Abstract
Acute Kidney Injury (AKI) is a complex heterogeneous syndrome that often can go unrecognized and is encountered in multiple clinical settings. One strategy for proactive identification of AKI has been through electronic alerts (e-alerts) to improve clinical outcomes. The two traditional criteria for AKI diagnosis and staging have been urinary output and serum creatinine. The latter has dominated in aiding identification and prediction of AKI by alert models. While creatinine can provide information to estimate glomerular filtration rate, the utility to depict real-time change in rapidly declining kidney function is paradoxical. Alerts for AKI have recently been popularized by several studies in the UK showcasing the various use cases for detection and management by simply relying on creatinine changes. Predictive models for real-time alerting to AKI have also gone beyond simple delta checks of creatinine as reviewed here, and hold promise to leverage data contained beyond the laboratory domain. However, laboratory data still remains vital to e-alerts in AKI. Here, we highlight a select number of approaches for real-time alerting to AKI built on traditional consensus definitions, evaluate impact on clinical outcomes from e-alerts, and offer critiques on new and expanded definitions of AKI.