期刊
JCO CLINICAL CANCER INFORMATICS
卷 5, 期 -, 页码 494-507出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1200/CCI.20.00185
关键词
-
类别
资金
- US Office of Naval Research [N00014-18-1-2888]
- Advancing a Healthier Wisconsin Endowment at the Medical College of Wisconsin
- Public Health Service from National Cancer Institute (NCI) [U24CA076518]
- Public Health Service from National Heart, Lung and Blood Institute (NHLBI) [U24CA076518]
- Public Health Service from National Institute of Allergy and Infectious Diseases (NIAID) [U24CA076518]
- Health Resources and Services Administration (HRSA) [HHSH250201700006C, SC1MC31881-01-00, HHSH250201700007C]
- Office of Naval Research [N00014-18-1-2888, N00014-18-1-2850, N00014-20-1-2705]
- BARDA
- Be the Match Foundation
- Boston Children's Hospital
- Dana Farber
- Japan Hematopoietic Cell Transplantation Data Center
- St Baldrick's Foundation
- National Marrow Donor Program
- Medical College of Wisconsin
- AbbVie
- Actinium Pharmaceuticals Inc
- Adaptive Biotechnologies
- Adienne SA
- Allovir Inc
- Amgen Inc
- Angiocrine Bioscience
- Anthem Inc
- Astellas Pharma US
- AstraZeneca
- Atara Biotherapeutics Inc
- bluebird bio Inc
- Bristol Myers Squibb Co
- Celgene Corp
- CSL Behring
- CytoSen Therapeutics Inc
- Daiichi Sankyo Co, Ltd
- Gamida-Cell, Ltd
- Genzyme
- HistoGenetics Inc
- Incyte Corporation
- Janssen Biotech Inc
- Janssen/Johnson Johnson
- Jazz Pharmaceuticals Inc
- Kiadis Pharma
- Kite, a Gilead Company
- Kyowa Kirin
- Legend Biotech
- Magenta Therapeutics
- Mallinckrodt LLC
- Medac GmbH
- Merck Company Inc
- Merck Sharp Dohme Corp
- Millennium, the Takeda Oncology Co
- Miltenyi Biotec Inc
- Novartis Oncology
- Novartis Pharmaceuticals Corporation
- Omeros Corporation
- Oncoimmune Inc
- OptumHealth
- Orca Biosystems Inc
- Pfizer Inc
- Pharmacyclics, LLC
- REGiMMUNE Corp
- Sanofi Genzyme
- Shire
- Sobi Inc
- Takeda Pharma
- Terumo BCT
- Viracor Eurofins
- Xenikos BV
- NHLBI [R21HL140314, U01HL128568, U24HL138660]
- NCI [U24HL138660]
- [P01CA111412]
- [R01CA152108]
- [R01CA215134]
- [R01CA218285]
- [R01CA231141]
- [R01AI128775]
- [R01HL129472]
- [R01HL130388]
- [R01HL131731]
- [U01AI069197]
- [U01AI126612]
The study confirmed the importance of selecting the youngest available MUD for HCT, regardless of patient features. Age was the only donor feature that affected outcomes consistently. By choosing a younger MUD, there is potential for improvement in transplant outcomes.
PURPOSE Donor selection practices for matched unrelated donor (MUD) hematopoietic cell transplantation (HCT) vary, and the impact of optimizing donor selection in a patient-specific way using modern machine learning (ML) models has not been studied. METHODS We trained a Bayesian ML model in 10,318 patients who underwent MUD HCT from 1999 to 2014 to provide patient- and donor-specific predictions of clinically severe (grade 3 or 4) acute graft-versus-host disease or death by day 180. The model was validated in 3,501 patients from 2015 to 2016 with archived records of potential donors at search. Donor selection optimizing predicted outcomes was implemented over either an unlimited donor pool or the donors in the search archives. Posterior mean differences in outcomes from optimal donor selection versus actual practice were summarized per patient and across the population with 95% intervals. RESULTS Event rates were 33% (training) and 37% (validation). Among donor features, only age affected outcomes, with the effect consistent regardless of patient features. The median (interquartile range) difference in age between the youngest donor at search and the selected donor was 6 (1-10) years, whereas the number of donors per patient younger than the selected donor was 6 (1-36). Fourteen percent of the validation data set had an approximate 5% absolute reduction in event rates from selecting the youngest donor at search versus the actual donor used, leading to an absolute population reduction of 1% (95% interval, 0 to 3). CONCLUSION We confirmed the singular importance of selecting the youngest available MUD, irrespective of patient features, identified potential for improved HCT outcomes by selecting a younger MUD, and demonstrated use of novel ML models transferable to optimize other complex treatment decisions in a patient-specific way.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据