, CI = Confidence Interval, EDSS = Expanded Disability Status Scale, ARR = Annualized Relapse Rate, NEDA = No Evidence of Disease Activity, *Bolded p-values indicate statistical significance (p < 0.05). Table 7: Subgroup Comparison by Age (<35 vs. ≥35 Years) Outcome <35 Years (n = 14) ≥35 Years (n = 20) p-value NEDA (%) 13/14 (92.9%) 17/20 (85.0%) 0.57 Post-Treatment ARR (Mean ± SD) 0.07 ± 0.26 0.15 ± 0.38 0.42 EDSS Improvement (%) 4/14 (28.6%) 5/20 (25.0%) 0.81 Note: NEDA = No Evidence of Disease Activity, ARR = Annualized Relapse Rate, EDSS = Expanded Disability Status Scale Table 8: ROC curve analysis for Baseline ARR and EDSS for prediction of successful treatment Variable Cut-off Value Sensitivity (%) Specificity (%) AUC (95% CI) p-value Baseline ARR >2.5 relapses/year 100 55.2 0.80 (0.63–0.96) 0.037* Baseline EDSS ≤2.0 points 60.0 75.9 0.61 (0.30–0.92) 0.437 Note: ARR = Annualized Relapse Rate, EDSS = Expanded Disability Status Scale, AUC = Area Under the Curve, CI = Confidence Interval, *Bolded p-values indicate statistical significance (p < 0.05). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Journal of Neuroimmune Pharmacology → Version 1 posted Editorial decision: Revision requested 05 Dec, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviews received at journal 16 Aug, 2025 Reviewers agreed at journal 15 Aug, 2025 Reviewers agreed at journal 15 Aug, 2025 Reviewers invited by journal 15 Aug, 2025 Editor assigned by journal 10 Aug, 2025 Submission checks completed at journal 24 Jul, 2025 First submitted to journal 14 Jul, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7118548","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501871509,"identity":"75e108bc-0d85-4871-a21c-6a41eb5cf604","order_by":0,"name":"Hosna Elshony","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACAwQz+eCDD0CKjZ14LWnJhjNAWpiJ15JjJs0DoglpMWdvf/bgB0OdPH97grGxza9t8nzMDIwfPubg1mLZc8bcsIfhsOGMMw8SH+f23TZsY2Zglpy5DY/DbuSwSfAwHGBsuJFw2Di35zYjUAsbMy9eLenPJP8w1NnPv5HYJm3Zc9ueCC0JIF8zJ264kcwmzfDjdiJhLWfOmEnLGBxO3njmGbNhb8Pt5DZmxmb8fjne/kzyTUWd7bzj+R8f/Phz23Z+e/PBDx/xaIFqhNKMbWCygZB6ZPCHFMWjYBSMglEwUgAADlNSCK59jt0AAAAASUVORK5CYII=","orcid":"","institution":"Menoufia University","correspondingAuthor":true,"prefix":"","firstName":"Hosna","middleName":"","lastName":"Elshony","suffix":""},{"id":501871511,"identity":"40c56483-7fe9-49de-9e6e-d95161758d99","order_by":1,"name":"Rakan Almuhanna","email":"","orcid":"","institution":"Security Forces Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rakan","middleName":"","lastName":"Almuhanna","suffix":""},{"id":501871512,"identity":"25fad0f9-bafe-4965-a48e-520f9baa2aa1","order_by":2,"name":"Abdulaziz Al-Ghmadi","email":"","orcid":"","institution":"Security Forces Hospital","correspondingAuthor":false,"prefix":"","firstName":"Abdulaziz","middleName":"","lastName":"Al-Ghmadi","suffix":""},{"id":501871514,"identity":"da5309ca-7131-47dd-ab7c-6620f0191982","order_by":3,"name":"Maha K. Almatrafi","email":"","orcid":"","institution":"Umm al-Qura University","correspondingAuthor":false,"prefix":"","firstName":"Maha","middleName":"K.","lastName":"Almatrafi","suffix":""},{"id":501871516,"identity":"451bde50-ea88-486b-b022-89d18cdd5ffe","order_by":4,"name":"Mohammed Ahmed Ashshi","email":"","orcid":"","institution":"Umm al-Qura University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"Ahmed","lastName":"Ashshi","suffix":""},{"id":501871517,"identity":"468192d3-12fe-4273-9127-ea93113105e4","order_by":5,"name":"Mohammed Uthman Almatani","email":"","orcid":"","institution":"Umm al-Qura University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"Uthman","lastName":"Almatani","suffix":""},{"id":501871519,"identity":"07a12490-5089-48f5-b8b5-d9a85c4dd623","order_by":6,"name":"Thamer Al-Ghamdi","email":"","orcid":"","institution":"Security Forces Hospital","correspondingAuthor":false,"prefix":"","firstName":"Thamer","middleName":"","lastName":"Al-Ghamdi","suffix":""},{"id":501871520,"identity":"f13ac6b3-66f6-4c0b-a504-83e315d6277e","order_by":7,"name":"Abdullah Tawakul","email":"","orcid":"","institution":"Umm al-Qura University","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"","lastName":"Tawakul","suffix":""},{"id":501871521,"identity":"05496c50-f400-498e-8e56-ce5947378bb1","order_by":8,"name":"Rasha Elsaadawy","email":"","orcid":"","institution":"Menoufia University","correspondingAuthor":false,"prefix":"","firstName":"Rasha","middleName":"","lastName":"Elsaadawy","suffix":""},{"id":501871523,"identity":"25b7139a-bc97-4df2-9155-8a890b4dadcb","order_by":9,"name":"Rabia Muddassir","email":"","orcid":"","institution":"Security Forces Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rabia","middleName":"","lastName":"Muddassir","suffix":""}],"badges":[],"createdAt":"2025-07-14 08:08:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7118548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7118548/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11481-026-10288-9","type":"published","date":"2026-04-13T15:59:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89977363,"identity":"83c271ff-bc14-4f7e-9330-a314025611e4","added_by":"auto","created_at":"2025-08-27 06:09:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93860,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve analysis for Baseline ARR and EDSS for prediction of successful treatment\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7118548/v1/fe97fc479d6b47c925bb9e44.png"},{"id":107352482,"identity":"fc159fed-43ae-40d6-9328-a97b91c004db","added_by":"auto","created_at":"2026-04-20 16:14:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1305769,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7118548/v1/20da096e-2462-4c38-9c32-d4a9db34c3dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Effectiveness of Rituximab in Treatment-Naïve vs. Switch Patients with Multiple Sclerosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disorder affecting approximately 2.8 million people globally, with rising prevalence particularly in women and high-income countries [1]. Clinically, MS is characterized by episodes of neurological dysfunction\u0026mdash;such as motor weakness, visual disturbances, and sensory loss\u0026mdash;as well as progressive disability and cognitive impairment that can significantly impact daily functioning and quality of life [2].\u003c/p\u003e\n\u003cp\u003eAlthough MS was traditionally viewed as a T-cell-mediated condition, growing evidence has underscored the central role of B cells in its pathogenesis. These cells infiltrate the central nervous system (CNS), accumulate in lesions and cerebrospinal fluid (CSF), and form oligoclonal bands and ectopic lymphoid follicles, particularly in progressive MS [3]. B cells in MS patients exhibit a proinflammatory profile, producing cytokines like IL-6, GM-CSF, and LT\u0026alpha;3, which drive disease activity\u0026mdash;profiles that tend to normalize following B-cell-depleting therapy (BCDT) [3]. Clonal links between peripheral and CNS B cells suggest that most B-cell maturation occurs outside the CNS before CD20+ B cells migrate into perivascular and subarachnoid spaces, supporting the rationale for BCDT [4].