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

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, & measurements In this prospective, multicenter study, we screened 732 adults who admitted for acute decompensated heart failure from September 2011 to December 2014, and evaluated whether renal injury biomarkers measured at time of AKI diagnosis can predict worsening of AKI. In 213 patients who developed Kidney Disease Improving Global Outcomes stage 1 or 2 AKI, six renal injury biomarkers, including 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, were measured at time of AM diagnosis. The primary outcome was AM progression defined by worsening of AKI stage (50 patients). The secondary outcome was AM progression with subsequent death (18 patients). Results After multivariable adjustment, the highest tertile of three urinary biomarkers remained associated with AKI progression compared with the lowest tertile: uAGT (odds ratio [OR], 10.8; 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 AM diagnosis improved risk stratification and identified CRS patients at highest risk of adverse outcomes.