A Prediction Model for the Risk of Incident Chronic Kidney Disease
A Prediction Model for the Risk of Incident Chronic Kidney Disease
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DOI：
10.1016/j.amjmed.2010.05.010
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发表时间：
2010-09-01
影响因子：
5.9
通讯作者：
Chen, Ming-Fong
中科院分区：
文献类型：
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作者：
Chien, Kuo-Liong;Lin, Hung-Ju;Chen, Ming-Fong
背景：慢性肾脏疾病是普通人群的健康负担。我们设计了一项队列研究，以构建中国人群慢性肾脏病的预测模型。方法：共随访了5168名参与者，中位数为2.2（四分位距，1.5-2.9）岁，190人（3.7%）发展为慢性肾脏疾病，定义为肾小球滤过率低于60 mL/min/1.73 m（2）。我们开发了一个点系统，使用以下变量估计4年时的慢性肾脏疾病风险：年龄（8分）、体重指数（2分），舒张压（2分），2型糖尿病史（1分）和中风史（4分）为临床模型，加尿酸对于生化模型，血糖（2分）、餐后血糖（1分）、血红蛋白A1 c（1分）和蛋白尿100 mg/dL或更高（6分）。在临床模型（受试者工作特征曲线下面积，0.768; 95%置信区间（CI），0.738-0.798）和生化模型（受试者工作特征曲线下面积，0.765; 95% CI，0.734-0.796）之间发现了相似的区分措施。对于来自社区队列参与者的外部验证数据，临床模型的受试者工作特征曲线下面积为0.667（95%CI，0.631-0.703）。临床模型的最佳截断值为7，敏感性为0.76，特异性为0.66。结论：建立了一个基于临床点的慢性肾脏病4年发病率预测模型。这种预测工具可能有助于针对有慢性肾脏疾病风险的中国受试者。(C)2010年由Elsevier Inc.出版美国医学杂志（2010）123，836-846
BACKGROUND: Chronic kidney disease is a health burden for the general population. We designed a cohort study to construct prediction models for chronic kidney disease in the Chinese population.METHODS: A total of 5168 participants were followed up during a median of 2.2 (interquartile range, 1.5-2.9) years, and 190 individuals (3.7%) developed chronic kidney disease, defined by a glomerular filtration rate of less than 60 mL/min/1.73 m(2).RESULTS: We developed a point system to estimate chronic kidney disease risk at 4 years using the following variables: age (8 points), body mass index (2 points), diastolic blood pressure (2 points), and history of type 2 diabetes (1 point) and stroke (4 points) for the clinical model, with the addition of uric acid (2 points), postprandial glucose (1 point), hemoglobin A1c (1 point), and proteinuria 100 mg/dL or greater (6 points) for the biochemical model. Similar discrimination measures were found between the clinical model (area under the receiver operating characteristic curve, 0.768; 95% confidence interval (CI), 0.738-0.798) and the biochemical model (area under the receiver operating characteristic curve, 0.765; 95% CI, 0.734-0.796). The area under the receiver operating characteristic curve of the clinical model was 0.667 (95% CI, 0.631-0.703) for the external validation data from community-based cohort participants. The optimal cutoff value for the clinical model was set as 7, with a sensitivity of 0.76 and a specificity of 0.66.CONCLUSION: We constructed a clinical point-based model to predict the 4-year incidence of chronic kidney disease. This prediction tool may help to target Chinese subjects at risk of developing chronic kidney disease. (C) 2010 Published by Elsevier Inc. The American Journal of Medicine (2010) 123, 836-846