A machine learning-based workflow for predicting transplant outcomes in patients with sickle cell disease.

IF 5.1 2区 医学 Q1 HEMATOLOGY
Haiou Li, Vandana Sachdev, Xin Tian, My-Le Nguyen, Matthew Hsieh, Courtney Fitzhugh, Emily Limerick, Wynona Coles, Nancy Asomaning, Anna Conrey, Colin O Wu, Swee Lay Thein
{"title":"A machine learning-based workflow for predicting transplant outcomes in patients with sickle cell disease.","authors":"Haiou Li, Vandana Sachdev, Xin Tian, My-Le Nguyen, Matthew Hsieh, Courtney Fitzhugh, Emily Limerick, Wynona Coles, Nancy Asomaning, Anna Conrey, Colin O Wu, Swee Lay Thein","doi":"10.1111/bjh.19842","DOIUrl":null,"url":null,"abstract":"<p><p>Allogeneic haematopoietic cell transplantation (HCT) with HLA-matched sibling donor remains the most established curative therapeutic option for patients with sickle cell disease (SCD). However, it is not without risks, highlighting the need for a risk stratification system. Utilizing a machine learning (ML) approach that combines clinical and imaging variables, we identified red cell distribution width and renal organ damage as important risk factors for patients undergoing HCT. This ML-based algorithm, similar to an approach previously reported for predicting mortality in patients with SCD, should be applicable to risk factor discovery in similar studies.</p>","PeriodicalId":135,"journal":{"name":"British Journal of Haematology","volume":" ","pages":""},"PeriodicalIF":5.1000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"British Journal of Haematology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/bjh.19842","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEMATOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Allogeneic haematopoietic cell transplantation (HCT) with HLA-matched sibling donor remains the most established curative therapeutic option for patients with sickle cell disease (SCD). However, it is not without risks, highlighting the need for a risk stratification system. Utilizing a machine learning (ML) approach that combines clinical and imaging variables, we identified red cell distribution width and renal organ damage as important risk factors for patients undergoing HCT. This ML-based algorithm, similar to an approach previously reported for predicting mortality in patients with SCD, should be applicable to risk factor discovery in similar studies.

基于机器学习的工作流程,用于预测镰状细胞病患者的移植结果。
对于镰状细胞病(SCD)患者来说,与 HLA 匹配的同胞供者进行异基因造血细胞移植(HCT)仍是最成熟的治疗方案。然而,它并非没有风险,这就凸显了对风险分层系统的需求。利用结合临床和影像学变量的机器学习(ML)方法,我们确定了红细胞分布宽度和肾脏器官损伤是接受 HCT 的患者的重要风险因素。这种基于 ML 的算法与之前报道的预测 SCD 患者死亡率的方法类似,应适用于类似研究中的风险因素发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
8.60
自引率
4.60%
发文量
565
审稿时长
1 months
期刊介绍: The British Journal of Haematology publishes original research papers in clinical, laboratory and experimental haematology. The Journal also features annotations, reviews, short reports, images in haematology and Letters to the Editor.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信