Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation Mortality 100 Days After Transplantation Using a Machine Learning Algorithm: A European Group for Blood and Marrow Transplantation Acute Leukemia Working Party Retrospective Data Mining Study

Research output: Contribution to journalArticlepeer-review


  • Roni Shouval
  • Myriam Labopin
  • Ori Bondi
  • Hila Mishan-Shamay
  • Avichai Shimoni
  • Fabio Ciceri
  • Jordi Esteve
  • Sebastian Giebel
  • Norbert C Gorin
  • Christoph Schmid
  • Emmanuelle Polge
  • Mahmoud Aljurf
  • Nicolaus Kroger
  • Andrea Bacigalupo
  • Jan J Cornelissen
  • Frederic Baron
  • Ron Unger
  • Arnon Nagler
  • Mohamad Mohty

Colleges, School and Institutes


PURPOSE: Allogeneic hematopoietic stem-cell transplantation (HSCT) is potentially curative for acute leukemia (AL), but carries considerable risk. Machine learning algorithms, which are part of the data mining (DM) approach, may serve for transplantation-related mortality risk prediction.

PATIENTS AND METHODS: This work is a retrospective DM study on a cohort of 28,236 adult HSCT recipients from the AL registry of the European Group for Blood and Marrow Transplantation. The primary objective was prediction of overall mortality (OM) at 100 days after HSCT. Secondary objectives were estimation of nonrelapse mortality, leukemia-free survival, and overall survival at 2 years. Donor, recipient, and procedural characteristics were analyzed. The alternating decision tree machine learning algorithm was applied for model development on 70% of the data set and validated on the remaining data.

RESULTS: OM prevalence at day 100 was 13.9% (n=3,936). Of the 20 variables considered, 10 were selected by the model for OM prediction, and several interactions were discovered. By using a logistic transformation function, the crude score was transformed into individual probabilities for 100-day OM (range, 3% to 68%). The model's discrimination for the primary objective performed better than the European Group for Blood and Marrow Transplantation score (area under the receiver operating characteristics curve, 0.701 v 0.646; P<.001). Calibration was excellent. Scores assigned were also predictive of secondary objectives.

CONCLUSION: The alternating decision tree model provides a robust tool for risk evaluation of patients with AL before HSCT, and is available online (∼bondi/web1.html). It is presented as a continuous probabilistic score for the prediction of day 100 OM, extending prediction to 2 years. The DM method has proved useful for clinical prediction in HSCT.


Original languageEnglish
Pages (from-to)3144-3151
Number of pages10
JournalJournal of Clinical Oncology
Issue number28
Early online date3 Aug 2015
Publication statusPublished - 1 Oct 2015


  • Adult, Algorithms, Area Under Curve, Data Mining, Decision Support Techniques, Decision Trees, Disease Progression, Disease-Free Survival, Europe, Female, Hematopoietic Stem Cell Transplantation, Humans, Kaplan-Meier Estimate, Leukemia, Myeloid, Acute, Logistic Models, Machine Learning, Male, Middle Aged, Postoperative Complications, Precursor Cell Lymphoblastic Leukemia-Lymphoma, Predictive Value of Tests, ROC Curve, Registries, Reproducibility of Results, Retrospective Studies, Risk Assessment, Risk Factors, Time Factors, Transplantation, Homologous, Treatment Outcome, Journal Article, Research Support, Non-U.S. Gov't, Validation Studies