\u003c/p\u003e\n\u003cp\u003eBCDT has emerged as a highly effective strategy in relapsing MS, offering strong suppression of disease activity with a favorable safety profile. Traditionally, MS therapy followed an escalation model\u0026mdash;beginning with lower-efficacy disease-modifying drugs (DMDs) and moving to higher-efficacy options if needed. Recently, however, a top-down approach has gained favor, emphasizing early use of potent therapies to limit long-term disability [1]. BCDT is particularly advantageous for patients who cannot tolerate conventional high-efficacy agents or those at elevated risk for progressive multifocal leukoencephalopathy (PML), a concern with treatments like natalizumab and fingolimod. Rituximab and similar agents offer a high-efficacy alternative with a comparatively lower PML risk, making them suitable for younger patients with high anti-JC virus antibody titers but otherwise low infection risk [5].\u003c/p\u003e\n\u003cp\u003eRituximab, a chimeric monoclonal antibody targeting CD20, was initially developed for hematologic malignancies and autoimmune disorders. Despite not being formally approved for MS, rituximab has been increasingly used off-label due to its demonstrated efficacy in reducing relapse rates, MRI lesion burden, and disability progression [4]. Approved anti-CD20 agents such as ocrelizumab and ofatumumab have since entered clinical use for relapsing MS, offering regulatory-approved alternatives with similar mechanisms of action [6]. However, rituximab remains widely used in real-world settings due to its lower cost, established efficacy, and comparable safety profile, particularly in regions or healthcare systems where access to newer agents may be limited or financially prohibitive. The growing off-label use of rituximab raises important regulatory and ethical considerations, especially given the availability of approved alternatives [6]. Nonetheless, in many healthcare environments, rituximab remains a practical and effective choice. This study focuses on rituximab to evaluate its real-world performance at a center where it is routinely used, aiming to clarify its utility across treatment-na\u0026iuml;ve and switch patient populations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Setting:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a retrospective cohort study conducted at Security Forces Hospital, Makkah (SFHM), evaluating medical records of patients with multiple sclerosis (MS) treated with rituximab between January 2018 and December 2024. The study adhered to Good Clinical Practice (GCP) guidelines, the Declaration of Helsinki, and followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist\u0026nbsp;[7].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Selection:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEligible patients were diagnosed with MS based on the 2017 revised McDonald criteria [8] and received at least one dose of rituximab during the study period. Inclusion criteria required complete clinical and radiological data, defined as having full documentation of baseline demographics, clinical evaluations (including EDSS), relapse history, and at least two MRIs (pre- and post-treatment) available for review. Patients were excluded if they had less than 12 months of follow-up, incomplete records, or concurrent systemic autoimmune or severe illnesses (e.g., SLE, sarcoidosis, malignancy).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Grouping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants were divided into two groups:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eTreatment-Na\u0026iuml;ve Group\u003c/strong\u003e: Patients who received rituximab as first-line therapy.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSwitch Group\u003c/strong\u003e: Patients who switched to rituximab from another disease-modifying therapy (DMT) due to inadequate response, side effects, or patient preference.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eRituximab Administration Protocol:\u0026nbsp;\u003c/strong\u003eRituximab was administered according to institutional protocols: 1000 mg IV on days 0 and 14, followed by maintenance doses of 1000 mg every 6\u0026ndash;12 months depending on clinical and MRI findings. Premedications included corticosteroids and antihistamines. Patients were monitored during and after infusion for adverse events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI Protocol and Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAll patients underwent brain and spinal MRI at baseline and after 12 months using a standardized protocol on a 1.5 Tesla scanner. Sequences included T2-weighted, FLAIR, and post-contrast T1-weighted imaging. Spinal MRIs were routinely performed in both groups. All imaging was evaluated by a single board-certified neuroradiologist blinded to clinical data to reduce inter-observer variability.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEvaluations included presence of new or enhancing lesions, changes in T2 lesion load, and descriptive assessment of brain atrophy by ventricular enlargement or sulcal widening. Brain atrophy was not included as a formal outcome due to the lack of volumetric tools limiting more granular insight into neurodegeneration.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eClinical and Safety Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic and clinical data collected included age, sex, disease duration, comorbidities, and reasons for initiating rituximab, prior DMTs (type, duration, reason for switch).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical outcomes included:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eEDSS scores at baseline and 12 months post-treatment.\u003c/li\u003e\n \u003cli\u003eAnnualized relapse rate (ARR) over 12 months before and after rituximab initiation.\u003c/li\u003e\n \u003cli\u003eAchievement of NEDA: absence of relapses, EDSS worsening (\u0026ge;1.0 point increase if baseline \u0026le;5.5 or \u0026ge;0.5 point if \u0026gt;5.5), and new/enhancing MRI lesions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSafety outcomes included infusion reactions, infections (including opportunistic), lymphocyte counts, and ESR levels. Immunoglobulin levels and vaccination status were not systematically monitored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome Definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eTreatment Success:\u0026nbsp;\u003c/strong\u003eDefined as achieving NEDA at 12 months.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTreatment Failure:\u0026nbsp;\u003c/strong\u003eDefined as occurrence of relapse, EDSS worsening, or new/enhancing MRI lesions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNormality of continuous variables was tested using the Shapiro\u0026ndash;Wilk test. Paired t-tests or Wilcoxon signed-rank tests were used for within-group comparisons. Chi-square or Fisher\u0026rsquo;s exact tests were used for categorical comparisons, depending on sample size. Between-group differences were assessed using t-tests or Mann\u0026ndash;Whitney U tests.\u003c/p\u003e\n\u003cp\u003eAlthough propensity score matching was not feasible due to small sample size, multivariate logistic regression was employed to adjust for disease duration, EDSS, ARR, and treatment group, identifying predictors of NEDA. Receiver operating characteristic (ROC) curves assessed the predictive utility of baseline EDSS and ARR for treatment success. No adjustment was made for multiple comparisons, which may increase type I error.\u003c/p\u003e\n\u003cp\u003eAll statistical tests were two-tailed with a significance threshold of p \u0026lt; 0.05. Analyses were performed using SPSS (v25.0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Institutional Review Board (IRB) of Security Forces Hospital Makkah, registered under the National Committee of Biomedical Ethics (HAP-02-K-052), with approval number 0749-081024. Although the IRB approval occurred in late 2024, all patient data were retrospectively collected and de-identified, with consent waived by the IRB due to the non-interventional, observational design.