Acute Kidney Injury

Identification of patients at risk for acute kidney injury (AKI), early detection of kidney stress, and initiating interventions to avoid injury are recommended following surgery.

Even transient stage-1 (mild) AKI has a significant long-term impact on post cardiac surgery mortality. Urinary biomarkers, including tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor binding protein 7 (IGFBP7) have been shown to identify kidney's under stress (i.e. prior to injury), which are at risk for developing AKI. These markers are more sensitive then traditional measures of kidney function such as serum creatinine or urinary output.

The KDIGO Guidelines is the primary international source guideline for classification, diagnosis, management, and prevention of AKI. Published in 2012, KDIGO offers graded AKI-risk interventions for kidneys under stress, prior to the occurrence of staged AKI. Visit KDIGO website

Diagnosing renal dysfunction by elevated serum creatinine may be too late, as it may not increase until as much as 50% of the glomerular filtration rate is lost. Since several factors can influence the urine output, this parameter has a low specificity for renal injury.

Urinary biomarkers aim to identify patients at high-risk, prior to injury. This allows for the initiation of protocols, or AKI-prevention bundles, which generally include minimizing further nephro-toxic insults, use of additional monitoring, and optimizing hemodynamics and fluid management through goal-directed therapy.

Previous studies have shown, in both non-cardiac and cardiac surgery, that implementing protocols based on urinary biomarkers can reduce the incidence of AKI. Many protocols exist and, despite institutional variability, share several commonalities. These include:

• Identifying patients at risk for AKI (which in the case of CV surgery may be ALL patients).
• Measuring urinary biomarkers early in the patient's care, before major renal injury has occurred.
• Having a multi-disciplinary, team-based approach to diagnosis, monitoring, assessment, and interventions for patients identified at-risk for AKI.
• Making use of a formal AKI Prevention Bundle, which maximizes the use of algorithms and goal-directed interventions/decisions, to deliver consistent care.

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