通过机器学习重新审视急性肾损伤肾脏的移植结果。

IF 1.9 4区医学 Q2 SURGERY Clinical Transplantation Pub Date : 2024-10-05 DOI:10.1111/ctr.15470

Caroline C. Jadlowiec, Charat Thongprayoon, Supawadee Suppadungsuk, Supawit Tangpanithandee, Napat Leeaphorn, Raymond Heilman, Matthew Cooper, Wisit Cheungpasitporn

{"title":"通过机器学习重新审视急性肾损伤肾脏的移植结果。","authors":"Caroline C. Jadlowiec, Charat Thongprayoon, Supawadee Suppadungsuk, Supawit Tangpanithandee, Napat Leeaphorn, Raymond Heilman, Matthew Cooper, Wisit Cheungpasitporn","doi":"10.1111/ctr.15470","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Despite many people awaiting kidney transplant, kidney allografts from acute kidney injury (AKI) donors continue to be underutilized. We aimed to cluster kidney transplant recipients of AKI kidney allografts using an unsupervised machine learning (ML) approach.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Using Organ Procurement and Transplantation Network–United Network for Organ Sharing (OPTN/UNOS) data, a consensus clustering cohort analysis was performed in 12 356 deceased donor kidney transplant recipients between 2015 and 2019 in whom donors had a terminal serum creatinine ≥1.5 mg/dL. Significant cluster characteristics were determined, and outcomes were compared.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The median donor terminal creatinine was 2.2 (interquartile range [IQR] 1.7–3.3) mg/dL. Cluster analysis was performed on 12 356 AKI kidney recipients, and three clinically distinct clusters were identified. Young, sensitized kidney re-transplant patients characterized Cluster 1. Cluster 2 was characterized by first-time kidney transplant patients with hypertensive and diabetic kidney diseases. Older diabetic recipients characterized Cluster 3. Clusters 1 and 2 donors were young and met standard kidney donor profile index (KDPI) criteria; Cluster 3 donors were older, more likely to have hypertension or diabetes, and meet high KDPI criteria. Cluster 1 had a higher risk of acute rejection, 3-year patient death, and graft failure. Cluster 3 had a higher risk of death-censored graft failure, patient death, and graft failure at 1 and 3 years. Cluster 2 had the best patient-, graft-, and death-censored graft survival at 1 and 3 years. Compared to non-AKI kidney recipients, the AKI clusters showed a higher incidence of delayed graft function (DGF, AKI: 43.2%, 41.7%, 45.3% vs. non-AKI: 25.5%); however, there were comparable long-term outcomes specific to death-censored graft survival (AKI: 93.6%, 93.4%, 90.4% vs. non-AKI: 92.3%), patient survival (AKI: 89.1%, 93.2%, 84.2% vs. non-AKI: 91.2%), and overall graft survival (AKI: 84.7%, 88.2%, 79.0% vs. non-AKI: 86.0%).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>In this unsupervised ML approach study, AKI recipient clusters demonstrated differing, but good clinical outcomes, suggesting opportunities for transplant centers to incrementally increase kidney utilization from AKI donors.</p>\n </section>\n </div>","PeriodicalId":10467,"journal":{"name":"Clinical Transplantation","volume":"38 10","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reexamining Transplant Outcomes in Acute Kidney Injury Kidneys Through Machine Learning\",\"authors\":\"Caroline C. Jadlowiec, Charat Thongprayoon, Supawadee Suppadungsuk, Supawit Tangpanithandee, Napat Leeaphorn, Raymond Heilman, Matthew Cooper, Wisit Cheungpasitporn\",\"doi\":\"10.1111/ctr.15470\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Despite many people awaiting kidney transplant, kidney allografts from acute kidney injury (AKI) donors continue to be underutilized. We aimed to cluster kidney transplant recipients of AKI kidney allografts using an unsupervised machine learning (ML) approach.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Using Organ Procurement and Transplantation Network–United Network for Organ Sharing (OPTN/UNOS) data, a consensus clustering cohort analysis was performed in 12 356 deceased donor kidney transplant recipients between 2015 and 2019 in whom donors had a terminal serum creatinine ≥1.5 mg/dL. Significant cluster characteristics were determined, and outcomes were compared.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>The median donor terminal creatinine was 2.2 (interquartile range [IQR] 1.7–3.3) mg/dL. Cluster analysis was performed on 12 356 AKI kidney recipients, and three clinically distinct clusters were identified. Young, sensitized kidney re-transplant patients characterized Cluster 1. Cluster 2 was characterized by first-time kidney transplant patients with hypertensive and diabetic kidney diseases. Older diabetic recipients characterized Cluster 3. Clusters 1 and 2 donors were young and met standard kidney donor profile index (KDPI) criteria; Cluster 3 donors were older, more likely to have hypertension or diabetes, and meet high KDPI criteria. Cluster 1 had a higher risk of acute rejection, 3-year patient death, and graft failure. Cluster 3 had a higher risk of death-censored graft failure, patient death, and graft failure at 1 and 3 years. Cluster 2 had the best patient-, graft-, and death-censored graft survival at 1 and 3 years. Compared to non-AKI kidney recipients, the AKI clusters showed a higher incidence of delayed graft function (DGF, AKI: 43.2%, 41.7%, 45.3% vs. non-AKI: 25.5%); however, there were comparable long-term outcomes specific to death-censored graft survival (AKI: 93.6%, 93.4%, 90.4% vs. non-AKI: 92.3%), patient survival (AKI: 89.1%, 93.2%, 84.2% vs. non-AKI: 91.2%), and overall graft survival (AKI: 84.7%, 88.2%, 79.0% vs. non-AKI: 86.0%).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>In this unsupervised ML approach study, AKI recipient clusters demonstrated differing, but good clinical outcomes, suggesting opportunities for transplant centers to incrementally increase kidney utilization from AKI donors.</p>\\n </section>\\n </div>\",\"PeriodicalId\":10467,\"journal\":{\"name\":\"Clinical Transplantation\",\"volume\":\"38 10\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Transplantation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/ctr.15470\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"SURGERY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Transplantation","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/ctr.15470","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SURGERY","Score":null,"Total":0}

