Development and Validation of a Simplified Score to Predict Early Relapse in Newly Diagnosed Multiple Myeloma (S-ERMM) in a Pooled Dataset of 2190 Patients

Purpose:Despite the improvement of therapeutic regimens,several patients with multiple myeloma (MM) still experienceearly relapse (ER). This subset of patients currently represents anunmet medical need.Experimental Design:We pooled data from seven Europeanmulticenter phase II/III clinical trials enrolling 2,190 patients withnewly diagnosed MM from 2003 to 2017. Baseline patient evalu-ation included 14 clinically relevant features. Patients with completedata (n¼1,218) were split into training (n¼844) and validationsets (n¼374). In the training set, a univariate analysis and amultivariate logistic regression model on ER within 18 months(ER18) were made. The most accurate model was selected on thevalidation set. We also developed a dynamic version of the score byincluding response to treatment.Results:The Simplified Early Relapse in Multiple Myeloma(S-ERMM) score was modeled on six features weighted by ascore: 5 points for high lactate dehydrogenase or t(4;14); 3for del17p, abnormal albumin, or bone marrow plasma cells>60%; and 2 forlfree light chain. The S-ERMM identifiedthree patient groups with different risks of ER18: Intermediate(Int) versus Low (OR¼2.39,P<0.001) and High versus Low(OR¼5.59,P<0.001). S-ERMM High/Int patients hadsignificantly shorter overall survival (High vs. Low: HR¼3.24,P<0.001; Int vs. Low: HR¼1.86,P<0.001) andprogression-free survival-2 (High vs. Low: HR¼2.89,P<0.001; Int vs. Low: HR¼1.76,P<0.001) than S-ERMM Low.The Dynamic S-ERMM (DS-ERMM) modulated the prognosticpower of the S-ERMM.Conclusions:On the basis of simple, widely available base-line features, the S-ERMM and DS-ERMM properlyidentified patients with different risks of ER and survivaloutcomes.