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

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; Charles Craddock; Andrea Bacigalupo; Jan J Cornelissen; Frederic Baron; Ron Unger; Arnon Nagler; Mohamad Mohty

doi:10.1200/JCO.2014.59.1339

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

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, Charles Craddock, Andrea Bacigalupo, Jan J Cornelissen, Frederic Baron, Ron Unger, Arnon Nagler, Mohamad Mohty

Cancer Clinical Trials Unit

Research output: Contribution to journal › Article › peer-review

62 Citations (Scopus)

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.

Original language	English
Pages (from-to)	3144-3151
Number of pages	10
Journal	Journal of Clinical Oncology
Volume	33
Issue number	28
Early online date	3 Aug 2015
DOIs	https://doi.org/10.1200/JCO.2014.59.1339
Publication status	Published - 1 Oct 2015

Keywords

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

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Shouval, R., Labopin, M., Bondi, O., Mishan-Shamay, H., Shimoni, A., Ciceri, F., Esteve, J., Giebel, S., Gorin, N. C., Schmid, C., Polge, E., Aljurf, M., Kroger, N., Craddock, C., Bacigalupo, A., Cornelissen, J. J., Baron, F., Unger, R., Nagler, A., & Mohty, M. (2015). 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. Journal of Clinical Oncology , 33(28), 3144-3151. Advance online publication. https://doi.org/10.1200/JCO.2014.59.1339

Shouval, Roni ; Labopin, Myriam ; Bondi, Ori et al. / 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. In: Journal of Clinical Oncology . 2015 ; Vol. 33, No. 28. pp. 3144-3151.

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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.",

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

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Shouval, R, Labopin, M, Bondi, O, Mishan-Shamay, H, Shimoni, A, Ciceri, F, Esteve, J, Giebel, S, Gorin, NC, Schmid, C, Polge, E, Aljurf, M, Kroger, N, Craddock, C, Bacigalupo, A, Cornelissen, JJ, Baron, F, Unger, R, Nagler, A & Mohty, M 2015, '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', Journal of Clinical Oncology , vol. 33, no. 28, pp. 3144-3151. https://doi.org/10.1200/JCO.2014.59.1339

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. / Shouval, Roni; Labopin, Myriam; Bondi, Ori et al.
In: Journal of Clinical Oncology , Vol. 33, No. 28, 01.10.2015, p. 3144-3151.

Research output: Contribution to journal › Article › peer-review

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

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.

KW - Adult

KW - Algorithms

KW - Area Under Curve

KW - Data Mining

KW - Decision Support Techniques

KW - Decision Trees

KW - Disease Progression

KW - Disease-Free Survival

KW - Europe

KW - Female

KW - Hematopoietic Stem Cell Transplantation

KW - Humans

KW - Kaplan-Meier Estimate

KW - Leukemia, Myeloid, Acute

KW - Logistic Models

KW - Machine Learning

KW - Male

KW - Middle Aged

KW - Postoperative Complications

KW - Precursor Cell Lymphoblastic Leukemia-Lymphoma

KW - Predictive Value of Tests

KW - ROC Curve

KW - Registries

KW - Reproducibility of Results

KW - Retrospective Studies

KW - Risk Assessment

KW - Risk Factors

KW - Time Factors

KW - Transplantation, Homologous

KW - Treatment Outcome

KW - Journal Article

KW - Research Support, Non-U.S. Gov't

KW - Validation Studies

U2 - 10.1200/JCO.2014.59.1339

DO - 10.1200/JCO.2014.59.1339

M3 - Article

C2 - 26240227

SN - 0732-183X

VL - 33

SP - 3144

EP - 3151

JO - Journal of Clinical Oncology

JF - Journal of Clinical Oncology

IS - 28

ER -

Shouval R, Labopin M, Bondi O, Mishan-Shamay H, Shimoni A, Ciceri F et al. 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. Journal of Clinical Oncology . 2015 Oct 1;33(28):3144-3151. Epub 2015 Aug 3. doi: 10.1200/JCO.2014.59.1339