A biomarker risk assessment tool predicts infection-related mortality after allogeneic stem cell transplantation

Nonrelapse mortality (NRM) in allogeneic haemopoietic stem cell transplantation (alloHSCT) is driven by acute graft-versus-host disease (aGVHD) and infection. The MAGIC algorithm probability (MAP) composite biomarker score is based on serum ST2 and REG3a levels at day (D)7 after transplantation and predicts aGVHD-related mortality. Although changes in MAP score predict aGVHD treatment response, their posttransplant predictive value and association with infection-related NRM (iNRM), independent of GVHD, are unclear. We evaluated the association of D0 to D21 MAP change (MAPΔ) vs D7 MAP with 6-month NRM (6mNRM) and developed a risk stratification model using random forest analysis and recursive partitioning. We prospectively enrolled 101 adult alloHSCT recipients from 2022 to 2024. Serum was collected weekly from D0 to D21 to derive MAPΔ. Associations with 6mNRM were assessed using generalized linear and random forest modeling. 6mNRM was 14.8%, with 80% (12/15) of deaths attributed to infection without prior aGVHD. Both MAPΔ ≥0.055 and D7 MAP ≥0.16 were significantly associated with 6mNRM (P< .0001), with MAPΔ showing superior predictive accuracy (receiver operator characteristic area under the curve 0.934 vs 0.779). In multivariable analysis, MAPΔ (odds ratio, 45; 95% confidence interval, 10.6-318; P = .01), but not D7 MAP, was an independent predictor of 6mNRM. A classification tree incorporating MAPΔ, D7 MAP, and age-adjusted comorbidity index stratified patients into low, intermediate, and high-risk groups. MAPΔ was a powerful, independent predictor of 6mNRM, which was driven by infection rather than aGVHD in this cohort. We propose a tool based on MAPΔ to assess iNRM, which may guide interventions and clinical trial design. Further validation in larger cohorts is warranted.