Machine learning evaluation of intensified conditioning on haematopoietic stem cell transplantation in adult acute lymphoblastic leukemia patients

The advantage of intensified myeloablative conditioning (MAC) over standard MAC has not been determined in haematopoietic stem cell transplantation (HSCT) for adult acute lymphoblastic leukemia (ALL) patients. To evaluate heterogeneous effects of intensified MAC among individuals, we analyzed the registry database of adult ALL patients between 2000 and 2021. After propensity score matching, we applied a machine-learning Bayesian causal forest algorithm to develop a prediction model of individualized treatment effect (ITE) of intensified MAC on reduction in overall mortality at 1 year after HSCT. Among 2440 propensity score-matched patients, our model shows heterogeneity in the association between intensified MAC and 1-year overall mortality. Individuals in the high-benefit group (n = 1220), defined as those with ITEs greater than the median, are more likely to be younger, male, and to have higher refined Disease Risk Index (rDRI), T-cell phenotype, and grafts from related donors than those in the low-benefit group (n = 1220). The high-benefit approach (applying intensified MAC to individuals in the high-benefit group) shows the largest reduction in overall mortality at 1 year (risk difference [95% confidence interval], +5.94 percentage points [0.88 to 10.51], p = 0.011). In contrast, the high-risk approach (targeting patients with high or very high rDRI) does not achieve statistical significance (risk difference [95% confidence interval], +3.85 percentage points [−1.11 to 7.90], p = 0.063). These findings suggest that the high-benefit approach, targeting patients expected to benefit from intensified MAC, has the capacity to maximize HSCT effectiveness using intensified MAC. People with acute lymphoblastic leukemia (ALL), a blood cancer, can be treated by being transplanted with stem cells from other healthy people. However, in some people the cancer grows back after treatment. Intensified treatment, which combines additional chemotherapy with standard conditioning (called intensified myeloablative conditioning, intensified MAC), prior to transplant can reduce relapse but it remains unclear which patients will benefit most from this approach. We used a computational approach to analyse results from Japanese cancer patients. We identified a group of patients whose likelihood of death within 1 year was reduced by intensified MAC. Targeting these patients with intensified MAC could maximize treatment effectiveness and improve transplant outcomes. Jo, Inoue et al. use a machine-learning Bayesian causal forest algorithm to evaluate the effect of intensified myeloablative conditioning (MAC) on mortality following haematopoietic stem cell transplantation (HSCT). Intensified myeloablative conditioning (MAC) has heterogeneous effects on reducing mortality.