Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Charat Thongprayoon; Panupong Hansrivijit; Tarun Bathini; Saraschandra Vallabhajosyula; Poemlarp Mekraksakit; Wisit Kaewput; Wisit Cheungpasitporn

doi:10.3390/jcm9061767

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

J Clin Med. 2020 Jun 7;9(6):1767. doi: 10.3390/jcm9061767.

Authors

Charat Thongprayoon¹, Panupong Hansrivijit², Tarun Bathini³, Saraschandra Vallabhajosyula⁴, Poemlarp Mekraksakit⁵, Wisit Kaewput⁶, Wisit Cheungpasitporn⁷

Affiliations

¹ Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
² Department of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USA.
³ Department of Internal Medicine, University of Arizona, Tucson, AZ 85724, USA.
⁴ Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA.
⁵ Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79424, USA.
⁶ Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand.
⁷ Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA.

Abstract

Cardiac 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.

Keywords: AKI; acute kidney injury; artificial intelligence; cardiac surgery; machine learning; nephrology.

Publication types

Editorial