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Combining Novel Renal Injury Markers with Delta Serum Creatinine Early after Cardiac Surgery and Risk-Stratification for Serious Adverse Outcomes: An Exploratory Analysis.

David R. McIlroy,David R. McIlroy,David Farkas,Kun Pan,J. Pickering,H. Lee

Published 2017 in Journal of Cardiothoracic and Vascular Anesthesia

ABSTRACT

OBJECTIVE To evaluate the prognostic utility of multiple novel urinary biomarkers of renal injury when used alone, in pair-wise combination with an early delta serum creatinine (ΔSCr) term, and combined as a broad biomarker panel for the prediction of serious adverse outcomes that may reflect AKI in patients undergoing cardiac surgery. DESIGN Post-hoc analysis of prospective observational study. SETTING Academic medical center. PARTICIPANTS 603 adults undergoing cardiac surgery. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Urinary cystatin-c, kidney injury molecule-1, chemokine (C-C motif) ligand 2 and interleukin-18 were measured at baseline and <1 hour, 3 hours and 18-24 hours after separation from cardiopulmonary bypass (CPB). ΔSCr-initial was defined as the difference in SCr from baseline to first postoperative measure. The primary outcome of hospital mortality or renal replacement therapy occurred in 25 patients. Concordant elevation of any urinary biomarker measured 3 hours after CPB together with ΔSCr-initial ≥0 mg.dL-1 provided excellent early risk stratification for the primary outcome (OR ≥15.1, 95% CI 4.1-55.4). Combining four urinary biomarkers together with ΔSCr-initial and neutrophil gelatinase-associated lipocalin, previously reported from the same cohort, to provide a 6-point AKI risk score enabled early identification of patients reaching the primary outcome (ROCAUC 0.86, 95% CI 0.79-0.92) with potentially useful sensitivity and specificity at varied cut-points. CONCLUSIONS Combining novel urinary biomarkers of renal injury with a creatinine-based metric soon after cardiac surgery provided excellent prognostic utility for serious adverse outcomes. Future studies are required to confirm these findings and determine optimal biomarker combinations for cost-effective risk stratification.

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