PO-AKID-teller:用于预测急性 A 型主动脉夹层手术后需要透析的急性肾损伤的可解释机器学习工具

Qiuying Chen, Biao Fu, Jue Yang, Zhe Jin, Lu Zhang, Ruixin Fan, Bin Zhang, Shuixing Zhang
{"title":"PO-AKID-teller:用于预测急性 A 型主动脉夹层手术后需要透析的急性肾损伤的可解释机器学习工具","authors":"Qiuying Chen,&nbsp;Biao Fu,&nbsp;Jue Yang,&nbsp;Zhe Jin,&nbsp;Lu Zhang,&nbsp;Ruixin Fan,&nbsp;Bin Zhang,&nbsp;Shuixing Zhang","doi":"10.1002/mef2.77","DOIUrl":null,"url":null,"abstract":"<p>Postoperative acute kidney injury requiring dialysis (PO-AKID) is a serious adverse event that not only affects acute morbidity and mortality, but also long-term prognosis. Here, we developed a practical and explainable web-based calculator (PO-AKID-teller) to detect patients who might experience PO-AKID after acute type A aortic dissection (ATAAD) surgery. This retrospective study reviewed 549 patients undergoing ATAAD surgery from October 2016 to June 2021. PO-AKID frequency was 19.7% (108 of 549 patients). The initial dataset was split into an 80% training cohort (<i>n</i> = 439) and a 20% test cohort (<i>n</i> = 110). There were seven predictors that could indicate PO-AKID, including prior cardiovascular surgery, platelet, serum creatinine, the terminal site of dissection involvement, right coronary artery involvement, estimated blood loss, and urine output. Among six machine learning classifiers, the random forest model exhibited the best predictive performance, with an area under the curve of 0.863 in the training cohort and 0.763 in the test cohort. This model was translated into a web-based risk calculator PO-AKID-teller to estimate an individual's probability of PO-AKID. The PO-AKID-teller can accurately estimate an individual's risk for PO-AKID in an interpretable manner, which may aid in informed decision-making, patient counseling, perioperative optimization, and longer-term care provision.</p>","PeriodicalId":74135,"journal":{"name":"MedComm - Future medicine","volume":"3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.77","citationCount":"0","resultStr":"{\"title\":\"PO-AKID-teller: An interpretable machine learning tool for predicting acute kidney injury requiring dialysis after acute type A aortic dissection surgery\",\"authors\":\"Qiuying Chen,&nbsp;Biao Fu,&nbsp;Jue Yang,&nbsp;Zhe Jin,&nbsp;Lu Zhang,&nbsp;Ruixin Fan,&nbsp;Bin Zhang,&nbsp;Shuixing Zhang\",\"doi\":\"10.1002/mef2.77\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Postoperative acute kidney injury requiring dialysis (PO-AKID) is a serious adverse event that not only affects acute morbidity and mortality, but also long-term prognosis. Here, we developed a practical and explainable web-based calculator (PO-AKID-teller) to detect patients who might experience PO-AKID after acute type A aortic dissection (ATAAD) surgery. This retrospective study reviewed 549 patients undergoing ATAAD surgery from October 2016 to June 2021. PO-AKID frequency was 19.7% (108 of 549 patients). The initial dataset was split into an 80% training cohort (<i>n</i> = 439) and a 20% test cohort (<i>n</i> = 110). There were seven predictors that could indicate PO-AKID, including prior cardiovascular surgery, platelet, serum creatinine, the terminal site of dissection involvement, right coronary artery involvement, estimated blood loss, and urine output. Among six machine learning classifiers, the random forest model exhibited the best predictive performance, with an area under the curve of 0.863 in the training cohort and 0.763 in the test cohort. This model was translated into a web-based risk calculator PO-AKID-teller to estimate an individual's probability of PO-AKID. The PO-AKID-teller can accurately estimate an individual's risk for PO-AKID in an interpretable manner, which may aid in informed decision-making, patient counseling, perioperative optimization, and longer-term care provision.</p>\",\"PeriodicalId\":74135,\"journal\":{\"name\":\"MedComm - Future medicine\",\"volume\":\"3 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mef2.77\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MedComm - Future medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/mef2.77\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MedComm - Future medicine","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/mef2.77","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

需要透析的术后急性肾损伤(PO-AKID)是一种严重的不良事件,不仅会影响急性发病率和死亡率,还会影响长期预后。在此,我们开发了一种实用且可解释的网络计算器(PO-AKID-teller),用于检测急性 A 型主动脉夹层(ATAAD)术后可能发生 PO-AKID 的患者。这项回顾性研究回顾了2016年10月至2021年6月期间接受ATAAD手术的549名患者。PO-AKID发生率为19.7%(549例患者中有108例)。初始数据集分为80%的训练队列(n = 439)和20%的测试队列(n = 110)。有七种预测因素可预示 PO-AKID,包括既往心血管手术、血小板、血清肌酐、夹层累及的终末部位、右冠状动脉累及、估计失血量和尿量。在六种机器学习分类器中,随机森林模型的预测性能最好,在训练队列中的曲线下面积为 0.863,在测试队列中的曲线下面积为 0.763。该模型被转化为基于网络的风险计算器 PO-AKID-teller,用于估算个人患 PO-AKID 的概率。PO-AKID-teller能以可解释的方式准确估计个人的PO-AKID风险,这可能有助于知情决策、患者咨询、围手术期优化和长期护理的提供。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

PO-AKID-teller: An interpretable machine learning tool for predicting acute kidney injury requiring dialysis after acute type A aortic dissection surgery

PO-AKID-teller: An interpretable machine learning tool for predicting acute kidney injury requiring dialysis after acute type A aortic dissection surgery

Postoperative acute kidney injury requiring dialysis (PO-AKID) is a serious adverse event that not only affects acute morbidity and mortality, but also long-term prognosis. Here, we developed a practical and explainable web-based calculator (PO-AKID-teller) to detect patients who might experience PO-AKID after acute type A aortic dissection (ATAAD) surgery. This retrospective study reviewed 549 patients undergoing ATAAD surgery from October 2016 to June 2021. PO-AKID frequency was 19.7% (108 of 549 patients). The initial dataset was split into an 80% training cohort (n = 439) and a 20% test cohort (n = 110). There were seven predictors that could indicate PO-AKID, including prior cardiovascular surgery, platelet, serum creatinine, the terminal site of dissection involvement, right coronary artery involvement, estimated blood loss, and urine output. Among six machine learning classifiers, the random forest model exhibited the best predictive performance, with an area under the curve of 0.863 in the training cohort and 0.763 in the test cohort. This model was translated into a web-based risk calculator PO-AKID-teller to estimate an individual's probability of PO-AKID. The PO-AKID-teller can accurately estimate an individual's risk for PO-AKID in an interpretable manner, which may aid in informed decision-making, patient counseling, perioperative optimization, and longer-term care provision.

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
1.00
自引率
0.00%
发文量
0
×
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