Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/105714
Title: 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
Author: Shouval, Roni
Labopin, Myriam
Bondi, Ori
Mishan Shamay, Hila
Shimoni, Avichai
Ciceri, Fabio
Esteve Reyner, Jordi
Giebel, Sebastian
Gorin, Norbert C.
Schmid, Christoph
Polge, Emmanuelle
Aljurf, Mahmoud
Kroger, Nicolaus
Craddock, Charles
Bacigalupo, Andrea
Cornelissen, Jan J.
Baron, Frederic
Unger, Ron
Nagler, Arnon
Mohty, Mohamad
Keywords: Hematopoesi
Cèl·lules mare
Leucèmia mieloide
Algorismes genètics
Mortalitat
Hematopoiesis
Stem cells
Myeloid leukemia
Genetic algorithms
Mortality
Issue Date: 1-Oct-2015
Publisher: American Society of Clinical Oncology
Abstract: 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 (http://bioinfo.lnx.biu.ac.il/∼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.
Note: Reproducció del document publicat a: https://doi.org/10.1200/JCO.2014.59.1339
It is part of: Journal of Clinical Oncology, 2015, vol. 33, num. 28, p. 3144-3151
Related resource: https://doi.org/10.1200/JCO.2014.59.1339
URI: http://hdl.handle.net/2445/105714
ISSN: 0732-183X
Appears in Collections:Articles publicats en revistes (Medicina)

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