The Effect of GVHD on Long-term Outcomes after Peripheral Blood Allogeneic Stem Cell Transplantation from an HLA-identical Sibling in Adult Acute Lymphocytic Leukemia: A Landmark Analysis Approach in Competing Risks

Allogeneic Hematopoietic stem cell transplantation (HSCT) is the most effective therapy to prevent relapse in acute lymphocytic leukemia (ALL). This benefit is affected by non-relapse mortality (NRM) due to complications such as graft versus host disease (GVHD). A new approach in analyzing time-dependent covariates in competing risks is landmark analysis. So, the aim of this study is to evaluate the effect of acute and chronic GVHD on long-term outcomes, relapse and NRM, after allogeneic HSCT in adult ALL using landmark analysis. This study was conducted on 252 ALL patients who were allogeneic transplanted from an HLA-identical sibling with peripheral blood (PB) as the source of stem cell from 2004 to 2012 and were followed-up until 2013. In the first 100 days after transplant, a landmark analysis on days +10, +11, +12, +17, +24, and +31 was applied to assess the effect of acute GVHD on early relapse and NRM. Similarly, for patients alive and event-free at day +100 after transplant, a landmark analysis at time points day +101, months +4, +5, +6, +9, and +12 was applied to evaluate the effect of chronic GVHD on late relapse and NRM. Five-year LFS and OS were 35.0% (95% CI: 29.1, 42.2%) and 37.5% (95% CI: 31.3, 45.0%), respectively. Five-year cumulative incidence of relapse was 44.5% (95% CI: 37.9, 51.0%) while this was 20.4% (95% CI: 15.4, 26.0%) for NRM. The landmark analysis in the first 100 days after transplant showed that the grade III/IV of aGVHD has a lower risk of relapse but higher risk of NRM after adjustment for the EBMT risk score. For patients alive at day +100, cGVHD had no significant effect on relapse. Limited cGVHD had lower risk of NRM and after 6 month post-transplant the risk of NRM decreased and there were not important difference between the groups of cGVHD. Using advanced models enables us to estimate the effects more precisely and ultimately make inference more accurately.