Establishment of a Predictive Model for GvHD-free, Relapse-free Survival after Allogeneic HSCT using Ensemble Learning

Abstract

Graft-versus-host-disease-free, relapse-free survival (GRFS) is a useful composite endpoint that measures survival without relapse or significant morbidity after allogeneic hematopoietic stem cell transplantation (allo-HSCT). We aimed to develop a novel analytical method that appropriately handles right-censored data and competing risks to understand the risk for GRFS and each component of GRFS. This study was a retrospective data-mining study on a cohort of 2207 adult patients who underwent their first allo-HSCT at the Kyoto Stem Cell Transplantation Group (KSCTG), a multi-institutional joint research group of 17 transplantation centers in Japan. The primary endpoint was GRFS. A stacked ensemble of Cox proportional hazard regression and seven machine learning algorithms was applied to develop a prediction model. The median age of patients was 48 years. For GRFS, the stacked ensemble model achieved better predictive accuracy evaluated by C-index than other top-of-the-art competing risk models (ensemble model: 0.670, Cox-PH: 0.668, Random Survival Forest: 0.660, Dynamic DeepHit: 0.646). The probability of GRFS after 2 years was 30.54% for the high-risk and 40.69% for the low-risk group, respectively (hazard ratio [HR] compared to the low-risk group: 2.127; 95% CI: 1.19-3.80). We developed a novel predictive model for survival analysis that showed superior risk stratification to existing methods using a stacked ensemble of multiple machine learning algorithms.