ICU digital health tools and biomarkers in renal system care

Acute kidney injury (AKI) is a common but complex clinical syndrome, especially among critically ill patients. Current consensus definitions for AKI rely only on changes in serum creatinine and urine output, which limits timely detection of renal injury and recovery. This review summarizes the recent literature on AKI biomarkers and digital tools investigated to optimize AKI care.

There is a growing body of literature on the use of biochemical, imaging, and functional biomarkers to detect AKI prior to changes in creatinine and urine output, ideally to provide a window for potential intervention and prevention of worsening renal injury. Biomarkers have also been shown to help prognosticate progression, need for dialysis, or recovery. Additionally, machine learning models have been developed and studied with similar goals. This review summarizes various biomarkers and machine learning models investigated to predict and prognosticate AKI in several critical care populations. Furthermore, this review discusses other digital tools such as electronic alerts and remote monitoring programs implemented to optimize AKI care.

The studies included in this review provide optimism for an abundance of novel resources to improve AKI care. Further validation is needed prior to utilization in clinical practice.