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An Epidemiology Model for Estimating the Numbers of US Patients With Multiple Myeloma by Line of Therapy and Treatment Exposure

Published:August 11, 2022DOI:https://doi.org/10.1016/j.jval.2022.05.011

      Highlights

      • Estimates on the distribution of patients with multiple myeloma by line of therapy are scarce and become outdated quickly as new treatments become available.
      • A mathematical model was developed to calculate the number of patients with MM by line of therapy and prior treatment exposure.
      • The proposed modeling framework can assist clinicians to understand future trends in MM epidemiology and healthcare systems and payers to plan for future resource use allocation.

      Abstract

      Objectives

      Estimates on the distribution of patients with multiple myeloma (MM) by line of therapy (LOT) are scarce and get outdated quickly as new treatments become available. The objective of this study was to estimate the number of patients with MM by LOT and the number of patients who have received at least 4 previous LOTs including proteasome inhibitors, immunomodulatory agents, and anti-CD38 monoclonal antibodies (mAbs).

      Methods

      A compartmental model was developed to calculate the number of patients by LOT. Two pathways were considered based on stem cell transplant eligibility, and at each pathway, treatments were stratified in 2 types: anti-CD38 mAbs or other. The model population was stratified into 4 subgroups based on age and cytogenetic risk. Model inputs were informed from real-world evidence.

      Results

      The model estimated that, in 2020, 126 869 patients were living with MM in the United States. Of these, 105 701 received treatment in any LOT, with 56 959, 27 252, 11 258, and 5217 in lines 1 to 4, respectively, and 5015 in line 5 or beyond. The model estimated that 3497 patients received at least 4 previous LOTs including proteasome inhibitors, immunomodulatory agents, and anti-CD38 mAbs. The model overall prevalence predictions aligned well with publicly available estimates.

      Conclusions

      This study proposes a novel framework to estimate MM prevalence. It can assist clinicians to understand future trends in MM epidemiology, healthcare systems to plan for future resource use allocation, and payers to quantify the budget impact of new treatments.

      Keywords

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