Background Acute kidney injury (AKI) is a serious complication affecting up to 15% of hospitalized patients. Early diagnosis is critical to prevent irreversible kidney damage that could otherwise lead to significant morbidity and mortality. However, AKI is a clinically silent syndrome, and current detection primarily relies on measuring a rise in serum creatinine, an imperfect marker that can be slow to react to developing AKI. Over the past decade, new innovations have emerged in the form of biomarkers and artificial intelligence tools to aid in the early diagnosis and prediction of imminent AKI. Content This review summarizes and critically evaluates the latest developments in AKI detection and prediction by emerging biomarkers and artificial intelligence. Main guidelines and studies discussed herein include those evaluating clinical utilitiy of alternate filtration markers such as cystatin C and structural injury markers such as neutrophil gelatinase-associated lipocalin and tissue inhibitor of metalloprotease 2 with insulin-like growth factor binding protein 7 and machine learning algorithms for the detection and prediction of AKI in adult and pediatric populations. Recommendations for clinical practices considering the adoption of these new tools are also provided. Summary The race to detect AKI is heating up. Regulatory approval of select biomarkers for clinical use and the emergence of machine learning algorithms that can predict imminent AKI with high accuracy are all promising developments. But the race is far from being won. Future research focusing on clinical outcome studies that demonstrate the utility and validity of implementing these new tools into clinical practice is needed.

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生物标志物与机器：预测急性肾损伤的竞赛

背景 急性肾损伤 (AKI) 是一种严重并发症，影响高达 15% 的住院患者。早期诊断对于防止不可逆的肾脏损伤至关重要，否则可能导致严重的发病率和死亡率。然而，AKI 是一种临床上无症状的综合征，目前的检测主要依赖于测量血清肌酐的升高，这是一种不完善的标记物，对发生 AKI 的反应可能很慢。在过去的十年中，新的创新以生物标志物和人工智能工具的形式出现，以帮助对即将发生的 AKI 进行早期诊断和预测。内容 本综述总结并批判性地评估了新兴生物标志物和人工智能在 AKI 检测和预测方面的最新进展。本文讨论的主要指南和研究包括评估替代过滤标志物（例如半胱氨酸蛋白酶抑制剂 C）和结构损伤标志物（例如中性粒细胞明胶酶相关脂质运载蛋白和金属蛋白酶 2 与胰岛素样生长因子结合蛋白 7 的组织抑制剂）的临床效用以及机器学习算法成人和儿童群体中 AKI 的检测和预测。还提供了考虑采用这些新工具的临床实践建议。摘要 检测 AKI 的竞赛正在升温。监管部门批准临床使用的精选生物标志物以及能够高精度预测即将发生的 AKI 的机器学习算法的出现都是有希望的发展。但这场比赛还远未获胜。未来的研究需要侧重于临床结果研究，以证明将这些新工具应用于临床实践的实用性和有效性。