摘要目的:构建肾小球和肾小管损伤相关因素的贝叶斯网络模型。方法:本研究为横断面研究。2019年4至11月山西省人民医院对山西省10个县区开展慢性肾脏病机会性筛查项目，收集研究对象的一般资料和血、尿标本实验室检查结果。采用χ 2检验、logistic回归筛选肾小球、肾小管损伤的相关因素并构建基于最大最小爬山法（MMHC）的贝叶斯网络模型。 结果:共纳入研究对象12 269名，男5 198名，女7 071名，中位年龄58岁（40~91岁）。肾小球和肾小管损伤的患病率分别为12.7%（1 561/12 269）和11.6%（1 425/12 269）。肾小球损伤的贝叶斯网络由8个节点和10条有向边构成；肾小管损伤的贝叶斯网络由11个节点和17条有向边构成。贝叶斯网络显示，年龄、糖化血红蛋白为肾小球损伤的直接相关因素，性别、空腹血糖为肾小球损伤的间接相关因素；年龄、性别、空腹血糖、糖化血红蛋白为肾小管损伤的直接相关因素。肾小球损伤和肾小管损伤贝叶斯网络模型的受试者工作特征曲线下面积分别为0.761（95% CI：0.746~0.777）和0.753（95% CI：0.736~0.769）。 结论:贝叶斯网络能揭示肾小球损伤、肾小管损伤各相关因素之间的复杂网络关系，贝叶斯风险推理能为临床上预防肾小球和肾小管损伤提供参考价值。

abstractsObjective:To construct Bayesian network (BN) models to explore the factors related to glomerular injury (GI) and tubular injury (TI).Methods:A cross-sectional study was carried out. From April to November 2019, Shanxi Provincial People′s Hospital performed an opportunistic screening for chronic kidney disease in 10 counties of Shanxi Province. The general data and laboratory results of blood and urine samples were collected. Chi-square test and logistic regression were used to explore the related factors of GI and TI, which were included in the construction of BN models with max-min hill-climbing (MMHC) algorithm.Results:A total of 12 269 participants were included, there were 5 198 males and 7 071 females, with a median age of 58 (40-91) years. The prevalence of GI and TI was 12.7% (1 561/12 269) and 11.6% (1 425/12 269), respectively. The BN model consisted of 8 nodes and 10 edges for GI, and 11 nodes and 17 edges for TI, respectively. BN models showed that age and glycated hemoglobin were direct related factors for GI, while gender and fasting blood glucose were indirect related factors for GI. Age, gender, fasting blood glucose and glycosylated hemoglobin were direct related factors for TI. Additionally, the area under the receiver operating characteristic curve (AUC) was 0.761 (95% CI: 0.746-0.777) and 0.753 (95% CI: 0.736-0.769) for GI and TI BN models, respectively. Conclusions:BN models allow for identifying the complex network relationships among the factors related to GI and TI. Meanwhile, Bayesian risk reasoning can provide reference value for the clinical prevention of GI and TI.