Machine learning approaches are evolving to predict cardiac surgery-associated acute kidney injury, potentially overcoming the limitations and variability of traditional risk assessment scores.
Can machine learning approaches accurately predict acute kidney injury after cardiac surgery?
Machine learning approaches offer evolving methodologies for predicting acute kidney injury following cardiac surgery, potentially improving upon traditional risk scores.
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.
Thongprayoon et al. (Sun,) conducted a review in Cardiac surgery-associated acute kidney injury (CSA-AKI). Machine learning approaches for risk prediction was evaluated. Machine learning approaches are evolving to predict cardiac surgery-associated acute kidney injury, potentially overcoming the limitations and variability of traditional risk assessment scores.
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