Association of Elevated Urinary Concentration of Renin-Angiotensin System Components and Severe AKI

Urinary angiotensinogen and renin improve prediction of a clinical model for the outcome of Acute Kidney Injury Network stage 3 AKI or death. (A) Receiver-operating characteristic (ROC) curves are shown for the clinical model (includes Cleveland Clinic score and percentage change in serum creatinine at the time of sample collection), the clinical model plus angiotensinogen (uAnCR), and the clinical model plus uAnCR plus urinary renin (uRenCR). ROC curves were considered statistically significant if the 95% CI of the area under the ROC curve (AUC) did not overlap 0.5. (B–E) Scatterplots show the improvement in risk prediction gained by adding (B and C) uAnCR and (D and E) uRenCR to the multivariate clinical model. The diagonal gray line represents the line of identity, which indicates no change in the calculated risk between the model before and after addition of the biomarker. Data points represent the calculated risks for individual patients using the two models being compared. If the data point lies below the line of identity, addition of the biomarker lowers this patient’s calculated risk, whereas if the data point is above the line of identity, the addition of the biomarker increases the calculated risk. Addition of uAnCR to the clinical model resulted in a net lower calculated risk for (B) patients who did not meet the combined outcome (nonevents; category free net reclassification improvement [cfNRI] nonevents, 0.39) and a net higher calculated risk for (C) patients who did meet the outcome (events; cfNRI events, 0.28). Addition of uRenCR to the clinical model plus uAnCR resulted in a net higher calculated risk for (E) patients who met the outcome (events; cfNRI events, 0.44) and a modest net lower risk for (D) patients who did not meet the outcome (nonevents; cfNRI nonevents, 0.11). The integrated sensitivity and specificity plots (F and G) show the improvement in sensitivity and specificity gained by addition of the biomarkers. Addition of uAnCR to the clinical model resulted in a gain of both sensitivity (F) and specificity (G), while addition of uRenCR to the clinical model plus uAnCR increased sensitivity (F) but did not alter specificity (G).

Classification tree for the outcome of Acute Kidney Injury Network 3 AKI or death. Chi-squared automatic interaction detection (CHAID) was used to grow the classification tree using the following covariates: Cleveland Clinic score, percentage increase in serum creatinine at the time of sample collection, urinary angiotensinogen-to-creatinine ratio (uAnCR), and urinary renin-to-creatinine ratio (uRenCR). Statistical significance was determined using the chi-squared test. Interactions between covariates and the outcome were deemed statistically significant if P<0.05 with Bonferroni correction. Pie charts represent the proportion of patients who met the outcome (events) or not (nonevents) at each node of the tree. The model only used uAnCR and uRenCR to predict the outcome, and so the truncated version of classification tree in Supplemental Figure 1 is shown here. An overall accuracy of 90.2% (misclassification risk estimate ± SEM, 0.132±0.024) was obtained by classifying each patient using the uAnCR and uRenCR cutoffs reported above.