Background:Cardiac surgery-associated acute kidney injury(CSA-AKI)is a major complication that increases morbidity and mortality after cardiac surgery.Most established predictive models are limited to the analysis of ...Background:Cardiac surgery-associated acute kidney injury(CSA-AKI)is a major complication that increases morbidity and mortality after cardiac surgery.Most established predictive models are limited to the analysis of nonlinear relationships and do not adequately consider intraoperative variables and early postoperative variables.Nonextracorporeal circulation coronary artery bypass grafting(off-pump CABG)remains the procedure of choice for most coronary surgeries,and refined CSA-AKI predictive models for off-pump CABG are notably lacking.Therefore,this study used an artificial intelligence-based machine learning approach to predict CSA-AKI from comprehensive perioperative data.Methods:In total,293 variables were analysed in the clinical data of patients undergoing off-pump CABG in the Department of Cardiac Surgery at the First Affiliated Hospital of Guangxi Medical University between 2012 and 2021.According to the KDIGO criteria,postoperative AKI was defined by an elevation of at least 50%within 7 days,or 0.3 mg/dL within 48 hours,with respect to the reference serum creatinine level.Five machine learning algorithms—a simple decision tree,random forest,support vector machine,extreme gradient boosting and gradient boosting decision tree(GBDT)—were used to construct the CSA-AKI predictive model.The performance of these models was evaluated with the area under the receiver operating characteristic curve(AUC).Shapley additive explanation(SHAP)values were used to explain the predictive model.Results:The three most influential features in the importance matrix plot were 1-day postoperative serum potassium concentration,1-day postoperative serum magnesium ion concentration,and 1-day postoperative serum creatine phos-phokinase concentration.Conclusion:GBDT exhibited the largest AUC(0.87)and can be used to predict the risk of AKI development after surgery,thus enabling clinicians to optimise treatment strategies and minimise postoperative complications.展开更多
Background Acute Kidney Injury （AKI） is a common and serious complication of cardiovascular surgery. There is a need to find biomarkers that are involved in the etiology of cardiac surgery-associated acute kidney in...Background Acute Kidney Injury （AKI） is a common and serious complication of cardiovascular surgery. There is a need to find biomarkers that are involved in the etiology of cardiac surgery-associated acute kidney injury （CSA-AKI） and have an earlier response to acute kidney injury. The association between urine neutrophil gelatinase-associated lipocalin （NGAL） concentrations and AKI progression is not well established. Methods The prospective-cohort study included 1631 consecutive adult patients undergoing cardiac surgery at Fuwai Hospital between September 2012 and November 2013. AKI defined by Acute Kidney Injury Network （AKIN） criteria with a postoperative increase in plasma creatinine 〉/50% baseline or/〉0.3 mg/dL. Urine NGAL was measured us- ing latex particle-enhanced turbidimetric immunoassay. Associations between Urine NGAL levels and AKI were determined by estimating areas under receiver operating characteristic curves （AUC）. Results A total of 438 （26.9%） patients developed CSA-AKI. And the patients were divided into four groups： 1193 non-AKI patients, 368（22.6%） patients with AKIN stage I AKI, 49（3.0%） with AKIN stage 2 AKI and 21（1.3%） with AKIN stage 3 AKI. urine NGAL concentrations at surgical intensive care unit （SICU） admission were significantly related to AKI severity. The AUCs for urine NGAL were for AKIN stage 1 （0.54±0.02）, AKIN stage 2 （0.67±0.04）, and AKIN stage 3 （0.76±0.06）, respectively. Conclusions Urinary NGAL is associated with CSA-AKI and its progression, indicating their potential use as prognostic markers. Urine NGAL level measured at SICU admission predicts the development of severe AKI after cardiac surgery.展开更多
基金supported by Natural Science Foundation of China(No.82060082).
文摘Background:Cardiac surgery-associated acute kidney injury(CSA-AKI)is a major complication that increases morbidity and mortality after cardiac surgery.Most established predictive models are limited to the analysis of nonlinear relationships and do not adequately consider intraoperative variables and early postoperative variables.Nonextracorporeal circulation coronary artery bypass grafting(off-pump CABG)remains the procedure of choice for most coronary surgeries,and refined CSA-AKI predictive models for off-pump CABG are notably lacking.Therefore,this study used an artificial intelligence-based machine learning approach to predict CSA-AKI from comprehensive perioperative data.Methods:In total,293 variables were analysed in the clinical data of patients undergoing off-pump CABG in the Department of Cardiac Surgery at the First Affiliated Hospital of Guangxi Medical University between 2012 and 2021.According to the KDIGO criteria,postoperative AKI was defined by an elevation of at least 50%within 7 days,or 0.3 mg/dL within 48 hours,with respect to the reference serum creatinine level.Five machine learning algorithms—a simple decision tree,random forest,support vector machine,extreme gradient boosting and gradient boosting decision tree(GBDT)—were used to construct the CSA-AKI predictive model.The performance of these models was evaluated with the area under the receiver operating characteristic curve(AUC).Shapley additive explanation(SHAP)values were used to explain the predictive model.Results:The three most influential features in the importance matrix plot were 1-day postoperative serum potassium concentration,1-day postoperative serum magnesium ion concentration,and 1-day postoperative serum creatine phos-phokinase concentration.Conclusion:GBDT exhibited the largest AUC(0.87)and can be used to predict the risk of AKI development after surgery,thus enabling clinicians to optimise treatment strategies and minimise postoperative complications.
文摘Background Acute Kidney Injury （AKI） is a common and serious complication of cardiovascular surgery. There is a need to find biomarkers that are involved in the etiology of cardiac surgery-associated acute kidney injury （CSA-AKI） and have an earlier response to acute kidney injury. The association between urine neutrophil gelatinase-associated lipocalin （NGAL） concentrations and AKI progression is not well established. Methods The prospective-cohort study included 1631 consecutive adult patients undergoing cardiac surgery at Fuwai Hospital between September 2012 and November 2013. AKI defined by Acute Kidney Injury Network （AKIN） criteria with a postoperative increase in plasma creatinine 〉/50% baseline or/〉0.3 mg/dL. Urine NGAL was measured us- ing latex particle-enhanced turbidimetric immunoassay. Associations between Urine NGAL levels and AKI were determined by estimating areas under receiver operating characteristic curves （AUC）. Results A total of 438 （26.9%） patients developed CSA-AKI. And the patients were divided into four groups： 1193 non-AKI patients, 368（22.6%） patients with AKIN stage I AKI, 49（3.0%） with AKIN stage 2 AKI and 21（1.3%） with AKIN stage 3 AKI. urine NGAL concentrations at surgical intensive care unit （SICU） admission were significantly related to AKI severity. The AUCs for urine NGAL were for AKIN stage 1 （0.54±0.02）, AKIN stage 2 （0.67±0.04）, and AKIN stage 3 （0.76±0.06）, respectively. Conclusions Urinary NGAL is associated with CSA-AKI and its progression, indicating their potential use as prognostic markers. Urine NGAL level measured at SICU admission predicts the development of severe AKI after cardiac surgery.
