Combining functional and tubular damage biomarkers improves diagnostic precision for acute kidney injury after cardiac surgery

Abstract

Background: Increases in serum creatinine (ΔSCr) from baseline signify acute kidney injury (AKI) but offer little granular information regarding its characteristics. The 10th Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) suggested that combining AKI biomarkers would provide better precision for AKI course prognostication.

Objectives: This study investigated the value of combining a functional damage biomarker (plasma cystatin C [pCysC]) with a tubular damage biomarker (urine neutrophil gelatinase-associated lipocalin [uNGAL]), forming a composite biomarker for prediction of discrete characteristics of AKI.

Methods: Data from 345 children after cardiopulmonary bypass (CPB) were analyzed. Severe AKI was defined as Kidney Disease Global Outcomes Initiative stages 2 to 3 (≥100% ΔSCr) within 7 days of CPB. Persistent AKI lasted >2 days. SCr in reversible AKI returned to baseline ≤48 h after CPB. The composite of uNGAL (>200 ng/mg urine Cr = positive [+]) and pCysC (>0.8 mg/l = positive [+]), uNGAL+/pCysC+, measured 2 h after CPB initiation, was compared to ΔSCr increases of ≥50% for correlation with AKI characteristics by using predictive probabilities, likelihood ratios (LR), and area under the curve receiver operating curve (AUC-ROC) values [Corrected].

Results: Severe AKI occurred in 18% of patients. The composite uNGAL+/pCysC+ demonstrated a greater likelihood than ΔSCr for severe AKI (+LR: 34.2 [13.0:94.0] vs. 3.8 [1.9:7.2]) and persistent AKI (+LR: 15.6 [8.8:27.5] versus 4.5 [2.3:8.8]). In AKI patients, the uNGAL-/pCysC+ composite was superior to ΔSCr for prediction of transient AKI. Biomarker composites carried greater probability for specific outcomes than ΔSCr strata.

Conclusions: Composites of functional and tubular damage biomarkers are superior to ΔSCr for predicting discrete characteristics of AKI.

Keywords: Acute Dialysis Quality Initiative; acute kidney injury phenotypes; biomarker combinations; cardiac surgery; functional acute kidney injury; pediatric acute kidney injury.

FIGURE 1. Side-by-Side 2 × 2 Tables Show 4 Different AKI Phenotypes Based on Pathophysiology of Injury Versus Translation When Using Combinations of Biomarkers to Detect the Corresponding Injuries

Transient AKI, formerly known as “pre-renal” AKI, falls into the category of loss of function without damage (bottom left of each table). Damage with loss of function is a combination of functional and tubular injury. Adapted with permission from Endre et al. (6). AKI = acute kidney injury.

FIGURE 2. Transposition of Table of Likelihood Ratios Next to 2 × 2 Tables From Figure 1 Allows Visualization of the Predictive Power of Composites of Biomarkers Representative of Pathophysiologic Types of Injury

Urinary neutrophil gelatinase-associated lipocalin (uNGAL) was used as a tubular damage marker, and plasma cystatin C (pCysC) was used as a functional damage marker. Prediction of transient acute kidney injury (denoted as a loss of function without damage) between the biomarker composites (bottom left of the far right table) should be compared to the post-operative percent of creatinine changes (ΔSCr; bottom right table). Data are for the entire cohort of 345 patients with 95% confidence intervals listed for the positive (+) and negative (−) likelihood ratios.

FIGURE 3. Decision Trees Created by CART Analysis Demonstrate Differences in Discrimination for Severe AKI by ΔSCr and Composites of uNGAL and pCysC

The terminal node of the supervised decision rules for uNGAL (ng/mg urinary creatinine) and pCysC (mg/l) shows 89% probability of severe AKI versus 43% for the unsupervised rule for ΔSCr (>29.2%). CART analysis also demonstrated a higher sensitivity, specificity, and AUC-ROC for the biomarker composite than the creatinine change. No AKI is defined as no injury or KDIGO stage 1; severe AKI is KDIGO stage 2 or within 7 days of surgery. AKI = acute kidney injury; AUC-ROC = areas under the receiver operating characteristic curve; CART = classification and regression tree; KDIGO = Kidney Disease Global Outcomes Initiative; other abbreviations as in Figure 2.

FIGURE 4. Decision Trees Created by CART Analysis Demonstrate Differences in Discrimination for Persistence of AKI by ΔSCr and Composites of uNGAL and pCysC

The terminal node of the supervised decision rules for uNGAL and pCysC shows 57% probability of severe AKI versus 29% for the unsupervised rule for ΔSCr (>45%). CART analysis also demonstrated a higher sensitivity, specificity, and AUC-ROC for the biomarker composite compared to the creatinine change. Abbreviations as in Figures 1 and 3.

CENTRAL ILLUSTRATION

Combining Biomarker Profiles Increases the Predictive Precision of Acute Kidney Injury Diagnosis