4.6 Article

Risk factors for acute kidney injury after surgery of the thoracic aorta using antegrade selective cerebral perfusion and moderate hypothermia

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DOI: 10.1016/j.jtcvs.2015.04.008

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acute kidney injury; thoracic aorta surgery; cerebral protection; logistic regression analysis; ROC analysis

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Background: The development of acute kidney injury (AKI) in cardiac surgery is associated with increased morbidity and mortality. The aim of the study was to assess the incidence and risk factors for AKI after thoracic aorta surgery, using antegrade selective cerebral perfusion (ASCP) and moderate hypothermia. Methods: We reviewed 641 patients undergoing thoracic aortic surgery, using ASCP and moderate hypothermia, from November 1996 to December 2012. Patient preoperative, intraoperative, and postoperative variables were evaluated for association with AKI with logistic regression analysis. Models including all variables and models, after the sequential removal of postoperative, and both postoperative and intraoperative variables, were assessed using receiver operating characteristic analysis. Results: The mean age of the patients was 62.9 years, and 194 patients (30%) were women. The overall incidence of AKI was 19.0%. In-hospital mortality was significantly higher in the AKI group (33.6% vs 6.7%; P <. 001). Logistic regression analysis identified 8 predictors of AKI: 4 of them were preoperative (priority, diabetes, preoperative glomerular filtration rate, and weight); 2 intraoperative (mitral valve and aortic valve replacement); and 2 postoperative (overall neurologic complication and reoperation for bleeding). Model-discrimination performance improved from an area under the curve (AUC) of 0.737, for the model including only preoperative variables, to an AUC of 0.798 for the model including all variables (P = .012). Conclusions: The incidence of AKI after thoracic aorta surgery is fairly common, and its occurrence strongly affects outcomes. Preoperative renal status and preoperative conditions are the main influences on AKI development. Predictive models can be improved by adding intraoperative and postoperative variables.

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