利用捐献者管理目标登记队列中的捐献者危重症护理数据,开发改进的移植受者科学登记处已故捐献者心脏产量模型。

IF 8.9 2区 医学 Q1 SURGERY American Journal of Transplantation Pub Date : 2024-11-01 DOI:10.1016/j.ajt.2024.07.001
Elizabeth A. Swanson , Shaina Kian , Samantha Noreen , Gaya Shivega , Virginia McBride , Paul Lange , Mitchell B. Sally , Darren J. Malinoski
{"title":"利用捐献者管理目标登记队列中的捐献者危重症护理数据,开发改进的移植受者科学登记处已故捐献者心脏产量模型。","authors":"Elizabeth A. Swanson ,&nbsp;Shaina Kian ,&nbsp;Samantha Noreen ,&nbsp;Gaya Shivega ,&nbsp;Virginia McBride ,&nbsp;Paul Lange ,&nbsp;Mitchell B. Sally ,&nbsp;Darren J. Malinoski","doi":"10.1016/j.ajt.2024.07.001","DOIUrl":null,"url":null,"abstract":"<div><div>Organ procurement organizations (OPOs) face increasing regulatory scrutiny, and the performance of predictive models used to assess OPO performance is critical. We sought to determine whether adding deceased donor physiological and critical care data to the existing Scientific Registry of Transplant Recipients (SRTR) heart yield model would improve the model’s performance. Donor data and heart transplanted (yes/no), the outcome of interest, were obtained from the United Network for Organ Sharing Donor Management Goal (DMG) Registry for 19 141 donors after brain death, from 25 OPOs. The data were split into training and testing portions. Multivariable LASSO regression was used to develop a statistical model incorporating DMG data elements with the existing components of the SRTR model. The DMG + SRTR and SRTR models were applied to the test data to compare the predictive performance of the models. The sensitivity (84%-86%) and specificity (84%-86%) were higher for the DMG + SRTR model compared to the SRTR model (71%-75% and 76%-77%, respectively). For the DMG + SRTR model, the C-statistic was 0.92 to 0.93 compared to 0.80 to 0.81 for the SRTR model. DMG data elements improve the predictive performance of the heart yield model. The addition of DMG data elements to the Organ Procurement and Transplantation Network data collection requirements should be considered.</div></div>","PeriodicalId":123,"journal":{"name":"American Journal of Transplantation","volume":"24 11","pages":"Pages 2108-2120"},"PeriodicalIF":8.9000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of an improved Scientific Registry of Transplant Recipients deceased donor heart yield model using donor critical care data from the Donor Management Goal Registry cohort\",\"authors\":\"Elizabeth A. Swanson ,&nbsp;Shaina Kian ,&nbsp;Samantha Noreen ,&nbsp;Gaya Shivega ,&nbsp;Virginia McBride ,&nbsp;Paul Lange ,&nbsp;Mitchell B. Sally ,&nbsp;Darren J. Malinoski\",\"doi\":\"10.1016/j.ajt.2024.07.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Organ procurement organizations (OPOs) face increasing regulatory scrutiny, and the performance of predictive models used to assess OPO performance is critical. We sought to determine whether adding deceased donor physiological and critical care data to the existing Scientific Registry of Transplant Recipients (SRTR) heart yield model would improve the model’s performance. Donor data and heart transplanted (yes/no), the outcome of interest, were obtained from the United Network for Organ Sharing Donor Management Goal (DMG) Registry for 19 141 donors after brain death, from 25 OPOs. The data were split into training and testing portions. Multivariable LASSO regression was used to develop a statistical model incorporating DMG data elements with the existing components of the SRTR model. The DMG + SRTR and SRTR models were applied to the test data to compare the predictive performance of the models. The sensitivity (84%-86%) and specificity (84%-86%) were higher for the DMG + SRTR model compared to the SRTR model (71%-75% and 76%-77%, respectively). For the DMG + SRTR model, the C-statistic was 0.92 to 0.93 compared to 0.80 to 0.81 for the SRTR model. DMG data elements improve the predictive performance of the heart yield model. The addition of DMG data elements to the Organ Procurement and Transplantation Network data collection requirements should be considered.</div></div>\",\"PeriodicalId\":123,\"journal\":{\"name\":\"American Journal of Transplantation\",\"volume\":\"24 11\",\"pages\":\"Pages 2108-2120\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American Journal of Transplantation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1600613524004003\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SURGERY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Transplantation","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1600613524004003","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SURGERY","Score":null,"Total":0}
引用次数: 0

