TG1101-RMS404

Study type

• Study topic:
  Human medicinal product
• Study type:
  Non-interventional study
• Scope of the study:
  Safety study (incl. comparative)
• Data collection methods:
  Combined primary data collection and secondary use of data

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Name of medicine:
  BRIUMVI
• Study drug International non-proprietary name (INN) or common name:
  UBLITUXIMAB
• Anatomical Therapeutic Chemical (ATC) code:
  (L04AG14) ublituximab

  ublituximab
• Medical condition to be studied:
  Relapsing-remitting multiple sclerosis

Population studied

• Short description of the study population:
  Female, between the ages of 15 and 50, and pregnant during the study period.
• Age groups:
  Adolescents (12 to < 18 years)

  Adults (18 to < 65 years)

  Adults (18 to < 46 years)

  Adults (46 to < 65 years)
• Special population of interest:
  Pregnant women
• Estimated number of subjects:
  1688

Study design details

• Study design:
  The study will involve a retrospective analysis of pregnant subjects diagnosed with MS who were exposed to BRIUMVI at the time of the EDC or at any time during pregnancy. ; The retrospective analysis will include three cohorts for comparison.
• Main study objective:
  The objective of this retrospective cohort study is to assess pregnancy and infant outcomes among pregnant individuals with multiple sclerosis (MS) who were exposed to BRIUMVI during pregnancy, compared to two unexposed patient groups: ; (a) Disease-matched pregnant individuals exposed to other MS therapies and ; (b) Disease-matched pregnant individuals who were not exposed to any treatment for MS at the time of the ; estimated start of pregnancy (estimated date of conception (EDC)) or at any time during pregnancy.
• Setting:
  Eligible subjects for the exposed cohort and two comparison groups will be identified from the administrative claims data, using the following criteria:; Subjects must be female, between the ages of 15 and 50, and pregnant during the study period. ; Pregnancies will be identified through claims data, and validated algorithms will be employed to estimate the first day of the last menstrual period (LMP) and the EDC. ; Additionally, the end of the pregnancy will be determined through this process (18).; The LMP will be estimated based on diagnosis and procedure codes that record the trimester or ; gestational age as of the date of service. ; For example, ICD-10-CM code Z3A.xx captures the weeks of gestation. In the absence of codes that inform the trimester or gestational age, the algorithm will use an established number of days prior to the end of pregnancy, depending on the type of pregnancy outcome.; The EDC will be computed as date of LMP plus 14 days. The EDC will be used as the index date for each pregnancy.
• Comparators:
  Other DMTs
• Outcomes:
  Major congenital malformations (MCMs):An abnormality of body structure or function that is present at birth, is of prenatal origin (i.e., birth defect), has significant medical, social, or cosmetic consequences for the affected individual, and typically requires medical intervention. ; Major structural birth defects will include ICD-10-CM codes for specific organ systems, as defined in validated claims algorithms (20). ; Minor congenital malformations will be excluded from consideration. MCMs will be reported in aggregate, and for each system organ class, as sample size permit.; ;
• Data analysis plan:
  To create comparable BRIUMVI-exposed and non-BRIUMVI-exposed pregnancy and live birth cohorts, we will use propensity score (PS) methods. ; We will use inverse probability of treatment weighting (IPTW) to adjust for confounding of baseline factors and to preserve sample size. ; For the proposed cohort comparison of pregnancies in Cohort 1 compared to those in Cohorts 2 and 3, PS models will be fit including maternal demographics, treatment, and all baseline characteristics with standardized differences >0.1 or <-0.1 in the eligible cohorts and any variable determined to be associated with pregnancy outcomes a priori. ; Baseline covariates with more than 5% missingness will not be considered for inclusion in the propensity score models, to avoid limiting the PS population based on data availability rather than the PS value itself.; The PS model will be a logistic regression model run on eligible pregnancies in the BRIUMVI exposed and each comparison cohort, with BRIUMVI-exposure as the outcome in each model and the baseline covariates as the predictors. ; The predicted probability of each individual receiving their treatment will be predicted from the model and will be used as the propensity score. The inverse of the probabilities from the PS model will be used as weights for each pregnancy in the analysis. ; Separate PS models and IPTW weights will be constructed for the comparison of the BRIUMVI-exposed cohort to Cohort 2 and for the comparison of the BRIUMVI-exposed cohort to Cohort 3.Separate PS models and corresponding IPTW weights will be computed for each comparison of interest (BRIUMVI exposed vs. Cohort 2 and BRIUMVI-exposed vs. Cohort 3) on the subset of live births, from among the linkable eligible pregnancies, for the analyses of infant outcomes.