A knowledge-bank (KB) algorithm provides more accurate outcome prediction than the current ELN risk stratification.
The decision to perform HSCT in first CR in AML can be personalized by integrating ELN 2017 risk, NPM1 MRD, and KB simulation.
A multistage model instructed by a large dataset (knowledge bank [KB] algorithm) has recently been developed to improve outcome prediction and tailor therapeutic decision, including hematopoietic stem cell transplantation (HSCT) in AML. We assessed the performance of the KB in guiding HSCT decision in first complete remission (CR1) in 656 AML patients younger than 60 years from the ALFA-0702 trial (NCT00932412). KB predictions of Overall Survival (OS) were superior to those of European LeukemiaNet (ELN) 2017 risk stratification (C-index 68.9 versus 63.0). Among patients reaching CR1, HSCT in CR1, as a time-dependent covariate was detrimental in those with favorable ELN 2017 risk and those with negative NPM1 MRD (interaction tests P=0.01 and P=0.02, respectively). Using KB simulations of survival at 5 years in a scenario without HSCT in CR1 (KB score), we identified in a similar time-dependent analysis a significant interaction between the KB score and HSCT, with HSCT in CR1 being detrimental only in patients with a good prognosis based on KB simulations (KB score ≥40, interaction test P=0.01). We could finally integrate ELN 2017, NPM1 MRD and KB scores to sort 545 CR1 patients into 278 (51.0%) HSCT candidates and 267 (49.0%) chemo-only candidates. In both time-dependent and 6-month landmark analyses, HSCT significantly improved OS in HSCT candidates while it significantly shortened OS in chemo-only candidates. Integrating KB predictions with ELN 2017 and MRD may thus represent a promising approach to optimize HSCT timing in younger AML patients.