Urinary Biomarkers may Complement the Cleveland Score for Prediction of Adverse Kidney Events After Cardiac Surgery: A Pilot Study

Abstract

Background: The ability of urinary biomarkers to complement established clinical risk prediction models for postoperative adverse kidney events is unclear. We assessed the effect of urinary biomarkers linked to suspected pathogenesis of cardiac surgery-induced acute kidney injury (AKI) on the performance of the Cleveland Score, a risk assessment model for postoperative adverse kidney events.

Methods: This pilot study included 100 patients who underwent open-heart surgery. We determined improvements to the Cleveland Score when adding urinary biomarkers measured using clinical laboratory platforms (neutrophil gelatinase-associated lipocalin [NGAL], interleukin-6) and those in the preclinical stage (hepcidin-25, midkine, alpha-1 microglobulin), all sampled immediately post-surgery. The primary endpoint was major adverse kidney events (MAKE), and the secondary endpoint was AKI. We performed ROC curve analysis, assessed baseline model performance (odds ratios [OR], 95% CI), and carried out statistical reclassification analyses to assess model improvement.

Results: NGAL (OR [95% CI] per 20 concentration-units wherever applicable): (1.07 [1.01-1.14]), Interleukin-6 (1.51 [1.01-2.26]), midkine (1.01 [1.00-1.02]), 1-hepcidin-25 (1.08 [1.00-1.17]), and NGAL/hepcidin-ratio (2.91 [1.30-6.49]) were independent predictors of MAKE and AKI (1.38 [1.03-1.85], 1.08 [1.01-1.15], 1.01 [1.00-1.02], 1.09 [1.01-1.18], and 3.45 [1.54-7.72]). Category-free net reclassification improvement identified interleukin-6 as a model-improving biomarker for MAKE and NGAL for AKI. However, only NGAL/hepcidin-25 improved model performance for event- and event-free patients for MAKE and AKI.

Conclusions: NGAL and interleukin-6 measured immediately post cardiac surgery may complement the Cleveland Score. The combination of biomarkers with hepcidin-25 may further improve diagnostic discrimination.

Keywords: Acute kidney injury; Cardiac surgery; Cleveland Score; Hepcidin; Interleukin-6; Major adverse kidney events; Midkine; Neutrophil gelatinase-associated lipocalin; Reclassification analysis.

Fig. 1. Patient flow diagram.

Fig. 2. Ranking of assessed urinary biomarker performance according to the univariate AUC (with 95% confidence interval bars) at ICU admission for predicting (A) MAKE and (B) AKI.

Abbreviations: NGAL, neutrophil gelatinase-associated lipocalin; ICU, intensive care unit; AUC, area under the ROC curve; MAKE, major adverse kidney events; AKI, acute kidney injury.

Fig. 3. Ranking of kidney risk prediction model performance to predict (A) MAKE and (B) AKI according to the area under the ROC curve (AUC with 95% confidence interval [CI] bars) with added urinary kidney injury biomarker at ICU admission (new model) and without (reference model [3], 95% CI highlighted orange).

Abbreviations: MAKE, major adverse kidney events; AKI, acute kidney injury; NGAL, neutrophil gelatinase-associated lipocalin; AUC, area under the ROC curve.

Fig. 4. Risk assessment plots showing the changes in model performance. Compared with AUC graphs, risk assessment plots illustrate information for events and non-events separately, representing the preferences and drawbacks of the reclassified risk models (●_nonevents, ■_events, solid lines) calculated by the addition of urinary biomarker concentrations (NGAL, interleukin-6, NGAL/hepcidin-25, interleukin-6/hepcidin-25) to the reference model (○_nonevents, □_events, dashed lines). □■ represent model sensitivity (Y-axis) versus the calculated risk (X-axis) for those with the event. ○● represent 1-specificity (Y-axis) versus the calculated risk (X-axis) for those without an event (endpoints MAKE, AKI).

Abbreviations: AUC, area under the ROC curve; MAKE, major adverse kidney events; AKI, acute kidney injury; NGAL, neutrophil gelatinase-associated lipocalin; IL-6, interleukin-6.