Clinical Division and Laboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium.
Division of Nephrology and Hypertension.
Curr Opin Crit Care. 2020 Dec;26(6):563-573. doi: 10.1097/MCC.0000000000000775.
Acute kidney injury (AKI) frequently complicates hospital admission, especially in the ICU or after major surgery, and is associated with high morbidity and mortality. The risk of developing AKI depends on the presence of preexisting comorbidities and the cause of the current disease. Besides, many other parameters affect the kidney function, such as the state of other vital organs, the host response, and the initiated treatment. Advancements in the field of informatics have led to the opportunity to store and utilize the patient-related data to train and validate models to detect specific patterns and, as such, predict disease states or outcomes.
Machine-learning techniques have also been applied to predict AKI, as well as the patients' outcomes related to their AKI, such as mortality or the need for kidney replacement therapy. Several models have recently been developed, but only a few of them have been validated in external cohorts.
In this article, we provide an overview of the machine-learning prediction models for AKI and its outcomes in critically ill patients and individuals undergoing major surgery. We also discuss the pitfalls and the opportunities related to the implementation of these models in clinical practices.
急性肾损伤（AKI）常并发于住院期间，尤其是在重症监护病房或大手术后，且与高发病率和死亡率相关。发生 AKI 的风险取决于是否存在预先存在的合并症以及当前疾病的病因。此外，许多其他参数也会影响肾功能，如其他重要器官的状态、宿主反应和已启动的治疗。信息学领域的进步使得存储和利用与患者相关的数据以训练和验证模型来检测特定模式成为可能，从而预测疾病状态或结局。
机器学习技术也已应用于预测 AKI 以及与 AKI 相关的患者结局，如死亡率或需要肾脏替代治疗。最近已经开发了几个模型，但只有少数模型在外部队列中得到了验证。
本文综述了机器学习在重症患者和大手术患者中预测 AKI 及其结局的模型，并讨论了在临床实践中实施这些模型的局限性和机遇。
