基于胸部CT及临床特征构建原发性干燥综合征患者肺脏受累的风险预测模型  

Construction of a risk prediction model of lung involvement based on chest CT and clinical features in patients with primary Sjogren's syndrome

作　　者：侯鸣 李有强 李雪梅[1] 贾军峰[1] 常军英[1] HOU Ming;LI Youqiang;LI Xuemei;JIA Junfeng;CHANG Junying(Handan Traditional Chinese Medicine Hospital,Handan 056001,China)

机构地区：[1]邯郸市中医院,河北邯郸056001

出　　处：《实用医学杂志》2024年第3期400-405,共6页The Journal of Practical Medicine

基　　金：河北省中医药管理局科研计划项目(编号:2020579)。

摘　　要：目的基于胸部CT及临床特征构建原发性干燥综合征(primary Sjogren′s syndrome,pSS)患者肺脏受累的风险预测模型,并探讨模型的风险预测价值。方法回顾性选取邯郸市中医院于2020年10月至2023年8月收治的360例pSS患者为研究对象,按照7∶3的分配比例分为建模组252例和验证组108例。建模组患者根据肺脏受累与否分为对照组201例和受累组51例。收集建模组患者临床特征资料与胸部高分辨CT(high resolution CT,HRCT)特点,行组间单因素分析确定收集信息中影响pSS患者肺脏受累的相关因素。对相关因素行二元logistic回归分析以筛选独立危险因素,并以独立危险因素建立预测模型,通过验证组资料收集配合完成列线图预测模型的验证与价值分析。结果患者年龄、病程、咳嗽、雷诺现象、C反应蛋白(C-reactive protein,CRP)、抗SSA抗体、HRCT等为影响pSS患者肺脏受累的相关因素(P<0.05)。进一步行二元logistic回归分析发现,患者年龄大、病程长、咳嗽及HRCT异常为影响SS患者肺脏受累的独立危险因素(P<0.05)。以独立影响因素构建列线图风险预测模型,模型验证结果提示,校准图显示预测模型性能良好;建模组受试者工作特征(ROC)曲线的曲线下面积(AUC)为0.993;验证组ROC的AUC为0.995。结论pSS患者临床特征与胸部CT结果与患者肺脏受累密切相关,其中患者年龄大、病程长、咳嗽及HRCT异常为影响pSS患者肺脏受累的独立危险因素,以此为基础建立预测模型对患者后装放疗是否发生肺脏受累具有较高预测价值。Objective To construct a risk prediction model of pulmonary involvement based on chest CT and clinical feature in patients with primary Sjogren′s syndrome(pSS),and to explore the risk prediction value of the model.Methods A total of 360 pSS patients who had been treated at Handan Hospital of Traditional Chinese Medicine from October 2020 to August 2023 were retrospectively selected as study objects,and were then divided into a modeling group(252 patients)and a verification group(108 patients)according to a ratio of 7∶3.The patients in the modeling group were divided into a control group(201 patients)and an involvement group(51 patients)based on presence or absence of lung involvement.The data on clinical characteristics and features of chest high⁃resolution CT(HRCT)in the modeling group was collected.Univariate analysis was performed among the groups to determine the relevant factors affecting lung involvement in pSS patients.Binary logistic regression analysis was performed on related factors to screen independent risk factors.A prediction model was established based on the independent risk factors.A verification and value analysis of the column⁃line prediction model were completed through data collection of the verification group.Results Age,disease course,cough,Raynaud′s phenomenon,C⁃reactive protein(CRP),anti⁃SSA antibody,and HRCT were the relevant factors affecting lung involvement in pSS patients(all P<0.05).Further binary logistic regression analysis showed that old age,prolonged disease course,cough and abnormal HRCT imaging were independent risk factors for lung involvement in SS patients(all P<0.05).A nomogram risk prediction model was constructed based on independent factors.The model verification results indicated that the calibration chart showed better performance in the prediction model.The AUC of the area under the receiver operating characteristic(ROC)curve was 0.993 the modeling group and 0.995 in the validation group.Conclusions The clinical characteristics and the results of ches

关 键 词：胸部高分辨CT 临床特征 原发性干燥综合征 肺脏受累 因素分析 风险预测模型 

分 类 号：R593.2[医药卫生—内科学] R816.4[医药卫生—临床医学]