Establishment of a predictive model for GVHD-free, relapse-free survival after allogeneic HSCT using ensemble learning
Makoto Iwasaki, Junya Kanda, Yasuyuki Arai, Tadakazu Kondo, Takayuki Ishikawa, Yasunori Ueda, Kazunori Imada, Takashi Akasaka, Akihito Yonezawa, Kazuhiro Yago, Masaharu Nohgawa, Naoyuki Anzai, Toshinori Moriguchi, Toshiyuki Kitano, Mitsuru Itoh, Nobuyoshi Arima, Tomoharu Takeoka, Mitsumasa Watanabe, Hirokazu Hirata, Kosuke Asagoe, Isao Miyatsuka, Le An, Masanori Miyanishi, Akifumi Takaori‐Kondo
Abstract
Graft-versus-host disease-free, relapse-free survival (GRFS) is a useful composite end point 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 within the Kyoto Stem Cell Transplantation Group, a multi-institutional joint research group of 17 transplantation centers in Japan. The primary end point was GRFS. A stacked ensemble of Cox Proportional Hazard (Cox-PH) regression and 7 machine-learning algorithms was applied to develop a prediction model. The median age for the patients was 48 years. For GRFS, the stacked ensemble model achieved better predictive accuracy evaluated by C-index than other state-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 group and 40.69% for the low-risk group (hazard ratio compared with 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.