TY - JOUR
T1 - Prediction of allogeneic hematopoietic stem-cell transplantation mortality 100 days after transplantation using a machine learning algorithm
T2 - A European group for blood and marrow transplantation acute leukemia working party retrospective data mining study
AU - Shouval, Roni
AU - Labopin, Myriam
AU - Bondi, Ori
AU - Mishan-Shamay, Hila
AU - Shimoni, Avichai
AU - Ciceri, Fabio
AU - Esteve, Jordi
AU - Giebel, Sebastian
AU - Gorin, Norbert C.
AU - Schmid, Christoph
AU - Polge, Emmanuelle
AU - Aljurf, Mahmoud
AU - Kroger, Nicolaus
AU - Craddock, Charles
AU - Bacigalupo, Andrea
AU - Cornelissen, Jan J.
AU - Baron, Frederic
AU - Unger, Ron
AU - Nagler, Arnon
AU - Mohty, Mohamad
N1 - Publisher Copyright:
© 2015 by American Society of Clinical Oncology.
PY - 2015/10/1
Y1 - 2015/10/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84944351165&partnerID=8YFLogxK
U2 - 10.1200/JCO.2014.59.1339
DO - 10.1200/JCO.2014.59.1339
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C2 - 26240227
AN - SCOPUS:84944351165
SN - 0732-183X
VL - 33
SP - 3144
EP - 3151
JO - Journal of Clinical Oncology
JF - Journal of Clinical Oncology
IS - 28
ER -