术前预后营养指数对584例心脏手术后患者急性肾损伤的预测价值

目的 评估术前预后营养指数(prognostic nutritional index,PNI)对择期体外循环(cardiopulmonary bypass,CPB)下非冠状动脉旁路移植(coronary artery bypass grafting,CABG)心脏手术后急性肾损伤(acute kidney injury...

The predictive value of preoperative prognostic nutritional index for postoperativeacute kidney injury in 584 patients undergoing cardiac surgery

Objective To determine the predictive value of the preoperative prognostic nutritional index(PNI)regarding the development of acute kidney injury(AKI)after non-coronary artery bypass grafting(CABG)cardiac surgery.Methods The clinical data of 584 patients who underwent elective non-CABG cardiac surgery with cardiopulmonary bypass(CPB)in our hospital from May to September 2019 were reviewed.There were 268(45.9%)males and 316(54.1%)females,with a mean age of 52.1±11.6 years.The mean cardiopulmonary time and aortic-clamp time was 124.8±50.1 min and 86.4±38.9 min,respectively.Totally 449(76.9%)patients received isolate valve surgery.We developed the risk prediction model of AKI using multivariable logistic regression.The predictive values of preoperative PNI,Cleveland Clinic Score(CCS)and risk prediction model were estimated by the area under the receiver operating characteristic curve(AUC)and Hosmer-Lemeshow goodness-of-fit test.The improvement of preoperative PNI to predictive values of CCS or AKI risk prediction models were defined by the net reclassification index(NRI)and variation of AUC.Results The preoperative PNI could neither effectively predict the occurrence of AKI following non-CABG cardiac surgery(AUC=0.553,95%CI 0.489-0.617,P=0.095)nor improve the predictive effect of other AKI predictive models.The risk prediction model of AKI structured by our study had high predictive value on AKI or severe AKI(stage 2-3)(AUC=0.741,95%CI 0.686-0.796,P<0.001)and superior to CCS(AUC=0.512,95%CI 0.449-0.576,P=0.703).Conclusion The preoperative PNI can neither predict the occurrence of AKI following elective non-CABG cardiac surgery nor improve the prediction values of other AKI prediction models.