{"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":"","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":null,"pages":null},"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\":\"\",\"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\":null,\"pages\":null},\"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}
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.
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.