Heterogeneity of Survival Benefit Conferred By Letermovir

Variation in treatment effects based on individual patient characteristics-known as treatment effect heterogeneity or effect modification-has recently gained significant attention. A previous clinical trial and its post hoc analysis suggested that letermovir (LTV) may reduce mortality more in some patients than in others. We hypothesized that the survival benefit of LTV differs according to each patient's specific characteristics. This study aimed to identify patient characteristics that are associated with significant survival benefits from LTV. Patients who underwent transplantation between 2018 and 2022 were randomly divided into training (n = 5779) and validation groups (n = 2865). We developed two models: one using a proportional hazards model with interaction terms (PI), and another using a modern machine learning (ML) approach to detect heterogeneity in the survival benefit-specifically, to identify patient characteristics associated with greater benefit from LTV. In our cohort, 60% of patients received LTV as prophylaxis. In the training cohort, the final PI model, using additive interactions, identified advanced age (≥60), high comorbidities (HCT-CI ≥3), umbilical cord blood (UCB), and haploidentical HCT with post cyclophosphamide (PTCy Haplo) as highly beneficial factors. Meanwhile, the ML model, using a causal forest algorithm, classified the top 60% of patients based on the estimated individual treatment effect as the high benefit group. In the validation group, 67.1% and 59.9% of patients were considered to be high benefit by the PI and ML models, respectively. The absolute difference in 6-month NRM (LTV versus no LTV) in the high benefit group (PI model: 9.8% versus 16.3%; ML model: 11.3% versus 16.3%) was greater than that in the low benefit group (PI model: 4.3% versus 6.9%; ML model: 4.1% versus 6.2%). Most patients (>80%) with advanced age, high comorbidities, or UCB were classified as high benefit by the ML model, supporting the robustness of the PI model. Our models successfully identified patients who could be expected to experience lower NRM with LTV prophylaxis, underscoring the importance of personalized medicine.