ABSTRACT
Current strategies to treat pediatric acute lymphoblastic leukemia rely on risk stratification algorithms using categorical data. We investigated whether using continuous variables assigned different weights would improve risk stratification. We developed and validated a multivariable Cox model for relapse-free survival (RFS) using information from 21199 patients. We constructed risk groups by identifying cutoffs of the COG Prognostic Index (PICOG) that maximized discrimination of the predictive model. Patients with higher PICOG have higher predicted relapse risk. The PICOG reliably discriminates patients with low vs. high relapse risk. For those with moderate relapse risk using current COG risk classification, the PICOG identifies subgroups with varying 5-year RFS. Among current COG standard-risk average patients, PICOG identifies low and intermediate risk groups with 96% and 90% RFS, respectively. Similarly, amongst current COG high-risk patients, PICOG identifies four groups ranging from 96% to 66% RFS, providing additional discrimination for future treatment stratification. When coupled with traditional algorithms, the novel PICOG can more accurately risk stratify patients, identifying groups with better outcomes who may benefit from less intensive therapy, and those who have high relapse risk needing innovative approaches for cure.