Abstract
Postoperative acute kidney injury (AKI) is not only one of the most common postoperative complications but is also associated with increased in-hospital mortality, decreased survival for up to 10 years after surgery and an increased risk for progression to chronic kidney disease and hemodialysis. Most of the studies that have developed clinically applicable risk models for prediction of AKI have focused on the most severe stages of AKI and rarely on less severe stages defined by consensus definitions. Furthermore, although multiple physiological signals are continuously recorded as a part of intraoperative management, their use for the development of risk models for AKI has been limited. Accurate risk stratification of patients in real time would enable the selection of optimal therapy in a timely fashion to prevent AKI altogether, or to mitigate the effects of the complication even before symptoms arise and can be tailored to a patients' personal clinical profile.