• Charat Thongprayoon, Panupong Hansrivijit, Tarun Bathini, Saraschandra Vallabhajosyula, Poemlarp Mekraksakit, Wisit Kaewput, and Wisit Cheungpasitporn.
• Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
• J Clin Med. 2020 Jun 7; 9 (6).

AbstractCardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.

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