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis retrospective cohort study included 34 MS patients: 16 treatment-na\u0026iuml;ve and 18 treatment-switch. Baseline characteristics and clinical data completeness are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe treatment-na\u0026iuml;ve and switch groups were comparable in age and sex distribution. However, the switch group had a significantly longer disease duration (6.56 \u0026plusmn; 3.67 vs. 3.71 \u0026plusmn; 2.33 years; p = 0.02) and a higher prevalence of comorbidities, including hypothyroidism, which approached significance (p = 0.09) [Table 1]. Hypothyroidism in three patients was secondary to autoimmune thyroiditis as per endocrinology consultation. These cases were considered stable and well-managed on levothyroxine, without systemic autoimmune overlap. These baseline imbalances underscore potential confounding factors influencing treatment outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEfficacy Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth groups experienced significant reductions in ARR (na\u0026iuml;ve: from 1.44 \u0026plusmn; 0.73 to 0.06 \u0026plusmn; 0.25; switch: from 2.67 \u0026plusmn; 1.46 to 0.17 \u0026plusmn; 0.38; both p \u0026lt; 0.001). All na\u0026iuml;ve patients and 94.4% of switch patients were relapse-free post-treatment. The proportion achieving NEDA was 93.8% in the na\u0026iuml;ve group and 83.3% in the switch group (p = 0.60). Despite a numerical difference, this was not statistically significant.\u003c/p\u003e\n\u003cp\u003eEDSS remained stable or improved in most patients at follow-up (p = 0.94). Individual improvements included reduction from EDSS 6 to 5 in one na\u0026iuml;ve patient and 3 to 2 in a switch patient, demonstrating clinical recovery potential [Table 2].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI Outcomes\u003c/strong\u003e:\u0026nbsp;No new T2 lesions were observed post-treatment in any patient. Baseline spinal lesions were more frequent in the switch group (83.3% vs. 68.8%, p = 0.43), but follow-up showed no radiological progression. Gadolinium-enhancing lesions were significantly reduced in both groups (p \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSafety Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRituximab was well tolerated. One patient in each group experienced a mild infusion reaction. Post-treatment lymphocyte counts were significantly lower in the switch group (1.82 \u0026plusmn; 0.68 vs. 2.40 \u0026plusmn; 0.69; p = 0.03). No cases of severe lymphopenia or opportunistic infections occurred. One switch patient developed a non-serious infection, and no patient discontinued treatment [Table 3].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations with Sociodemographic and Clinical Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLower baseline ARR was significantly associated with treatment success (p = 0.026). Other variables, including age, sex, disease duration, and MRI activity, did not significantly predict outcomes. A trend toward better outcomes in females was noted but did not reach statistical significance (p = 0.16) [Table 4].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Between Baseline Characteristics and Treatment Response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline EDSS negatively correlated with change in EDSS (\u0026rho; = -0.40, p = 0.01), suggesting greater improvements in patients with higher initial disability. Similarly, higher baseline ARR predicted greater reductions in relapse activity (\u0026rho; = -0.45, p = 0.008) [Table 5].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictors of Treatment Response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLower baseline ARR and EDSS were associated with higher likelihood of NEDA. Multivariate regression identified baseline ARR (OR 2.93, p = 0.03) and EDSS (OR 2.78, p = 0.04) as significant predictors. ROC analysis showed good discrimination for ARR (AUC = 0.80, p = 0.037), but EDSS was not predictive (AUC = 0.61, p = 0.437). No significant difference in outcomes was observed based on age group (\u0026lt;35 vs. \u0026ge;35 years). Na\u0026iuml;ve status and disease duration were not significant after adjustment, highlighting the predictive value of disease activity markers at treatment initiation [Table 6].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup and Sensitivity Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analysis confirmed comparable outcomes across age brackets [Table 7]. There was no significant effect of disease duration or MRI baseline characteristics on treatment success. The definition of treatment success as NEDA was consistently applied, and statistical models did not identify treatment group (na\u0026iuml;ve vs. switch) as an independent predictor of outcome.\u003c/p\u003e\n\u003cp\u003eThe ROC curve for baseline ARR (AUC = 0.80) demonstrated good predictive ability. However, the ROC for EDSS was not statistically significant (p = 0.437), indicating limited standalone utility for predicting response [Table 8, Figure 1].\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective cohort study provides real-world evidence on the short-term effectiveness and safety of rituximab in patients with multiple sclerosis (MS), comparing outcomes between treatment-na\u0026iuml;ve individuals and those who switched from other disease-modifying therapies (DMTs). The significant reduction in annualized relapse rate (ARR), stability of Expanded Disability Status Scale (EDSS), and absence of new T2 lesions across both groups underscore rituximab\u0026rsquo;s clinical utility in diverse patient populations. However, findings must be interpreted with caution given the study\u0026apos;s methodological limitations, including small sample size and retrospective design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation of Key Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth treatment-na\u0026iuml;ve and switch patients exhibited significant reductions in ARR and maintained clinical stability during follow-up. Notably, 93.8% of treatment-na\u0026iuml;ve patients achieved NEDA compared to 83.3% in the switch group, although this difference was not statistically significant. This aligns with previous observational studies suggesting rituximab\u0026rsquo;s superior performance when used as an early intervention [11,12], possibly due to limited immune reprogramming from prior therapies. The consistent suppression of radiological activity, with no new T2 lesions reported in either group, emphasizes rituximab\u0026rsquo;s potent anti-inflammatory effect [13, 14].