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Objective:To establish a predictive risk model for acute kidney injury (AKI) in acute myocardial infarction (AMI) patients based on machine learning algorithm and compare with a traditional logistic regression model.Methods:It was a retrospective study. The demographic data, laboratory examination, treatment regimen and medication of AMI patients from July 2011 to December 2016 in Beijing Anzhen Hospital, Capital Medical University were collected. The diagnostic criteria of AKI were based on the AKI diagnosis and treatment guidelines published by Kidney Diseases: Improving Global Outcomes in 2012. The selected AMI patients were randomly divided into training set (70%) and internal test set (30%) by simple random sampling. SelectFromModel and Lasso regression models were used to extract clinical parameters as predictors of AKI in AMI patients. Logistic regression model (model A) and machine learning algorithm (model B) were used to establish the risk prediction model of AKI in AMI patients. DeLong method was used to compare the area under the receiver-operating characteristic (ROC) curve ( AUC) between model A and model B for selecting the best model. Results:A total of 6 014 AMI patients were included in the study, with age of (58.4±11.7) years old and 3 414 males (80.5%). There were 674 patients (11.2%) with AKI. There were 4 252 patients (70.7%) in the training set and 1 762 patients (29.3%) in the test set. The selected twelve clinical parameters by the SelectFromModel and Lasso regression models included the number of myocardial infarctions, ST-segment elevation myocardial infarction, ventricular tachycardia, third degree atrioventricular block, decompensated heart failure at admission, admission serum creatinine, admission blood urea nitrogen, admission peak creatine kinase isoenzyme, diuretics, maximum daily dose of diuretics, days of diuretic use and statins. Logistic regression prediction model showed that AUC for the test set was 0.80 (95% CI 0.76-0.84). The machine learning algorithm model obtained AUC in the test set with 0.82 (95% CI 0.78-0.85).There was no significant difference in AUC between the two models ( Z=0.858, P=0.363), and AUC of the machine learning algorithm predictive model was slightly higher than that of the traditional logistic regression model. Conclusions:The prediction effect of AKI risk in AMI patients based on machine learning algorithm is similar to that of traditional logistic regression model, and the prediction accuracy of machine learning algorithm is better. The introduction of machine learning algorithm model may improve the ability to predict AKI risk.
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Objective To explore the association between circulating endothelial cells (CECs)and atherosclerosis in maintenance hemodialysis(MHD)patients. Methods A crosssectional study was performed to investigate the association between CECs and carotid atherosclerotic change in 65 MHD patients,25 non-hemodialysis patients with chronic kidney disease(CKD)of stage 4 or 5(CKD-non-HD)and 24 age-and Sex-matched healthy controls. CECs in peripheral blood were determined by multiparametrie flow cytometry(FCM).CECs were labeled with CD3-PerCP and CD146-PE before FCM and identified as CD3dim,CD146bright.Atherosclerosis in both groups Was assessed by the measurement of common carotid arery intimamedia thickness (CCA-IMT)and plaque of the common carotid arteries with ultrasound scanner. Results CECs were significantly higher in pre-dialysis patients[(151.52±98.24) cell/ml]and CKD-non-HD patients[(183.00±81.38)cell/ml ] compared with control group[(106.50± 24.14)cell/ml](P<0.05 and P<0.01,respectively).But the number of CECs was not significantly different between MHD and CKD-non-HD patients.CCA-IMT was also significantly higher in MHD patients[(0.94±0.36)mm]and CKD-non-HD patients [(1.02±0.37)mml compared with control group[(0.75±0.15)mm](P<0.05 and P<0.01,respectively).The number of pre-dialysis CECs was positively correlated with CCA-IMT in MHD patients(r=0.328,P<0.01).Multivariate analysis showed that CEC level was a strong independent risk factor of CCA-IMT. Conclusion InMHD patients, CEC level is associated with carotid atherosclerosis and may be used as a marker to evaluate the endothelial damage.