Biomarker Enrichment in Sepsis-Associated Acute Kidney Injury: Finding High-Risk Patients in the Intensive Care Unit.

IF 4.3 3区 医学 Q1 UROLOGY & NEPHROLOGY
American Journal of Nephrology Pub Date : 2024-01-01 Epub Date: 2023-10-16 DOI:10.1159/000534608
Louis Baeseman, Samantha Gunning, Jay L Koyner
{"title":"Biomarker Enrichment in Sepsis-Associated Acute Kidney Injury: Finding High-Risk Patients in the Intensive Care Unit.","authors":"Louis Baeseman, Samantha Gunning, Jay L Koyner","doi":"10.1159/000534608","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Sepsis-associated acute kidney injury (AKI) is a leading comorbidity in admissions to the intensive care unit. While a gold standard definition exists, it remains imperfect and does not allow for the timely identification of patients in the setting of critical illness. This review will discuss the use of biochemical and electronic biomarkers to allow for prognostic and predictive enrichment of patients with sepsis-associated AKI over and above the use of serum creatinine and urine output.</p><p><strong>Summary: </strong>Current data suggest that several biomarkers are capable of identifying patients with sepsis at risk for the development of severe AKI and other associated morbidity. This review discusses these data and these biomarkers in the setting of sub-phenotyping and endotyping sepsis-associated AKI. While not all these tests are widely available and some require further validation, in the near future we anticipate several new tools to help nephrologists and other providers better care for patients with sepsis-associated AKI.</p><p><strong>Key messages: </strong>Predictive and prognostic enrichment using both traditional biomarkers and novel biomarkers in the setting of sepsis can identify subsets of patients with either similar outcomes or similar pathophysiology, respectively. Novel biomarkers can identify kidney injury in patients without consensus definition AKI (e.g., changes in creatinine or urine output) and can predict other adverse outcomes (e.g., severe consensus definition AKI, inpatient mortality). Finally, emerging artificial intelligence and machine learning-derived risk models are able to predict sepsis-associated AKI in critically ill patients using advanced learning techniques and several laboratory and vital sign measurements.</p>","PeriodicalId":7570,"journal":{"name":"American Journal of Nephrology","volume":" ","pages":"72-85"},"PeriodicalIF":4.3000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10872813/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Nephrology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000534608","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/10/16 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Sepsis-associated acute kidney injury (AKI) is a leading comorbidity in admissions to the intensive care unit. While a gold standard definition exists, it remains imperfect and does not allow for the timely identification of patients in the setting of critical illness. This review will discuss the use of biochemical and electronic biomarkers to allow for prognostic and predictive enrichment of patients with sepsis-associated AKI over and above the use of serum creatinine and urine output.

Summary: Current data suggest that several biomarkers are capable of identifying patients with sepsis at risk for the development of severe AKI and other associated morbidity. This review discusses these data and these biomarkers in the setting of sub-phenotyping and endotyping sepsis-associated AKI. While not all these tests are widely available and some require further validation, in the near future we anticipate several new tools to help nephrologists and other providers better care for patients with sepsis-associated AKI.

Key messages: Predictive and prognostic enrichment using both traditional biomarkers and novel biomarkers in the setting of sepsis can identify subsets of patients with either similar outcomes or similar pathophysiology, respectively. Novel biomarkers can identify kidney injury in patients without consensus definition AKI (e.g., changes in creatinine or urine output) and can predict other adverse outcomes (e.g., severe consensus definition AKI, inpatient mortality). Finally, emerging artificial intelligence and machine learning-derived risk models are able to predict sepsis-associated AKI in critically ill patients using advanced learning techniques and several laboratory and vital sign measurements.

脓毒症相关急性肾损伤的生物标志物富集:在ICU中发现高危患者。
摘要:背景:脓毒症相关急性肾损伤(AKI)是重症监护病房的主要合并症。虽然存在金标准定义,但它仍然不完善,不允许在危重症情况下及时识别患者。这篇综述将讨论生物化学和电子生物标志物的使用,以使败血症相关AKI患者的预后和预测性富集超过血清肌酐和尿量的使用。摘要:目前的数据表明,几种生物标志物能够识别败血症患者,这些患者有发展为严重急性肾损伤和其他相关发病率的风险。这篇综述讨论了这些数据和这些生物标志物在亚表型和内分型败血症相关AKI中的作用。虽然并非所有这些测试都能广泛使用,有些还需要进一步验证,但在不久的将来,我们预计会有一些新的工具来帮助肾脏科医生和其他提供者更好地护理败血症相关AKI患者。关键信息:在败血症的情况下,使用传统生物标志物和新型生物标志物进行预测和预后富集,可以分别识别具有相似结果或相似病理生理学的患者亚群。新的生物标志物可以识别没有一致定义AKI的患者的肾损伤(例如肌酸酐或尿量的变化);并且可以预测其他不良结果(例如严重一致定义AKI、住院死亡率)。最后,新兴的人工智能和机器学习衍生的风险模型能够使用先进的学习技术以及几种实验室和生命体征测量来预测危重患者败血症相关的AKI。
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
American Journal of Nephrology
American Journal of Nephrology 医学-泌尿学与肾脏学
CiteScore
7.50
自引率
2.40%
发文量
74
审稿时长
4-8 weeks
期刊介绍: The ''American Journal of Nephrology'' is a peer-reviewed journal that focuses on timely topics in both basic science and clinical research. Papers are divided into several sections, including:
×
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