\u003c/p\u003e\n\u003cp\u003eA single patient in the switch group experienced a relapse without radiological change, underscoring the complexity of defining treatment failure in MS and the need for multidimensional outcome metrics and suggesting possible residual inflammatory activity despite adequate radiological suppression. This dissociation supports the concept that subclinical disease progression may persist in patients with prior treatment exposure, highlighting the advantage of earlier B-cell-targeted intervention [15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImaging and Predictive Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMRI data revealed that most patients, particularly those in the treatment-na\u0026iuml;ve group, remained free from new or enhancing lesions. Although brain atrophy progression was descriptively assessed, the primary focus was on active lesion formation, which rituximab effectively suppressed. These imaging findings complement functional outcomes, with EDSS remaining stable or improving in most patients. Notably, individual patients in both groups experienced clinically meaningful improvements in EDSS, suggesting not only prevention of progression but potential functional recovery [13, 16].\u003c/p\u003e\n\u003cp\u003eBaseline ARR and EDSS were explored as potential predictors of achieving NEDA. While baseline ARR demonstrated significant predictive value, baseline EDSS yielded mixed findings: in some analyses it appeared predictive, but in others (e.g., AUC = 0.61, p = 0.437), its utility was limited. This discrepancy underscores the need for caution in interpreting these variables as robust predictors of treatment success [17].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with Other Anti-CD20 Agents and Justification for Rituximab Use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough ocrelizumab and ofatumumab are approved anti-CD20 therapies for relapsing MS, rituximab remains widely used off-label due to several pragmatic factors. First, its lower cost and broader global availability make it a practical option in resource-limited settings [18]. Second, its established role in treating other autoimmune diseases has resulted in a well-documented safety record. Spelman et al. (2018) and Andersen et al. (2022) showed that rituximab and ocrelizumab have comparable effectiveness in real-world and registry-based studies [14, 19].\u003c/p\u003e\n\u003cp\u003eWhile rituximab and ocrelizumab share a CD20-depleting mechanism, differences in molecular structure (chimeric vs. humanized antibodies) and immune reconstitution profiles may affect outcomes. Ocrelizumab offers regulatory approval and standardized dosing, but rituximab\u0026rsquo;s longer history and cost-effectiveness support its off-label use in appropriate settings. Differential B-cell repopulation kinetics between na\u0026iuml;ve and switch patients may underlie some response heterogeneity.\u003c/p\u003e\n\u003cp\u003eWhile not formally licensed for MS, rituximab provides a cost-effective, accessible, and clinically validated alternative, particularly when access to approved agents is limited due to economic or regulatory constraints. Regulatory considerations, as discussed by Laurson-Doube et al. (2021), stress that off-label rituximab use must be ethically justified by clinical need, cost-benefit analyses, and local health policy [19].\u003c/p\u003e\n\u003cp\u003eRecent real-world studies have reinforced rituximab\u0026rsquo;s efficacy and safety, positioning it as a viable alternative to newer anti-CD20 agents [16, 17, 20]. Additionally, a pooled analysis by Alcal\u0026aacute; et al. (2022) highlighted that rituximab shares similar disease control metrics with ocrelizumab across a wide MS population, although direct comparative trials remain limited [18].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSafety and Tolerability of Rituximab\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsistent with prior literature, our study confirms rituximab\u0026apos;s favorable safety profile. Only two adverse events were reported: a mild infusion-related rash and a case of lymphopenia, neither of which necessitated treatment discontinuation. Post-treatment lymphocyte levels were significantly lower in the switch group compared to the na\u0026iuml;ve group (p = 0.03), though no severe lymphopenia-related complications were observed. Luna et al. (2020) and Salzer et al. (2016) highlighted a lower rate of infection-related complications with rituximab compared to other high-efficacy agents [16, 13]. Winkelmann et al. (2016) and Jalkh et al. (2021) also noted that infection risks are manageable, particularly with appropriate monitoring and patient selection [20, 21].\u0026nbsp;Monitoring of immunoglobulin levels or vaccine response was not performed, which remains a limitation in safety profiling and should be prioritized in future studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite its limitations, this study reinforces the role of rituximab as a viable treatment option for relapsing MS in real-world settings, particularly where regulatory or financial barriers restrict access to approved anti-CD20 therapies [19]. The enhanced efficacy seen in treatment-na\u0026iuml;ve patients suggests a potential advantage for early B-cell depletion [11,13]. However, these findings should be confirmed through larger, prospective, multicenter studies incorporating long-term follow-up, immunologic monitoring, and direct comparisons with ocrelizumab or other high-efficacy agents.\u003c/p\u003e\n\u003cp\u003eFuture research should also evaluate rituximab\u0026rsquo;s role in progressive MS and assess patient-reported outcomes, quality of life, and cost-effectiveness metrics. Establishing clear criteria for treatment success\u0026mdash;including radiological and functional recovery endpoints\u0026mdash;may further guide individualized therapy choices in MS care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several important limitations. First, its retrospective design introduces inherent risks of selection and information bias, potentially affecting the accuracy and completeness of clinical and radiological data. Second, the small sample size (n = 34), along with the modest imbalance between the treatment-na\u0026iuml;ve and switch groups (16 vs. 18), limits statistical power and may increase the risk of type II errors, as illustrated by the non-significant difference in NEDA rates (p = 0.60). Third, the 12-month follow-up duration, while adequate for assessing short-term efficacy and safety, may be insufficient to capture long-term outcomes, including sustained disease control, delayed adverse events, opportunistic infections, or malignancy risk.\u0026nbsp;Although MRI assessments were standardized and conducted by a single radiologist, subtle discrepancies in protocol timing and image quality could affect outcome sensitivity. Fourth, while multivariate analysis was used to adjust for key confounders, residual confounding likely remains. Notably, na\u0026iuml;ve and switch groups differed significantly in disease duration and comorbidity profiles, which may have influenced observed effects. No adjustment for multiple comparisons was made, which may increase the likelihood of type I error.\u003c/p\u003e\n\u003cp\u003eFifth, the absence of longitudinal immunoglobulin data, vaccine response tracking, and extended safety follow-up limits the scope of safety assessment. Finally, the single-center nature and exclusion of patients with uncontrolled comorbidities constrain external validity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eRituximab demonstrated significant effectiveness in reducing relapse rates, suppressing radiological disease activity, and stabilizing or improving functional status in both treatment-na\u0026iuml;ve and previously treated patients with multiple sclerosis. Its short-term safety profile was favorable, and no serious adverse events were observed. These real-world findings support rituximab\u0026apos;s potential as a viable therapeutic option, particularly in settings where access to approved anti-CD20 therapies is limited. Nonetheless, the study\u0026rsquo;s retrospective design, small sample size, and baseline heterogeneity warrant cautious interpretation. Prospective, multicenter studies with longer follow-up and larger cohorts are needed to validate these preliminary observations and better define rituximab\u0026rsquo;s long-term safety and effectiveness in MS management.