Real-world treatment patterns and clinical outcomes for patients with relapsed/refractory multiple myeloma who received elranatamab

Study type

• Study topic:
  Disease /health condition

  Human medicinal product
• Study type:
  Non-interventional study
• Scope of the study:
  Drug utilisation

  Effectiveness study (incl. comparative)

  Other

  Safety study (incl. comparative)
• If ‘other’, further details on the scope of the study:
  Real-world evidence
• Data collection methods:
  Secondary use of data

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Medicinal product name:
  ELREXFIO
• Study drug International non-proprietary name (INN) or common name:
  ELRANATAMAB
• Anatomical Therapeutic Chemical (ATC) code:
  (L01FX32) elranatamab

  elranatamab

Population studied

• Short description of the study population:
  Study population is US patients diagnosed with Multiple Myeloma (MM) on or after January 1, 2013, who received elranatamab treatment. Approximately 120 patients' data is expected to be included in the study.

Study design details

• Study design:
  This is a retrospective, observational cohort study using de-identified electronic health record (EHR)-derived data from the Flatiron Health Research database (FHRD)
• Main study objective:
  This study aims to fill this evidence gap by comprehensively characterizing the treatment patterns, real-world overall response rate (rwORR), and adverse events (AEs) among RRMM patients treated with elranatamab.
• Data analysis plan:
  Descriptive statistics will be used to summarize demographics, clinical characteristics, treatment patterns, incidence of AEs, and rwORR. For continuous variables, the descriptive statistics will include medians, interquartile range (IQR), means, standard deviations, and minimum and maximum values (as applicable). For categorical variables, frequencies and percentages will be generated. The number of patients with missing data will be reported for all variables. Levels of categorical variables may be combined to account for small sample sizes.