Wainstein Marina, Flanagan Emily, Johnson David W, Shrapnel Sally
Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia.
Department of Medicine, West Moreton Kidney Health Service, Ipswich Hospital, Brisbane, QLD, Australia.
Front Nephrol. 2023 Aug 3;3:1220214. doi: 10.3389/fneph.2023.1220214. eCollection 2023.
Acute kidney injury (AKI) is one of the most common and consequential complications among hospitalized patients. Timely AKI risk prediction may allow simple interventions that can minimize or avoid the harm associated with its development. Given the multifactorial and complex etiology of AKI, machine learning (ML) models may be best placed to process the available health data to generate accurate and timely predictions. Accordingly, we searched the literature for externally validated ML models developed from general hospital populations using the current definition of AKI. Of 889 studies screened, only three were retrieved that fit these criteria. While most models performed well and had a sound methodological approach, the main concerns relate to their development and validation in populations with limited diversity, comparable digital ecosystems, use of a vast number of predictor variables and over-reliance on an easily accessible biomarker of kidney injury. These are potentially critical limitations to their applicability in diverse socioeconomic and cultural settings, prompting a need for simpler, more transportable prediction models which can offer a competitive advantage over the current tools used to predict and diagnose AKI.
急性肾损伤（AKI）是住院患者中最常见且后果严重的并发症之一。及时进行AKI风险预测或许能采取一些简单干预措施，将与其发展相关的危害降至最低或避免危害发生。鉴于AKI病因具有多因素性和复杂性，机器学习（ML）模型可能最适合处理现有的健康数据，以生成准确且及时的预测。因此，我们检索了文献，查找使用当前AKI定义从综合医院人群中开发的经过外部验证的ML模型。在筛选的889项研究中，仅检索到三项符合这些标准的研究。虽然大多数模型表现良好且方法合理，但主要问题在于它们在多样性有限的人群、类似的数字生态系统中开发和验证，使用大量预测变量以及过度依赖易于获取的肾损伤生物标志物。这些可能是其在不同社会经济和文化背景下适用性的关键限制，这促使需要更简单、更便于移植的预测模型，这些模型相较于目前用于预测和诊断AKI的工具可能具有竞争优势。