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003eAdverse Event\u003c/li\u003e\n \u003cli\u003eARR: Annualized Relapse Rate\u003c/li\u003e\n \u003cli\u003eCI: Confidence Interval\u003c/li\u003e\n \u003cli\u003eCTCAE: Common Terminology Criteria for Adverse Events\u003c/li\u003e\n \u003cli\u003eDMT: Disease-Modifying Therapy\u003c/li\u003e\n \u003cli\u003eEDSS: Expanded Disability Status Scale\u003c/li\u003e\n \u003cli\u003eESR: Erythrocyte Sedimentation Rate\u003c/li\u003e\n \u003cli\u003eFU: Follow-Up\u003c/li\u003e\n \u003cli\u003eGd+: Gadolinium-Enhancing (lesions)\u003c/li\u003e\n \u003cli\u003eHTN: Hypertension\u003c/li\u003e\n \u003cli\u003eMRI: Magnetic Resonance Imaging\u003c/li\u003e\n \u003cli\u003eMS: Multiple Sclerosis\u003c/li\u003e\n \u003cli\u003eNEDA: No Evidence of Disease Activity\u003c/li\u003e\n \u003cli\u003eOR: Odds Ratio\u003c/li\u003e\n \u003cli\u003ePKD: Polycystic Kidney Disease\u003c/li\u003e\n \u003cli\u003eRR: Relapsing-Remitting (MS phenotype)\u003c/li\u003e\n \u003cli\u003eSD: Standard Deviation\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Declaration:\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eH.E: Study design, Manuscript writing, literature review, editingM. A. A, M. U. A, R.M, M. A, T. A: Data CollectionR. A, R. E, A. A, A.T: Study design, Manuscript review\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003ethe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJakimovski D, Bittner S, Zivadinov R et al (2023) Multiple sclerosis. Lancet 402(10397):1048\u0026ndash;1064\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatti F (2012) Treatment of cognitive impairment in patients with multiple sclerosis. Expert Opin Investig Drugs 21(11):1679\u0026ndash;1699\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi R, Patterson KR, Bar-Or A (2018) Reassessing B cell contributions in multiple sclerosis. Nat Immunol 19(7):696\u0026ndash;707\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHauser SL, Cree BAC (2020) Treatment of multiple sclerosis: a review. Am J Med 133(12):1380\u0026ndash;1390 e2\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCross AH, Naismith RT (2014) Established and novel disease-modifying treatments in multiple sclerosis. J Intern Med 275(4):350\u0026ndash;363\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMontalban X, Hauser SL, Kappos L et al (2019) The therapeutic potential of anti-CD20 monoclonal antibodies in multiple sclerosis: A review. Autoimmun Rev 18(7):721\u0026ndash;728\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evon Elm E, Altman DG, Egger M et al (2007) The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for reporting observational studies. PLoS Med 4(10):e296\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThompson AJ, Banwell BL, Barkhof F et al (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 17(2):162\u0026ndash;173\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKurtzke JF (1983) Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology 33(11):1444\u0026ndash;1452\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNational Cancer Institute. Common Terminology Criteria for Adverse Events (CTCAE) v5.0 (2017) Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ctep.cancer.gov/protocolDevelopment/electronic_applications/ctc.htm\u003c/span\u003e\u003cspan address=\"https://ctep.cancer.gov/protocolDevelopment/electronic_applications/ctc.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGranqvist M, Boremalm M, Poorghobad A, Svenningsson A, Salzer J, Frisell T et al (2018) Comparative Effectiveness of Rituximab and Other Initial Treatment Choices for Multiple Sclerosis. JAMA Neurol 75(3):320\u0026ndash;327\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScotti B, Disanto G, Sacco R, Guigli M, Zecca C, Gobbi C (2018) Effectiveness and safety of Rituximab in multiple sclerosis: an observational study from Southern Switzerland. PLoS ONE 13(5):e0197415\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalzer J, Svenningsson R, Alping P, Novakova L, Bj\u0026ouml;rck A, Fink K et al (2016) Rituximab in multiple sclerosis: A retrospective observational study on safety and efficacy. Neurology 87(20):2074\u0026ndash;2081\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpelman T, Frisell T, Piehl F, Hillert J (2018) Comparative effectiveness of rituximab relative to IFN-β or glatiramer acetate in relapsing-remitting MS from the Swedish MS registry. Mult Scler 24(8):1087\u0026ndash;1095\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHonce JM, Nair KV, Sillau S, Valdez B, Miravalle A, Alvarez E et al (2019) Rituximab vs placebo induction prior to glatiramer acetate monotherapy in multiple sclerosis. Neurology 92(7):e723\u0026ndash;e732\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuna G, Alping P, Burman J, Fink K, Fogdell-Hahn A, Gunnarsson M et al (2020) Infection Risks Among Patients With Multiple Sclerosis Treated With Fingolimod, Natalizumab, Rituximab, and Injectable Therapies. JAMA Neurol 77(2):184\u0026ndash;191\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVermersch P, Oreja-Guevara C, Siva A, Van Wijmeersch B, Wiendl H, Wuerfel J et al (2022) Efficacy and safety of ocrelizumab in patients with relapsing-remitting multiple sclerosis with suboptimal response to prior disease-modifying therapies: A primary analysis from the phase 3b CASTING single-arm, open-label trial. Eur J Neurol 29(3):790\u0026ndash;801\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlcal\u0026aacute; C, Quintanilla-Bord\u0026aacute;s C, Gasc\u0026oacute;n F, Sempere \u0026Aacute;P, Navarro L, Carcel\u0026eacute;n-Gadea M et al (2022) Effectiveness of rituximab vs. ocrelizumab for the treatment of primary progressive multiple sclerosis: a real-world observational study. J Neurol 269(7):3676\u0026ndash;3681\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaurson-Doube J, Rijke N, Helme A, Baneke P, Banwell B, Viswanathan S, Hemmer B, Yamout B (2021) Ethical use of off-label disease-modifying therapies for multiple sclerosis. Mult Scler 27(9):1403\u0026ndash;1410\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWinkelmann A, Loebermann M, Reisinger EC, Hartung HP, Zettl UK (2016) Disease-modifying therapies and infectious risks in multiple sclerosis. Nat Rev Neurol 12(4):217\u0026ndash;233\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJalkh G, Abi Nahed R, Macaron G, Rensel M (2021) Safety of Newer Disease Modifying Therapies in Multiple Sclerosis. Vaccines 9(1):12\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Baseline Characteristics of MS Patients by Treatment Group\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNa\u0026iuml;ve Group (n = 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSwitch Group (n = 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatistical Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge, mean \u0026plusmn; SD (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.88 \u0026plusmn; 10.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.72 \u0026plusmn; 9.