摘要

器官获取组织(OPO)面临着越来越多的监管审查,用于评估 OPO 性能的预测模型的性能至关重要。我们试图确定在现有的器官移植受者科学登记处(SRTR)心脏产量模型中添加已故捐献者生理和重症监护数据是否会提高该模型的性能。我们从器官共享联合网络(United Network for Organ Sharing)的器官捐献者管理目标(DMG)登记处获得了25个器官移植组织的19141名脑死亡后捐献者的捐献者数据和心脏移植结果(是/否)。数据分为训练和测试两部分。使用多变量 LASSO 回归法建立了一个统计模型,将 DMG 数据元素与 SRTR 模型的现有组成部分结合在一起。将 DMG+SRTR 模型和 SRTR 模型应用于测试数据,以比较模型的预测性能。与 SRTR 模型(分别为 71-75% 和 76-77%)相比,DMG+SRTR 模型的灵敏度(84-86%)和特异度(84-86%)更高。DMG+SRTR模型的C统计量为0.92-0.93,而SRTR模型的C统计量为0.80-0.81。DMG 数据元素提高了心脏产量模型的预测性能。应考虑在器官获取与移植网络数据收集要求中增加 DMG 数据元素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Development of an improved Scientific Registry of Transplant Recipients deceased donor heart yield model using donor critical care data from the Donor Management Goal Registry cohort
Organ procurement organizations (OPOs) face increasing regulatory scrutiny, and the performance of predictive models used to assess OPO performance is critical. We sought to determine whether adding deceased donor physiological and critical care data to the existing Scientific Registry of Transplant Recipients (SRTR) heart yield model would improve the model’s performance. Donor data and heart transplanted (yes/no), the outcome of interest, were obtained from the United Network for Organ Sharing Donor Management Goal (DMG) Registry for 19 141 donors after brain death, from 25 OPOs. The data were split into training and testing portions. Multivariable LASSO regression was used to develop a statistical model incorporating DMG data elements with the existing components of the SRTR model. The DMG + SRTR and SRTR models were applied to the test data to compare the predictive performance of the models. The sensitivity (84%-86%) and specificity (84%-86%) were higher for the DMG + SRTR model compared to the SRTR model (71%-75% and 76%-77%, respectively). For the DMG + SRTR model, the C-statistic was 0.92 to 0.93 compared to 0.80 to 0.81 for the SRTR model. DMG data elements improve the predictive performance of the heart yield model. The addition of DMG data elements to the Organ Procurement and Transplantation Network data collection requirements should be considered.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
18.70
自引率
4.50%
发文量
346
审稿时长
26 days
期刊介绍: The American Journal of Transplantation is a leading journal in the field of transplantation. It serves as a forum for debate and reassessment, an agent of change, and a major platform for promoting understanding, improving results, and advancing science. Published monthly, it provides an essential resource for researchers and clinicians worldwide. The journal publishes original articles, case reports, invited reviews, letters to the editor, critical reviews, news features, consensus documents, and guidelines over 12 issues a year. It covers all major subject areas in transplantation, including thoracic (heart, lung), abdominal (kidney, liver, pancreas, islets), tissue and stem cell transplantation, organ and tissue donation and preservation, tissue injury, repair, inflammation, and aging, histocompatibility, drugs and pharmacology, graft survival, and prevention of graft dysfunction and failure. It also explores ethical and social issues in the field.
期刊最新文献
Graft-derived extracellular vesicles transport miRNAs to modulate macrophage polarization after heart transplantation. Outside Front Cover Late ascites after bladder-drained pancreas transplantation Corrigendum to ‘Partial Bladder Transplantation with En Bloc Kidney Transplant—The First Case Report of a ‘Bladder Patch Technique’ in a Human’ [American Journal of Transplantation 8 (2008) 1060-1063] Implantation of autologous induced pluripotent stem cell-derived islets provides long-term insulin independence in a patient with type 1 diabetes
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1