Prediction of Acute Graft-Versus-Host Disease Following Allogeneic Hematopoietic Stem Cell Transplantation Using a Machine Learning Algorithm

This study was a database dependent retrospective cohort study analyzing the data of adult recipients of HSCT obtained from the registry of Japanese Society for Hematopoietic Cell Transplantation. Pre-HSCT parameters, such as those for patients, donors, conditioning regimens, and other procedures were retrieved from the database and introduced into the data mining approach. The alternating decision tree (ADTree) machine learning algorithm was applied to develop a model. This cohort was randomly divided into the training cohort (70% of the entire dataset) and the validation cohort (the remaining 30%). The algorithm was trained and tested using a 10-fold cross validation on the training cohort. The ADTree was validated in the validation cohort using the competitive risk hazard model.ResultsIn total, 26,695 patients transplanted from allogeneic donors since 1992 to 2016 were included in this study. More than half of the patients were treated for acute myeloid leukemia or myelodysplastic syndrome (50.9%), followed by acute lymphoblastic leukemia (19.2%) and non-Hodgkin lymphoma (8.3%). The cumulative incidence of grades 2-4 and 3-4 aGVHD was 42.8% (95% confident interval [CI], 42.2 - 43.4%) and 17.1% (95%CI, 16.6 - 17.5%), respectively.Predictive ADTree models were established using the training cohort (N = 17,244). Out of >30 variables considered, 15 variables, such as underlying disease, donor source, HLA and sex mismatch, conditioning regimen, GVHD prophylaxis, and donor ag...
Source: Blood - Category: Hematology Authors: Tags: 722. Clinical Allogeneic Transplantation: Acute and Chronic GVHD, Immune Reconstitution: GVHD Grading and Outcomes and Management Source Type: research