Plasma Metabolites–Based Prediction in Cardiac Surgery–Associated Acute Kidney Injury

Abstract

Background Cardiac surgery–associated acute kidney injury (CSA‐AKI) is a common postoperative complication following cardiac surgery. Currently, there are no reliable methods for the early prediction of CSA‐AKI in hospitalized patients. This study developed and evaluated the diagnostic use of metabolomics‐based biomarkers in patients with CSA‐AKI. Methods and Results A total of 214 individuals (122 patients with acute kidney injury [AKI], 92 patients without AKI as controls) were enrolled in this study. Plasma samples were analyzed by liquid chromatography tandem mass spectrometry using untargeted and targeted metabolomic approaches. Time‐dependent effects of selected metabolites were investigated in an AKI swine model. Multiple machine learning algorithms were used to identify plasma metabolites positively associated with CSA‐AKI. Metabolomic analyses from plasma samples taken within 24 hours following cardiac surgery were useful for distinguishing patients with AKI from controls without AKI. Gluconic acid, fumaric acid, and pseudouridine were significantly upregulated in patients with AKI. A random forest model constructed with selected clinical parameters and metabolites exhibited excellent discriminative ability (area under curve, 0.939; 95% CI, 0.879–0.998). In the AKI swine model, plasma levels of the 3 discriminating metabolites increased in a time‐dependent manner (R2, 0.480–0.945). Use of this AKI predictive model was then confirmed in the validation cohort (area under curve, 0.972; 95% CI, 0.947–0.996). The predictive model remained robust when tested in a subset of patients with early‐stage AKI in the validation cohort (area under curve, 0.943; 95% CI, 0.883–1.000). Conclusions High‐resolution metabolomics is sufficiently powerful for developing novel biomarkers. Plasma levels of 3 metabolites were useful for the early identification of CSA‐AKI.

Keywords

• acute kidney injury
• biomarkers
• cardiac surgery
• machine learning
• metabolomics