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et-test=1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eChi-square\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDisease duration,\u003c/p\u003e\n \u003cp\u003emean \u0026plusmn; SD (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.71 \u0026plusmn; 2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.56 \u0026plusmn; 3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.02*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRituximab duration,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003emean \u0026plusmn; SD (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21.38 \u0026plusmn; 18.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15.67 \u0026plusmn; 8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBaseline EDSS, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.81 \u0026plusmn; 1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.67 \u0026plusmn; 1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eU = 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo reported comorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (81.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Hypothyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.09*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Bronchial Asthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Sj\u0026ouml;gren\u0026rsquo;s Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Hepatitis B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Epilepsy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Uterine Fibroma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Severe Cervical Stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Polycystic Kidney Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: EDSS = Expanded Disability Status Scale; SD = Standard Deviation. *Bolded p-values indicate statistical significance (p \u0026lt; 0.05), Mann\u0026ndash;Whitney U = U Test, FE = Fisher\u0026rsquo;s Exact Test\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Efficacy Outcomes Before and After Rituximab Treatment\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eNa\u0026iuml;ve Group (n = 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSwitch Group (n = 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eStatistical Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 575px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelapse Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePre-treatment\u003c/p\u003e\n \u003cp\u003e(mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.44 \u0026plusmn; 0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.67 \u0026plusmn; 1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.007*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePost-treatment\u003c/p\u003e\n \u003cp\u003e(mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.06 \u0026plusmn; 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.17 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eWithin-group change (p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eWilcoxon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 575px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEDSS Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003cp\u003e(mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.81 \u0026plusmn; 1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.67 \u0026plusmn; 1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eFollow-up (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.44 \u0026plusmn; 1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.39 \u0026plusmn; 1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eWithin-group change (p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eWilcoxon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 575px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRI Findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSpinal lesions (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e11 (68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e15 (83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eFE\u003c/p\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNew T2 lesions (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNot Applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eWithin-group change (p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMcNemar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNEDA (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e15 (93.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e15 (83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eFE = 0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: EDSS = Expanded Disability Status Scale; NEDA = No Evidence of Disease Activity. *Bolded p-values indicate significance (p \u0026lt; 0.05), Mann\u0026ndash;Whitney U = U Test, FE = Fisher\u0026rsquo;s Exact Test\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Safety Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSafety Parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNa\u0026iuml;ve Group (n = 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSwitch Group (n = 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatistical Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eLymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Pre-treatment (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.23 \u0026plusmn; 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.77 \u0026plusmn; 0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e- Post-treatment (mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.40 \u0026plusmn; 0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.82 \u0026plusmn; 0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e*0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eESR (Normal/High), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (81.3%)/3 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (72.2%)/5 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFE\u003c/p\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInfusion Reactions, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFE\u003c/p\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInfections, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFE = 0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTreatment Discontinuations, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot Applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eESR = Erythrocyte Sedimentation Rate,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eMann\u0026ndash;Whitney\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eU = U Test, FE =\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eFisher\u0026rsquo;s Exact\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eTest,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e*Bolded p-values indicate statistical significance (p \u0026lt; 0.05),\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Improvement of Cases on Rituximab by Sociodemographic and Clinical Data\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eTreatment Success (n = 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eTreatment Failure (n = 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eStatistical Test (p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e36.86 \u0026plusmn; 10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e37.20 \u0026plusmn; 6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003et-test: 0.0 (p = 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e18\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e28\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eFE Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e12 (41.