Song W Z, Qiu L X, Wang X C, Li Y H, Hu F Y, Li Y F, Li R S, Zhou X S
Department of Nephrology, the Fifth Hospital of Shanxi Medical University (Shanxi Provincial People's Hospital), Taiyuan 030012, China.
School of Public Health, Shanxi Medical University, Taiyuan 030001, China.
Zhonghua Yi Xue Za Zhi. 2023 May 16;103(18):1401-1409. doi: 10.3760/cma.j.cn112137-20221101-02279.
To construct Bayesian network (BN) models to explore the factors related to glomerular injury (GI) and tubular injury (TI). 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. 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%: 0.746-0.777) and 0.753 (95%: 0.736-0.769) for GI and TI BN models, respectively. 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.
构建贝叶斯网络（BN）模型以探究与肾小球损伤（GI）和肾小管损伤（TI）相关的因素。开展了一项横断面研究。2019年4月至11月，山西省人民医院在山西省10个县进行了慢性肾脏病的机会性筛查。收集了一般资料以及血液和尿液样本的实验室检查结果。采用卡方检验和逻辑回归探究GI和TI的相关因素，并将其纳入采用最大-最小爬山法（MMHC）算法构建的BN模型中。共纳入12269名参与者，其中男性5198名，女性7071名，中位年龄为58（40 - 91）岁。GI和TI的患病率分别为12.7%（1561/12269）和11.6%（1425/12269）。BN模型中，GI模型由8个节点和10条边组成，TI模型由11个节点和17条边组成。BN模型显示，年龄和糖化血红蛋白是GI的直接相关因素，而性别和空腹血糖是GI的间接相关因素。年龄、性别、空腹血糖和糖化血红蛋白是TI的直接相关因素。此外，GI和TI的BN模型的受试者工作特征曲线下面积（AUC）分别为0.761（95%：0.746 - 0.777）和0.753（95%：0.736 - 0.769）。BN模型有助于识别与GI和TI相关因素之间的复杂网络关系。同时，贝叶斯风险推理可为GI和TI的临床预防提供参考价值。
