Urinary biomarkers for acute kidney injury: perspectives on translation

Abstract

Acute kidney injury (previously known as acute renal failure) is a common complication in hospitalized patients, and its incidence has risen significantly in the past 15 yr. Despite significant technical advances in therapeutics, the mortality and morbidity rates associated with acute kidney injury remain dismally high and have not appreciably improved during the past four decades. Although the serum creatinine concentration performs fairly well for estimating kidney function in patients with stable chronic kidney disease, it performs poorly in the setting of acute disease. An ideal biomarker for acute kidney injury would help clinicians and scientists diagnose the most common form of acute kidney injury in hospitalized patients, acute tubular necrosis, early and accurately and may aid to risk-stratify patients with acute kidney injury by predicting the need for renal replacement therapy, the duration of acute kidney injury, the length of stay, and mortality. Herein is reviewed the diagnostic and prognostic performance of several types of urinary biomarkers for the diagnosis and risk stratification of acute kidney injury. The major types of urinary biomarkers fall into three classes: (1) Inflammatory, (2) renal tubular proteins that are excreted into the urine after injury, and (3) surrogate markers of tubular injury. Also discussed are statistical issues in evaluating the accuracy of biomarkers as diagnostic tests. It is likely that a panel of biomarkers, rather than a single biomarker, will be needed to perform extremely well in these three situations.

Figure 1.

Potential roles of biomarkers. *Increased risk includes preexisting chronic kidney disease, older age, and renovascular disease; †stressors include various ischemic, inflammatory, and nephrotoxic insults such as cardiac surgery, sepsis, acute lung injury, kidney transplantation, and nephrotoxic agents. AKI, acute kidney injury; RRT, renal replacement therapy. Adapted from Acute Kidney Injury Network (AKIN) Working Group Meeting in Vancouver, BC, September 11 through September 14, 2006 (72).

Figure 2.

Correspondence between the true-positive fraction and the false-positive fraction of a marker and the odds ratio. Reprinted from reference (66), with permission.

Figure 3.

Predictiveness curves. The x axis represents percentile rank of the biomarker. The y axis represents the probability of identifying the disease. The horizontal line is prevalence of the disease. The predictiveness curve is better if it is farther away from prevalence line and useless if it is close to the prevalence line. (A) Predictiveness curve for a “poor biomarker.” In this scenario, the risk for disease with either very low or very high concentrations of biomarker varies little from baseline risk (prevalence of disease). (B) Predictiveness curve for a “good biomarker.” In this scenario, the risk for disease for individuals varies significantly from the baseline risk depending on whether the biomarker concentration is low (e.g., ≤40th percentile) or very high (≥80thpercentile).