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e4 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.55 (p = 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e17 (58.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease Duration (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eU Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e5.39 \u0026plusmn; 3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e4.20 \u0026plusmn; 1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eU = 0.44 (p = 0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1\u0026ndash;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e2\u0026ndash;6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of Rituximab (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eU Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e18.34 \u0026plusmn; 15.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e18.40 \u0026plusmn; 7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eU = 0.72 (p = 0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e6\u0026ndash;66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e8\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eTreatment Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eFE Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Na\u0026iuml;ve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e15 (51.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.72 (p = 0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Switched\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e14 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e4 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRI Activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Baseline Spinal Affection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eFE Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e21 (72.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e5 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.80 (p = 0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e8 (27.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Baseline Enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eFE Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e16 (55.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e2 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; = 0.39 (p = 0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e13 (44.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e3 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelapse Rate (Pre-Treatment)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eU Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.80 \u0026plusmn; 1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e3.20 \u0026plusmn; 1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eU = 2.23\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(*\u003cstrong\u003ep = 0.026\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Median (Range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1 (1\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e3 (2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocytes (Pre-Treatment)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eU Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.95 \u0026plusmn; 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e2.17 \u0026plusmn; 0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eU = 0.66 (p = 0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e- Median (Range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.9 (0.55\u0026ndash;4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e2.2 (1.14\u0026ndash;3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eSD = Standard Deviation,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eMann\u0026ndash;Whitney\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eU = U Test, \u0026chi;\u0026sup2; = Chi-Square Test,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eFE\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e=\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eFisher\u0026rsquo;s Exact\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eTest\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e,\u003c/em\u003e\u003c/strong\u003e \u003cstrong\u003e\u003cem\u003eMRI = Magnetic Resonance Imaging,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e*Bolded p-values indicate statistical significance (p \u0026lt; 0.05),\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e.\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Correlation Between Baseline Variables and Treatment Response\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eEDSS Change\u0026nbsp;\u003c/p\u003e\n \u003cp\u003er (p)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eARR Change\u0026nbsp;\u003c/p\u003e\n \u003cp\u003er (p)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eNEDA Achievement r(p)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eDisease Duration (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.26 (p = 0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.12 (p = 0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0.01 (p = 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eBaseline EDSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.32 (p = 0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e-0.12 (0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0.32 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eBaseline Relapse Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e-0.25 (p = 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.97 (p \u0026lt; 0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0.40 (*\u003cstrong\u003ep = 0.02\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eBaseline Lymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.29 (p = 0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e-0.33 (p = 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e-0.02 (p = 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eEDSS = Expanded Disability Status Scale, ARR = Annualized Relapse Rate, NEDA = No Evidence of Disease Activity, \u0026rho; = Spearman\u0026rsquo;s Rank Correlation Coefficient,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e*Bolded p-values indicate statistical significance (p \u0026lt; 0.05).\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Multivariate Logistic Regression for Predictors of NEDA\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eNa\u0026iuml;ve Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e1.18 (0.4\u0026ndash;13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eDisease Duration (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e0.87 (0.72\u0026ndash;3.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eBaseline EDSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2.78 (1.56\u0026ndash;6.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eBaseline ARR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2.93 (1.66\u0026ndash;7.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eOR\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e= Odds Ratio, CI\u0026nbsp;= Confidence Interval, EDSS\u0026nbsp;= Expanded Disability Status Scale, ARR\u0026nbsp;= Annualized Relapse Rate, NEDA\u0026nbsp;= No Evidence of Disease Activity, *Bolded p-values indicate statistical significance (p \u0026lt; 0.05).