引用次数: 0

摘要

背景：尽管有许多人在等待肾移植，但急性肾损伤（AKI）供体的肾脏异体移植仍未得到充分利用。我们的目标是使用无监督机器学习（ML）方法对AKI肾脏异体移植受者进行聚类：利用器官采购与移植网络-器官共享联合网络（OPTN/UNOS）的数据，对 2015 年至 2019 年期间 12 356 名死亡供体肾移植受者进行了共识聚类队列分析，这些受者的供体末期血清肌酐≥1.5 mg/dL。确定了重要的群组特征，并对结果进行了比较：供体末期血肌酐中位数为 2.2（四分位距[IQR] 1.7-3.3）mg/dL。对 12 356 名 AKI 肾脏受者进行了聚类分析，发现了三个临床上截然不同的聚类。年轻、敏感的肾脏再移植患者是群组 1 的特征。第 2 组的特征是患有高血压和糖尿病肾病的首次肾移植患者。老年糖尿病受者是第 3 组的特征。群组 1 和群组 2 的供体都很年轻，符合标准的肾脏供体档案指数（KDPI）标准；群组 3 的供体年龄较大，更有可能患有高血压或糖尿病，并符合较高的 KDPI 标准。第 1 组发生急性排斥反应、3 年患者死亡和移植物失败的风险较高。第 3 组发生死亡校验后的移植物失败、患者死亡以及 1 年和 3 年移植物失败的风险较高。第2组患者、移植物和死亡校正移植物1年和3年存活率最高。与非 AKI 肾脏受者相比，AKI 群组显示出更高的移植物功能延迟发生率（DGF，AKI：43.2%、41.7%、45.3% vs. 非 AKI：25.5%）；然而，死亡校验移植物存活率的长期结果却相当（AKI：93.6%、93.4%、90.4% vs. non-AKI：92.3%）、患者生存率（AKI：89.1%、93.2%、84.2% vs. non-AKI：91.2%）和总体移植物生存率（AKI：84.7%、88.2%、79.0% vs. non-AKI：86.0%）：在这项无监督 ML 方法研究中，AKI 受体群表现出不同但良好的临床结果，这表明移植中心有机会逐步提高 AKI 供体的肾脏利用率。

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

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Reexamining Transplant Outcomes in Acute Kidney Injury Kidneys Through Machine Learning

Background

Despite many people awaiting kidney transplant, kidney allografts from acute kidney injury (AKI) donors continue to be underutilized. We aimed to cluster kidney transplant recipients of AKI kidney allografts using an unsupervised machine learning (ML) approach.

