慢性肾脏病并发肾衰竭的风险因素分析与列线图模型构建  

Analysis of risk factors for chronic kidney disease complicated by renal failure and constructing a nomogram model

作　　者：布合力其·麦麦提[1] 冯琦[1] 刘红艳[1] 徐超 Buheliqi·Maimaiti;Feng Qi;Liu Hong-yan;Xu Chao(Department of Nephrology,Fifth Affiliated Hospital,Xinjiang Medical University,Urumqi 830011,China)

机构地区：[1]新疆医科大学第五附属医院肾病科,乌鲁木齐830011

出　　处：《临床肾脏病杂志》2023年第6期467-473,共7页Journal Of Clinical Nephrology

基　　金：新疆维吾尔自治区自然科学基金(2021D01C434)。

摘　　要：目的分析慢性肾脏病患者中肾衰竭发生的相关风险因素,依次构建预测慢性肾脏病患者合并肾衰竭的列线图模型。方法我们使用日本慢性肾脏病治疗和流行病学结局研究的数据进行预测模型的开发和内部验证,而外部验证集使用了自2013年1月至2018年12月在新疆医科大学第五附属医院收集的患者样本数据。我们使用Cox比例风险回归模型并在R软件中开发了列线图模型。最后,采用R软件对模型判别、标定和临床价值进行了测试。结果开发和内部验证数据集分别包括797例患者[191例(23.96%)进展患者]和341例患者[89例(26.10%)进展患者],而外部验证数据集中包括297例患者[108例(36.36%)进展患者]。将列线图模型与年龄、估算肾小球滤过率、血红蛋白、血白蛋白和试纸蛋白尿一起开发,以预测3年无不良结局的概率。该列线图的C统计量(一致性统计量)在开发数据集为0.95(95%CI:0.89~0.92),内部验证数据集为0.91(95%CI:0.89~0.94),外部验证数据集为0.83(95%CI:0.78~0.88)。该模型的校准和决策曲线分析良好。结论这种可视化的预测列线图模型可以准确预测慢性肾脏病患者的慢性肾脏病3年肾功能衰竭结果,为临床从业者提供易于使用且广泛适用的工具。Objective To explore the risk factors associated with the occurrence of renal failure and column line graph model for predicting concurrent renal failure in patients with chronic kidney disease(CKD).Methods The raw data were collected from the Japanese CKD-ROUTE study for prediction model development and internal validation while the external validation set obtained data from a sampling of patients from January 2013 to December 2018.Cox proportional risk regression was employed for column line graph modeling in R software.Finally model discrimination,calibration and clinical value were tested with R software.Results The development and internal validation datasets included 797 patients[191 progressive cases(23.96%)]and 341 patients[89 progressive cases(26.10%)]while the external validation dataset included 297 patients[108 progressive cases(36.36%)].Columnar line graph model were developed with age,estimated glomerular filtration rate(eGFR),hemoglobin,blood albumin and test paper proteinuria for predicting the 3-year probability of no adverse outcome.C statistic for column line plot was 0.90(95%CI:0.89-0.92)for development dataset,0.91(95%CI:0.89-0.94)for internal validation dataset and 0.83(95%CI:0.78-0.88).The model was well-calibrated and examined for decision curves.Conclusion This visual predictive line graph model may predict accurately 3-year kidney failure out-comes in CKD patients.Thus clinical practitioners have a convenient and widely applicable tool.

关 键 词：慢性肾脏病 肾功能衰竭 危险因素 列线图预测模型 

分 类 号：R692[医药卫生—泌尿科学]