Faculty Collaboration Database

Evaluation of a Machine Learning-Based Prognostic Model for Unrelated Hematopoietic Cell Transplantation Donor Selection. Biol Blood Marrow Transplant 2018 Jun;24(6):1299-1306

Date

02/08/2018

Pubmed ID

29410341

Pubmed Central ID

PMC5993610

DOI

10.1016/j.bbmt.2018.01.038

Scopus ID

2-s2.0-85043462058 (requires institutional sign-in at Scopus site)   13 Citations

Abstract

The survival of patients undergoing hematopoietic cell transplantation (HCT) from unrelated donors for acute leukemia exhibits considerable variation, even after stringent genetic matching. To improve the donor selection process, we attempted to create an algorithm to quantify the likelihood of survival to 5 years after unrelated donor HCT for acute leukemia, based on the clinical characteristics of the donor selected. All standard clinical variables were included in the model, which also included average leukocyte telomere length of the donor based on its association with recipient survival in severe aplastic anemia, and links to multiple malignancies. We developed a multivariate classifier that assigned a Preferred or NotPreferred label to each prospective donor based on the survival of the recipient. In a previous analysis using a resampling method, recipients with donors labeled Preferred experienced clinically compelling better survival compared with those labeled NotPreferred by the test. However, in a pivotal validation study in an independent cohort of 522 patients, the overall survival of the Preferred and NotPreferred donor groups was not significantly different. Although machine learning approaches have successfully modeled other biological phenomena and have led to accurate predictive models, our attempt to predict HCT outcomes after unrelated donor transplantation was not successful.

Author List

Buturovic L, Shelton J, Spellman SR, Wang T, Friedman L, Loftus D, Hesterberg L, Woodring T, Fleischhauer K, Hsu KC, Verneris MR, Haagenson M, Lee SJ

Author

Tao Wang PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Acute Disease
Algorithms
Donor Selection
Hematopoietic Stem Cell Transplantation
Humans
Leukemia
Machine Learning
Predictive Value of Tests
Prognosis
Survival Rate
Unrelated Donors