Section of Nephrology, Department of Medicine, University of Chicago, Chicago, Illinois, USA.
Am J Nephrol. 2024;55(1):72-85. doi: 10.1159/000534608. Epub 2023 Oct 16.
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.
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.
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.
脓毒症相关性急性肾损伤(AKI)是重症监护病房收治患者的主要合并症。虽然存在金标准定义,但它并不完善,无法在危重病患者中及时识别患者。本综述将讨论使用生化和电子生物标志物来预测和预测脓毒症相关性 AKI 患者的预后,以超过使用血清肌酐和尿量的方法。
目前的数据表明,几种生物标志物能够识别患有脓毒症的患者,这些患者有发生严重 AKI 和其他相关发病率的风险。本综述讨论了这些数据以及这些生物标志物在亚表型和脓毒症相关性 AKI 的终表型中的应用。虽然并非所有这些检测都广泛可用,有些需要进一步验证,但在不久的将来,我们预计会有一些新的工具来帮助肾脏病学家和其他提供者更好地照顾脓毒症相关性 AKI 患者。
在脓毒症的背景下,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,可以分别识别出具有相似结局或相似病理生理学的患者亚组。新型生物标志物可以在没有共识定义 AKI 的患者(例如肌酐或尿量的变化)中识别出肾脏损伤,并预测其他不良结局(例如严重共识定义 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和几种实验室及生命体征测量值来预测重症监护病房中脓毒症相关性 AKI。