Comparison of Dimethyl Fumarate vs Fingolimod and Rituximab vs Natalizumab for Treatment of Multiple Sclerosis

Hou, Jue; Kim, Nicole J.; Cai, Tianrun; Dahal, Kumar; Weiner, Howard L.; Chitnis, Tanuja; Cai, Tianxi; Xia, Zongqi

doi:10.1001/jamanetworkopen.2021.34627

Cited by 24 publications

(14 citation statements)

References 53 publications

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“…3,4 The available MS disease-modifying therapies (DMTs) pose soaring costs and exhibit variable real-world effectiveness in preventing inflammatory disease activity and delaying disability worsening. [5][6][7] In the current practice, clinicians primarily rely on history, exams and neuroimaging to assess MS disease activity, disability progression, and treatment response. There is an unmet need to improve disease monitoring at the point of care to guide individualized management.…”

Section: Introductionmentioning

confidence: 99%

Association between Serum Biomarker Profile and Real-World Evidence of Disability in Multiple Sclerosis

Zhu

Chen

Zhang

et al. 2022

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ObjectiveBiomarkers could inform disease worsening and severity in people with MS (pwMS). Few studies have examined blood biomarkers informative of patient-reported outcome (PRO) of disability in pwMS. In this study we examine the associations between serum protein biomarker profiles and patient-reported disability in pwMS.MethodsThis cross-sectional study included adults with a neurologist-confirmed diagnosis of MS from the University of Pittsburgh Medical Center (Pittsburgh, PA) and the Rocky Mountain MS Clinic (Salt Lake City, Utah) between 2017 and 2020. For exposure, we included 19 serum protein biomarkers potentially associated with MS inflammatory disease activity and 7 key clinical factors (age at sample collection, sex, race/ethnicity, disease subtype, disease duration, disease-modifying treatment, and time interval between sample collection and closest PRO assessment). Using 6 machine learning approaches (Least Absolute Shrinkage and Selection Operator [LASSO] regression, Random Forest [RF], XGBoost, Support-Vector Machines [SVM], stacking ensemble learning, and stacking classification algorithm), we examined model performance in predicting Patient Determined Disease Steps (PDDS) as the primary outcome. We assessed model prediction of Patient-Reported Outcomes Measurement Information System (PROMIS) physical function in a subgroup. We reported model performance using the held-out testing set.ResultsWe included 431 unique participants (mean age 49 years, 81% women, 94% non-Hispanic White). Using binary outcomes, models comprising both routine clinical factors and the 19 proteins as features consistently outperformed base models (containing clinical features alone or clinical features plus single protein) in predicting severe (PDDS≥4, PROMIS<35) versus mild/moderate (PDDS<4, PROMIS≥35) disability for all machine learning approaches, with LASSO achieving the best area under the curve (AUCPDDS=0.91, AUCPROMIS=0.90). Using ordinal/continuous outcomes, LASSO models with combined clinical factors and 19 proteins as features (R2PDDS=0.31, R2PROMIS=0.35) again outperformed base models. The four LASSO models (PDDS, PROMIS; both binary and ordinal/continuous) with combined clinical and protein features shared 2 clinical features (disease subtype, disease duration) and 4 protein biomarkers (CDCP1, IL-12B, NEFL, PRTG).ConclusionsSerum protein biomarker profiles improve the prediction of real-world MS disability status beyond clinical profile alone or clinical profile plus individual protein biomarker, reaching clinically actionable performance.

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Section: Introductionmentioning

confidence: 99%

Association between Serum Biomarker Profile and Real-World Evidence of Disability in Multiple Sclerosis

Zhu

Chen

Zhang

et al. 2022

Preprint

Self Cite

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“…Based on their statistical algorithms, there was an increased 1-year (0.08 rate difference), 2-year (0.132 rate difference) and shorter time to related (0.903 rate difference) in natalizumab-treated patients compared to rituximab. Interestingly, there were no significant difference in relapses between dimethyl fumarate and fingolimod in all three outcome measures (150).…”

Section: Injectable Vs Oral Dmts Vs High-efficacy Dmtmentioning

confidence: 81%

“…In this study, the oral DMT group demonstrated a lower time to first relapse (HR 0.57, 95% CI 0.47, 0.69) and annualized relapse rate (0.65 incidence ratio, 95% CI 0.52, 0.82) but no difference in the disability progression between the injectable and oral DMT groups (149). A comparative effectiveness study used an MS research registry and the electronic health record of 1,535 patients in the registry to determine 1-year, 2-year relapse rate and time to relapse for patients treated with dimethyl fumarate, fingolimod, natalizumab and rituximab after adjusting for confounders and propensity scores (150). The study compared natalizumab with rituximab, and dimethyl fumarate with fingolimod.…”

