Assessment of algorithms for identifying patients with triple-class refractory multiple myeloma using real-world data.

Patients with triple-class refractory (TCR) multiple myeloma (MM) have limited treatment options and poor prognoses. This high unmet need has prompted the development of new therapies allowing for improved outcomes for these patients. Recently, new targeted therapies for the treatment of patients with relapsed or refractory MM have been approved based on single-arm clinical trial results. Real-world (RW) data enable a better understanding of the effectiveness of new therapies in clinical practice and provide external controls for single-arm studies. However, using RW data to identify patients with TCR MM is challenging and subject to limitations. In this retrospective cohort study of an analysis of the COTA electronic health record (EHR) database, we used four algorithms to define refractory status and created four groups of patients with TCR MM initiating post-TCR therapy. Each algorithm relied on slightly different criteria to identify TCR patients, but all were based on the International Myeloma Working Group (IMWG)-derived and/or healthcare provider (HCP)-reported progressions within the database. A total of 3815 patients with newly diagnosed MM met the eligibility criteria for this study. The choice of the algorithm did not impact the characteristics of identified patients with TCR MM (Algorithm 1 [ = 404], Algorithm 2 [ = 123], Algorithm 3 [ = 404], and Algorithm 4 [ = 375]), including their demographic and disease characteristics, MM treatment history, or treatment patterns received after becoming TCR. However, identifying TCR MM using a combination of IMWG-derived and HCP-reported progressions allowed up to a 70% increase in the size of the identified group of patients compared with using only IMWG-derived progressions. In RW settings, progressions from both IMWG-derived data and physician reports may be used to identify patients with TCR MM.