某些由政府机构和专业组织制定的指南以及作者以其他语言发表的科研成果等，需要让更多的读者了解。根据国际惯例（参考《向生物医学期刊投稿的统一要求》）和我国的实际情况，凡符合下列条件并提供相应材料，中华医学会系列杂志允许或接受论文用同一种语言或另一种语言的二次发表。
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.
为维护中华医学会系列杂志的声誉和广大读者的利益,现将中华医学会杂志对一稿两投和一稿两用问题的处理声明如下:(1)本声明中所涉及的文稿均指原始研究的报告或尽管 2 篇文稿在文字的表达和讨论的叙述上可能存在某些不同之处,但这些文稿的主要数据和图表是相同的.
Peritoneal ultrafiltration failure is a common reason for peritoneal dialysis (PD) withdrawal as well as mortality in PD patients. Based on the three-pore system, inter-cellular small pores and trans-cellular ultra-small pores (aquaporin-1) are mainly responsible for water transfer across the peritoneum. Both small and ultra-small pores-dependent water (free water) transport decline accompanied with time on PD, with more significant decrease in free water, resulting in peritoneal ultrafiltration failure. The reduction of free water transport is associated with fast peritoneal solute transfer, reduced crystalloid osmotic gradient due to increased interstitial glucose absorption, and declined osmotic conductance to glucose resulted from impaired aquaporin-1 function and peritoneal interstitial fibrosis. The decline of small pore-based water is mainly because of fast loss of crystalloid osmotic gradient, decrease of hydrostatic pressure mediated by peritoneal vasculopathy, as well as reduced absolute number of small pores. The current review discusses the advance on pathogenesis of acquired peritoneal ultrafiltration failure in long-term PD.
The population of chronic kidney disease (CKD) with hypertension in China is characterized by complex etiology, high incidence rate, low awareness and control rate. How to diagnose and treat hypertension in CKD patients properly and improve their prognosis is particularly urgent. Several clinical guidelines or expert consensus on the diagnosis, treatment and management of hypertension have been issued. Some of them involve the diagnosis and treatment of hypertension in CKD patients, but they still can not meet the demand for diagnosis and treatment of hypertension in CKD patients. Based on the situation of hypertension in CKD patients in China, the Chinese Society of Nephrology organized an expert group to formulate this guideline. This guideline systematically introduces the diagnostic criteria, epidemiology, risk factors, poor prognosis of hypertension, the purpose, timing and control goals of antihypertensive therapy in CKD patients, as well as blood pressure control goals for special populations, non drug treatment and drug treatment of hypertension. This guideline aims to further strengthen the management of hypertension in CKD patients, standardize the diagnosis and treatment standards, formulate reasonable treatment plans, effectively control hypertension, reduce complications, so as to delay the progress of kidney diseases and improve the long-term prognosis of hypertension in Chinese CKD patients.
