August 18, 2016Clinical Journal of the American Society of Nephrology114 citationsOpen Access

Urinary Biomarkers at the Time of AKI Diagnosis as Predictors of Progression of AKI among Patients with Acute Cardiorenal Syndrome

CCChunbo Chen XYXiaobing Yang YLYing Lei

Structured PICO

Do urinary biomarkers measured at the time of AKI diagnosis predict the progression of AKI in patients with acute cardiorenal syndrome?

Population

213 adults admitted for acute decompensated heart failure who developed Kidney Disease Improving Global Outcomes (KDIGO) stage 1 or 2 AKI (acute cardiorenal syndrome), screened from an initial cohort of 732 adults.

Intervention

Measurement of six renal injury biomarkers (urinary angiotensinogen [uAGT], urinary neutrophil gelatinase-associated lipocalin [uNGAL], plasma neutrophil gelatinase-associated lipocalin, urinary IL-18 [uIL-18], urinary kidney injury molecule-1, and urinary albumin-to-creatinine ratio) at the time of AKI diagnosis.

Comparator

Lowest tertile of the respective biomarkers, and clinical prediction model alone.

Outcome

AKI progression defined by worsening of AKI stage.hard clinical

Urinary biomarkers, particularly urinary angiotensinogen, measured at the time of AKI diagnosis significantly improve risk stratification for AKI progression and adverse outcomes in patients with acute cardiorenal syndrome.

Abstract

BACKGROUND AND OBJECTIVES: A major challenge in early treatment of acute cardiorenal syndrome (CRS) is the lack of predictors for progression of AKI. We aim to investigate the utility of urinary angiotensinogen and other renal injury biomarkers in predicting AKI progression in CRS. DESIGN, SETTINGS, PARTICIPANTS, 95% confidence interval 95% CI, 3.4 to 34.7), uNGAL (OR, 4.7; 95% CI, 1.7 to 13.4), and uIL-18 (OR, 3.6; 95% CI, 1.4 to 9.5). uAGT was the best predictor for both primary and secondary outcomes with area under the receiver operating curve of 0.78 and 0.85. These three biomarkers improved risk reclassification compared with the clinical model alone, with uAGT performing the best (category-free net reclassification improvement for primary and secondary outcomes of 0.76 95% CI, 0.46 to 1.06 and 0.93 95% CI, 0.50 to 1.36; P<0.001). Excellent performance of uAGT was further confirmed with bootstrap internal validation. CONCLUSIONS: uAGT, uNGAL, and uIL-18 measured at time of AKI diagnosis improved risk stratification and identified CRS patients at highest risk of adverse outcomes.

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