基于CD161构建肺结核的临床诊断模型  

Constructing a clinical diagnostic model for pulmonary tuberculosis based on CD161

作　　者：张莹 张志苏 施自伦 赵沨 邢应如 Zhang Ying;Zhang Zhisu;Shi Zilun;Zhao Feng;Xing Yingru(School of Medicine,Anhui University of Science and Technology,Huainan 232001;Dept of Clinical Laboratory,Affiliated Cancer Hospital of Anhui University of Science and Technology,Huainan 232035;Dept of Clinical Laboratory,Oriental Hospital Group Hospital of Anhui University of Science and Technology,Huainan 232001;Dept of Thyroid and Breast Surgery,The First Affiliated Hospital of Anhui University of Science and Technology,Huainan 232000;Dept of Blood Transfusion,Shanghai Pudong New Area People′s Hospital,Shanghai 201299;Key Laboratory of Industrial Dust Deep Reduction and Occupational Health and Safety of Anhui Higher Education Institutes,Anhui University of Science and Technology,Huainan 232001)

机构地区：[1]安徽理工大学医学院,淮南232001 [2]安徽理工大学附属肿瘤医院检验科,淮南232035 [3]安徽理工大学附属东方医院集团总医院检验科,淮南232001 [4]安徽理工大学第一附属医院甲乳外科,淮南232000 [5]上海市浦东新区人民医院输血科,上海201299 [6]安徽理工大学工业粉尘深度净化与职业健康安全安徽省普通高校重点实验室,淮南232001

出　　处：《安徽医科大学学报》2025年第3期515-523,共9页Acta Universitatis Medicinalis Anhui

基　　金：安徽省卫生健康科研项目(省财政支持项目)(编号:AHWJ2023A10015);安徽省高校自然科学研究重大项目(编号:2023AH040405);工业粉尘深度净化与职业健康安全安徽省普通高校重点实验室项目(编号:AYZJSGXLK202202006);淮南市科技基金项目(编号:2021A254)。

摘　　要：目的构建临床诊断模型区分肺结核与非结核性肺病。方法收集258例呼吸系统疾病的患者信息,通过R软件采用随机数种子的方式分为训练集152例,测试集106例,比例为6∶4,根据患者诊断是否为肺结核将训练集分为结核组95例和非结核组57例,测试集分为结核组65例和非结核组41例。根据多因素Logistic回归分析确定肺结核影响因素并构建诊断模型,采用受试者工作特征(ROC)曲线、校准曲线及临床决策曲线(DCA)评估诊断模型的预测价值和临床实用性。结果CD161+%(OR=0.768;95%CI 0.697~0.845;P<0.001)、AST(OR=0.961;95%CI 0.930~0.993;P=0.019)、吸烟史(OR=3.181;95%CI 1.149~8.804;P=0.026)是肺结核发生的独立影响因素。在训练集和测试集中,ROC曲线下面积(AUC)达到了0.870(95%CI 0.816~0.924)和0.887(95%CI 0.827~0.948),Hosmer-Lemeshow拟合优度检验结果显示,拟合度较好(训练集χ^(2)=6.213,P=0.623;测试集χ^(2)=6.197,P=0.625)。DCA表明该模型对肺结核发生概率诊断具有较好的参考意义。结论利用CD161+%、AST、吸烟史构建诊断模型具有一定的诊断性能,便于临床快速区别肺结核与非结核性肺病。Objective To construct and validate a clinical diagnostic model to differentiate between pulmonary tuberculosis and non-tuberculous lung diseases.Methods Information was collected from 258 patients with respiratory system diseases,and they were divided into a training set of 152 cases and a test set of 106 cases with a ratio of 6∶4 using the random number seed method in R software.The training set was further divided into a tuberculosis group of 95 cases and a non-tuberculosis group of 57 cases,and the test set into a tuberculosis group of 65 cases and a non-tuberculosis group of 41 cases based on the diagnosis of pulmonary tuberculosis.A diagnostic model was constructed using multivariate logistic regression analysis to determine the influencing factors of pulmonary tuberculosis.The diagnostic value and clinical utility of the model were assessed using the receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis(DCA).Results CD161+%(OR=0.768;95%CI 0.697-0.845;P<0.001),AST(OR=0.961;95%CI 0.930-0.993;P=0.019),and smoking history(OR=3.181;95%CI 1.149-8.804;P=0.026)were identified as independent risk factors for the occurrence of pulmonary tuberculosis.In both the training and test sets,the area under the ROC curve(AUC)reached 0.870(95%CI 0.816-0.924)and 0.887(95%CI 0.827-0.948),respectively.The Hosmer-Lemeshow goodness-of-fit test showed a good fit(training setχ^(2)=6.213,P=0.623;test setχ^(2)=6.197,P=0.625).DCA indicated that the model had good reference significance for the diagnosis of the probability of pulmonary tuberculosis occurrence.Conclusion The diagnostic model constructed using the percentage of CD161+%,AST levels,and smoking history has certain diagnostic performance,facilitating rapid clinical differentiation between pulmonary tuberculosis and non-tuberculous lung diseases.

关 键 词：肺结核 非结核性肺病 CD161+% 流式细胞术 诊断模型 列线图 

分 类 号：R446[医药卫生—诊断学]