\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: Subgroup Comparison by Age (\u0026lt;35 vs. \u0026ge;35 Years)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026lt;35 Years (n = 14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026ge;35 Years (n = 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNEDA (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e13/14 (92.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e17/20 (85.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003ePost-Treatment ARR (Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.07 \u0026plusmn; 0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.15 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eEDSS Improvement (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e4/14 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e5/20 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eNEDA\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e= No Evidence of Disease Activity, ARR = Annualized Relapse Rate, EDSS = Expanded Disability Status Scale\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8: ROC curve analysis for Baseline ARR and EDSS\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for prediction of successful treatment\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCut-off Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eBaseline ARR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026gt;2.5 relapses/year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e55.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.80 (0.63\u0026ndash;0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eBaseline EDSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026le;2.0 points\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e75.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e0.61 (0.30\u0026ndash;0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eARR\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e= Annualized Relapse Rate, EDSS = Expanded Disability Status Scale, AUC = Area Under the Curve, CI = Confidence Interval, *Bolded p-values indicate statistical significance (p \u0026lt; 0.05).\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroimmune-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jnip","sideBox":"Learn more about [Journal of Neuroimmune Pharmacology](http://link.springer.com/journal/11481)","snPcode":"11481","submissionUrl":"https://submission.nature.com/new-submission/11481/3","title":"Journal of Neuroimmune Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Multiple sclerosis, Rituximab, Retrospective cohort, Treatment-naïve, Disease-modifying therapy","lastPublishedDoi":"10.21203/rs.3.rs-7118548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7118548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003cbr\u003e\nRituximab, an anti-CD20 monoclonal antibody, is increasingly used off-label in multiple sclerosis (MS), particularly where access to approved B-cell therapies is limited. This retrospective cohort study assessed its real-world effectiveness and safety in treatment-naïve versus switch patients at a single center in Saudi Arabia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\nWe retrospectively analyzed data from 34 MS patients treated with rituximab at Security Forces Hospital, Makkah, between January 2018 and December 2024. Patients were categorized as treatment-naïve (n = 16) or treatment-switch (n = 18). Outcomes included annualized relapse rate (ARR), Expanded Disability Status Scale (EDSS), MRI activity, adverse events, and no evidence of disease activity (NEDA) status at 12-month follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nRituximab significantly reduced ARR in both groups (naïve: 1.44 ± 0.73 to 0.06 ± 0.25, p \u0026lt; 0.001; switch: 2.67 ± 1.46 to 0.17 ± 0.38, p \u0026lt; 0.001). NEDA was achieved in 93.8% of naïve and 83.3% of switch patients (p = 0.60). EDSS remained stable or improved in most cases (p = 0.94). No new T2 lesions were observed on MRI in any patient. Adverse events were minimal and manageable, with one mild infusion reaction and one case of asymptomatic lymphopenia. Post-treatment lymphocyte counts were lower in switch patients (p = 0.03), but no severe infections occurred.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003cbr\u003e\nIn this retrospective cohort, rituximab demonstrated significant short-term efficacy in relapse reduction and disease stabilization in both treatment-naïve and switch MS patients, with a favorable safety profile. However, these findings must be interpreted cautiously due to the small sample size, retrospective design, and baseline group differences. Larger prospective studies are warranted to confirm long-term outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegistration:\u003cbr\u003e\n \u003c/strong\u003eThe study protocol was approved by the Institutional Review Board (IRB) of Security Forces Hospital Makkah (SFHM), registered with the National BioMedical Ethics Committee under King Abdulaziz City for Science and Technology (Registration number: HAP-02-K-052). IRB approval number: [0749-081024], dated November 2024.\u003c/p\u003e","manuscriptTitle":"Comparative Effectiveness of Rituximab in Treatment-Naïve vs. Switch Patients with Multiple Sclerosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 05:53:01","doi":"10.21203/rs.3.rs-7118548/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-05T15:26:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T21:23:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89969967872458546142960436218954430874","date":"2025-11-08T12:43:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281563562396368606610108668315921661838","date":"2025-08-18T00:50:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226037984636661724142094964999872488447","date":"2025-08-17T07:29:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-16T05:08:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282672188427371362338674556422897031717","date":"2025-08-16T03:38:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235491938806959271645037210856724496749","date":"2025-08-15T20:07:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-15T18:35:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-10T15:13:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-24T07:43:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuroimmune Pharmacology","date":"2025-07-14T08:03:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroimmune-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jnip","sideBox":"Learn more about [Journal of Neuroimmune Pharmacology](http://link.springer.com/journal/11481)","snPcode":"11481","submissionUrl":"https://submission.nature.com/new-submission/11481/3","title":"Journal of Neuroimmune Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cf010d3a-24ce-48f8-bd5e-35e4d430cff7","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:12:20+00:00","versionOfRecord":{"articleIdentity":"rs-7118548","link":"https://doi.org/10.1007/s11481-026-10288-9","journal":{"identity":"journal-of-neuroimmune-pharmacology","isVorOnly":false,"title":"Journal of Neuroimmune Pharmacology"},"publishedOn":"2026-04-13 15:59:05","publishedOnDateReadable":"April 13th, 2026"},"versionCreatedAt":"2025-08-27 05:53:01","video":"","vorDoi":"10.1007/s11481-026-10288-9","vorDoiUrl":"https://doi.org/10.1007/s11481-026-10288-9","workflowStages":[]},"version":"v1","identity":"rs-7118548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7118548","identity":"rs-7118548","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}