International urology and nephrology · 2019
Prediction model for acute kidney injury after coronary artery bypass grafting: a retrospective study.
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Yue Zhou, Yan-Meng Guan, Ji-Zhuang Lou
Аннотация
BACKGROUND: Acute kidney injury (AKI) after coronary artery bypass grafting (CABG) is associated with a less favorable outcome. The aim of this study is to investigate the incidence, mortality and risk factors of AKI after CABG, and to establish a risk prediction model.
METHODS: From January 2016 to June 2018, 541 patients who underwent CABG were enrolled. The clinical characteristics were collected to calculate the incidence and mortality of AKI after CABG. Patients were divided into AKI group and non-AKI group according to the statistical data. The differences of preoperative, intraoperative and postoperative variables between the two groups were comparatively analysed. The risk factors of AKI were obtained by binary logistic stepwise regression analyses using related factors as independent variables.
RESULTS: The incidence of postoperative AKI in 541 patients was 27.9% (151 cases). The in-hospital mortality in AKI group was higher than that in non-AKI group (5.30% vs 0.00%, P < 0.001). Single factor analysis showed that the risk factors for postoperative AKI including age, BMI, hypertension, cardiac insufficiency, eGFR, serum uric acid level, CABG combined valve operation, cardiopulmonary bypass (CPB), operation time, aortic cross-clamping time, CPB time, mechanical ventilation time and postoperative low cardiac output syndrome. Multivariate regression analysis suggested that age (P = 0.006, OR 2.323), BMI (P = 0.004, OR 2.495), hypertension (P = 0.032, OR 1.712), eGFR (P = 0.002, OR 3.054), CPB time (P = 0.024, OR 1.007) and postoperative low cardiac output syndrome (P = 0.010, OR 2.640) were independent risk factors for AKI.
CONCLUSIONS: AKI is a common complication after CABG and is related to multiple perioperative factors. It is suggested that early recognition of these risk factors and interventions should be carried out in clinical practice. The risk prediction model can be used as a simple tool for predicting postoperative AKI.
METHODS: From January 2016 to June 2018, 541 patients who underwent CABG were enrolled. The clinical characteristics were collected to calculate the incidence and mortality of AKI after CABG. Patients were divided into AKI group and non-AKI group according to the statistical data. The differences of preoperative, intraoperative and postoperative variables between the two groups were comparatively analysed. The risk factors of AKI were obtained by binary logistic stepwise regression analyses using related factors as independent variables.
RESULTS: The incidence of postoperative AKI in 541 patients was 27.9% (151 cases). The in-hospital mortality in AKI group was higher than that in non-AKI group (5.30% vs 0.00%, P < 0.001). Single factor analysis showed that the risk factors for postoperative AKI including age, BMI, hypertension, cardiac insufficiency, eGFR, serum uric acid level, CABG combined valve operation, cardiopulmonary bypass (CPB), operation time, aortic cross-clamping time, CPB time, mechanical ventilation time and postoperative low cardiac output syndrome. Multivariate regression analysis suggested that age (P = 0.006, OR 2.323), BMI (P = 0.004, OR 2.495), hypertension (P = 0.032, OR 1.712), eGFR (P = 0.002, OR 3.054), CPB time (P = 0.024, OR 1.007) and postoperative low cardiac output syndrome (P = 0.010, OR 2.640) were independent risk factors for AKI.
CONCLUSIONS: AKI is a common complication after CABG and is related to multiple perioperative factors. It is suggested that early recognition of these risk factors and interventions should be carried out in clinical practice. The risk prediction model can be used as a simple tool for predicting postoperative AKI.