Methods

Using Organ Procurement and Transplantation Network–United Network for Organ Sharing (OPTN/UNOS) data, a consensus clustering cohort analysis was performed in 12 356 deceased donor kidney transplant recipients between 2015 and 2019 in whom donors had a terminal serum creatinine ≥1.5 mg/dL. Significant cluster characteristics were determined, and outcomes were compared.

Results

The median donor terminal creatinine was 2.2 (interquartile range [IQR] 1.7–3.3) mg/dL. Cluster analysis was performed on 12 356 AKI kidney recipients, and three clinically distinct clusters were identified. Young, sensitized kidney re-transplant patients characterized Cluster 1. Cluster 2 was characterized by first-time kidney transplant patients with hypertensive and diabetic kidney diseases. Older diabetic recipients characterized Cluster 3. Clusters 1 and 2 donors were young and met standard kidney donor profile index (KDPI) criteria; Cluster 3 donors were older, more likely to have hypertension or diabetes, and meet high KDPI criteria. Cluster 1 had a higher risk of acute rejection, 3-year patient death, and graft failure. Cluster 3 had a higher risk of death-censored graft failure, patient death, and graft failure at 1 and 3 years. Cluster 2 had the best patient-, graft-, and death-censored graft survival at 1 and 3 years. Compared to non-AKI kidney recipients, the AKI clusters showed a higher incidence of delayed graft function (DGF, AKI: 43.2%, 41.7%, 45.3% vs. non-AKI: 25.5%); however, there were comparable long-term outcomes specific to death-censored graft survival (AKI: 93.6%, 93.4%, 90.4% vs. non-AKI: 92.3%), patient survival (AKI: 89.1%, 93.2%, 84.2% vs. non-AKI: 91.2%), and overall graft survival (AKI: 84.7%, 88.2%, 79.0% vs. non-AKI: 86.0%).

Conclusions

In this unsupervised ML approach study, AKI recipient clusters demonstrated differing, but good clinical outcomes, suggesting opportunities for transplant centers to incrementally increase kidney utilization from AKI donors.

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

Clinical Transplantation 医学-外科

CiteScore

3.70

自引率

4.80%

发文量

286

审稿时长

2 months

期刊介绍： Clinical Transplantation: The Journal of Clinical and Translational Research aims to serve as a channel of rapid communication for all those involved in the care of patients who require, or have had, organ or tissue transplants, including: kidney, intestine, liver, pancreas, islets, heart, heart valves, lung, bone marrow, cornea, skin, bone, and cartilage, viable or stored. Published monthly, Clinical Transplantation’s scope is focused on the complete spectrum of present transplant therapies, as well as also those that are experimental or may become possible in future. Topics include: Immunology and immunosuppression; Patient preparation; Social, ethical, and psychological issues; Complications, short- and long-term results; Artificial organs; Donation and preservation of organ and tissue; Translational studies; Advances in tissue typing; Updates on transplant pathology;. Clinical and translational studies are particularly welcome, as well as focused reviews. Full-length papers and short communications are invited. Clinical reviews are encouraged, as well as seminal papers in basic science which might lead to immediate clinical application. Prominence is regularly given to the results of cooperative surveys conducted by the organ and tissue transplant registries. Clinical Transplantation: The Journal of Clinical and Translational Research is essential reading for clinicians and researchers in the diverse field of transplantation: surgeons; clinical immunologists; cryobiologists; hematologists; gastroenterologists; hepatologists; pulmonologists; nephrologists; cardiologists; and endocrinologists. It will also be of interest to sociologists, psychologists, research workers, and to all health professionals whose combined efforts will improve the prognosis of transplant recipients.