Section: Injectable Vs Oral Dmts Vs High-efficacy Dmtmentioning

confidence: 99%

Therapeutic Advances in Multiple Sclerosis

et al. 2022

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Multiple sclerosis (MS) is an autoimmune disease affecting the central nervous system that causes significant disability and healthcare burden. The treatment of MS has evolved over the past three decades with development of new, high efficacy disease modifying therapies targeting various mechanisms including immune modulation, immune cell suppression or depletion and enhanced immune cell sequestration. Emerging therapies include CNS-penetrant Bruton's tyrosine kinase inhibitors and autologous hematopoietic stem cell transplantation as well as therapies aimed at remyelination or neuroprotection. Therapy development for progressive MS has been more challenging with limited efficacy of current approved agents for inactive disease and older patients with MS. The aim of this review is to provide a broad overview of the current therapeutic landscape for MS.

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“…In our analysis, we adjusted for key covariates (age, sex, race, region, time since MS cohort entry, and ECI) readily available from the claims data. Our recent study highlighted the value of incorporating electronic health record (EHR) data as high-dimensional covariates in real-world comparative analysis of MS [ 32 ]. In future studies, we will perform integrated analysis of claims and EHR data to compare long-term healthcare utilization, expenditure, and disability (based on computed phenotype from claims and/or EHR data) across the treatment groups after better accounting for confounders.…”

Section: Discussionmentioning

confidence: 99%

Patterns of Utilization and Expenditure Across Multiple Sclerosis Disease-Modifying Therapies: A Retrospective Cohort Study Using Claims Data from a Commercially Insured Population in the United States, 2010–2019

et al. 2022

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Introduction Comparisons of healthcare utilization and expenditure among multiple sclerosis (MS) disease-modifying therapies (DMTs) are limited. Methods In this retrospective cohort study using commercial insurance claims data of a US population (2010–2019), we compared healthcare utilization and costs in MS across different DMTs. We assigned patients to different treatment arms: no DMT (ND), high-efficacy (HE) DMT (alemtuzumab, B cell depletion, cladribine, and natalizumab), and standard-efficacy (SE) DMT (dimethyl fumarate, glatiramer acetate, interferon beta, sphingosine-1-phosphate receptor modulator, and teriflunomide). We obtained healthcare costs and occurrences of healthcare services: outpatient visits, emergency room visits, hospitalizations, MS-related magnetic resonance imaging (MRI). We quantified relapses (based on MS-related hospitalizations, as well as outpatient visits with prescription of high-dose steroids) and medical complexity (based on unique drug classes of prescriptions). We calculated covariate-adjusted incidence rate ratio of healthcare services using negative binomial regression with ND as reference and covariate-adjusted mean cumulative healthcare costs using a generalized linear model with log-link function and gamma distribution. Results Among the 25,932 patients with MS (mean age 52.8 years, 75.2% women), both HE (mean age 54.0 years, 76.2% women) and SE (mean age 43.9 years, 75.6% women) groups had more non-pharmacy healthcare utilization than ND (mean age 57.6 years, 75.4% women), including overall outpatient doctor visits, neurology visits, and MS-related MRIs as well as relapses and medical complexities. Relative to ND, both HE and SE groups had higher pharmacy costs and overall healthcare costs 12 months after treatment initiation, despite having lower or equivalent non-pharmacy medical costs. In patients on DMT, pharmacy costs accounted for up to 65% of overall healthcare costs with over 85% of pharmacy costs attributable to DMT costs. Conclusion DMT cost is a key driver of the overall healthcare expenditure in MS. Future comparative and cost-effectiveness studies integrating claims and electronic health records data with better balancing of patient characteristics are warranted. Supplementary Information The online version contains supplementary material available at 10.1007/s40120-022-00358-4.

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Comparison of Dimethyl Fumarate vs Fingolimod and Rituximab vs Natalizumab for Treatment of Multiple Sclerosis

Cited by 24 publications

References 53 publications

Association between Serum Biomarker Profile and Real-World Evidence of Disability in Multiple Sclerosis

Association between Serum Biomarker Profile and Real-World Evidence of Disability in Multiple Sclerosis

Therapeutic Advances in Multiple Sclerosis

Patterns of Utilization and Expenditure Across Multiple Sclerosis Disease-Modifying Therapies: A Retrospective Cohort Study Using Claims Data from a Commercially Insured Population in the United States, 2010–2019

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