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Urine metabolites for preoperative prediction of acute kidney injury after coronary artery bypass graft surgery
Institution:1. National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China;2. Department of Cardiovascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China;3. Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China;4. Department of Cardiothoracic Surgery, Shenzhen Children''s Hospital, Shenzhen, China
Abstract:ObjectiveAcute kidney injury is a common complication after on-pump coronary artery bypass grafting. Prediction of acute kidney injury remains a challenge. Our study aims to identify a panel of urine metabolites for preoperative warning of acute kidney injury after on-pump coronary artery bypass grafting.MethodsA total of 159 patients undergoing isolated on-pump coronary artery bypass grafting were enrolled from July 7, 2017, to May 17, 2019. Preoperative urine samples were analyzed with the approach of liquid chromatography-mass spectrometry–based urine metabolomics. The study end point was the episode of acute kidney injury within 48 hours postoperatively. The predictive performance was determined by the area under the curve and calibration curve. The results were validated using bootstrap resampling.ResultsThe acute kidney injury (n = 55) and nonacute kidney injury (n = 104) groups showed significant different metabolic profiling. A total of 28 metabolites showed significant differences between the acute kidney injury and nonacute kidney injury groups. A metabolite panel of 5 metabolites (tyrosyl-gamma-glutamate, deoxycholic acid glycine conjugate, 5-acetylamino-6-amino-3-methyluracil, arginyl-arginine, and L-methionine) was discovered to have a good predicting performance (area under the curve, 0.89; 95% confidence interval, 0.82-0.93), which is higher than the clinical factor–based model (area under the curve, 0.63; 95% confidence interval, 0.53-0.72). Internal validation by bootstrap resampling showed an adjusted area under the curve of 0.88, and the calibration curve demonstrated good agreement between prediction and observation in the probability of postoperative acute kidney injury. Decision curve analysis revealed a superior net benefit of the metabolite model over the traditional clinical factor–based model.ConclusionsWe present 5 urine metabolites related to acute kidney injury after coronary artery bypass grafting. This metabolite model may serve as a preoperative warning of acute kidney injury after on-pump coronary artery bypass grafting.
Keywords:acute kidney injury  coronary artery bypass graft  urine metabolomics  AKI"}  {"#name":"keyword"  "$":{"id":"kwrd0030"}  "$$":[{"#name":"text"  "_":"acute kidney injury  AUC"}  {"#name":"keyword"  "$":{"id":"kwrd0040"}  "$$":[{"#name":"text"  "_":"area under the curve  CABG"}  {"#name":"keyword"  "$":{"id":"kwrd0050"}  "$$":[{"#name":"text"  "_":"coronary artery bypass grafting  CI"}  {"#name":"keyword"  "$":{"id":"kwrd0060"}  "$$":[{"#name":"text"  "_":"confidence interval  CPB"}  {"#name":"keyword"  "$":{"id":"kwrd0070"}  "$$":[{"#name":"text"  "_":"cardiopulmonary bypass  IGFBP7"}  {"#name":"keyword"  "$":{"id":"kwrd0080"}  "$$":[{"#name":"text"  "_":"insulin-like growth factor-binding protein 7  LC"}  {"#name":"keyword"  "$":{"id":"kwrd0090"}  "$$":[{"#name":"text"  "_":"liquid chromatography  MS"}  {"#name":"keyword"  "$":{"id":"kwrd0100"}  "$$":[{"#name":"text"  "_":"mass spectrometry  OPLS-DA"}  {"#name":"keyword"  "$":{"id":"kwrd0110"}  "$$":[{"#name":"text"  "_":"orthogonal partial least squares discriminant analysis  TIMP-2"}  {"#name":"keyword"  "$":{"id":"kwrd0120"}  "$$":[{"#name":"text"  "_":"tissue inhibitor of metalloproteinases-2
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