Risk stratification tools in multiple myeloma (MM) are used to define risk after first relapse in clinical trials and standard practice. Although these tools can help clinicians to define survival expectations and treatment decisions, there are additional variables that may need to be considered to understand drivers of disease progression and ensure that treatment strategies are aligned with patient risk. European researchers assessed predictors of overall survival (OS) and developed a new risk stratification tool to predict OS at the time of treatment decision after first relapse (TTD1). The K-Adaptive Partitioning for Survival (KAPS) method, which stratified data based on survival expectations, was run to define 4 distinct groups of patients. The risk stratification tool consists of 4 dimensions and 12 questions based on the strongest predictors of survival at TTD1.
Dimensions include the following: (1) patient factors (age and Eastern Cooperative Oncology Group [ECOG] performance status); (2) existing stratification factors (R-ISS [Revised International Staging System] at diagnosis and ISS at TTD1); (3) disease factors (calcium level, number of bone lesions, extramedullary disease, thrombocyte count, clonal cells in bone marrow aspiration cytology, and lactate dehydrogenase); and (4) treatment history (refractory to prior therapy and time to next treatment). Subsequently, the researchers assessed each group based on distribution of frailty-driven measures (age and ECOG) and aggressiveness of the disease (all other parameters) to determine which parameters contribute most to risk stratification.
The stratification process allowed researchers to identify 4 distinct groups with different survival expectations, and no overlap in confidence intervals. Group 1 showed a median OS of 57.2 months; group 2, 28.8 months; group 3, 13.4 months; and group 4, 4.7 months. When evaluating the drivers of risk, researchers found that the differences for the risk groups were greater for mean aggressiveness scores than for mean frailty scores, underscoring the considerable impact of disease severity on outcomes.
Study authors concluded that the risk stratification tool has shown promising results; however, they acknowledged that further validation is required using other real-world and clinical trial data. This approach may represent a new method for individualizing therapeutic selection based on drivers of risk and improved understanding of patient profiles.
Hajek R, et al. ASH 2016. Abstract 2417.
