Risk stratification tools in multiple myeloma 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. This European study assessed predictors of overall survival (OS) and developed a new Risk Stratification Tool (RST) to predict OS at time of treatment decision after first relapse (TTD1). The Partitioning for Survival method, which stratified data based on distinct survival expectations, was run to define 4 distinct groups of patients.
The RST 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.
A survival analysis showed strong differentiation in survival expectations between the 4 groups when viewing the Kaplan-Meier curves; median OS after first relapse was significantly different for all groups and the confidence intervals did not overlap (group 1, 57.2 months; group 2, 28.8 months; group 3, 13.4 months; 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 RST has shown promising results; however, further validation is required using other real-world and clinical trials data. This approach may represent a new method for systematically assessing patient risk to improve the selection of treatments based on improved understanding of patient profiles.
Hajek R, et al. ASH 2016. Abstract 2417.