Development of a prediction model to identify undiagnosed chronic obstructive pulmonary disease patients in primary care settings in China

作     者：Buyu Zhang Dong Sun Hongtao Niu Fen Dong Jun Lyu Yu Guo Huaidong Du Yalin Chen Junshi Chen Weihua Cao Ting Yang Canqing Yu Zhengming Chen Liming Li on behalf of the China Kadoorie Biobank Collaborative Group 

作者机构：Department of Epidemiology and BiostatisticsSchool of Public HealthPeking UniversityBeijing 100191China Department of Pulmonary and Critical Care MedicineCenter of Respiratory MedicineChina–Japan Friendship HospitalBeijing 100029China National Center for Respiratory Medicine and National Clinical Research Center for Respiratory DiseasesBeijing 100029China Institute of Respiratory MedicineChinese Academy of Medical SciencesBeijing 100007China Peking University Center for Public Health and Epidemic Preparedness and ResponseBeijing 100191China Key Laboratory of Molecular Cardiovascular Sciences(Peking University)Ministry of EducationBeijing 100191China National Center for Cardiovascular DiseasesFuwai HospitalChinese Academy of Medical SciencesBeijing 100037China Medical Research Council Population Health Research Unit at the University of OxfordOxford OX37LFUK Clinical Trial Service Unit and Epidemiological Studies UnitNuffield Department of Population HealthUniversity of OxfordOxford OX37LFUK Maiji Center for Disease Control and PreventionTianshuiGansu 741020China China National Center for Food Safety Risk AssessmentBeijing 100022China 不详 

出 版 物：《Chinese Medical Journal》 (中华医学杂志（英文版）)

年 卷 期：2023年第136卷第6期

页      面：676-682页

核心收录：

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

基　　金：supported by the National Key Research&Development Program of China(Nos.2016YFC1303904 and 2016YFC0900500) National Natural Science Foundation of China(Nos.81941018,91846303,and 91843302) 

主　　题：Chronic obstructive pulmonary disease Screening Prediction model China Kadoorie Biobank 

摘      要：Background:At present,a large number of chronic obstructive pulmonary disease(COPD)patients are undiagnosed in ***,this study aimed to develop a simple prediction model as a screening tool to identify patients at risk for ***:The study was based on the data of 22,943 subjects aged 30 to 79 years and enrolled in the second resurvey of China Kadoorie Biobank during 2012 and 2013 in *** stepwisely selected the predictors using logistic regression *** we tested the model validity through P-P graph,area under the receiver operating characteristic curve(AUROC),ten-fold cross validation and an external validation in a sample of 3492 individuals from the Enjoying Breathing Program in ***:The final prediction model involved 14 independent variables,including age,sex,location(urban/rural),region,educational background,smoking status,smoking amount(pack-years),years of exposure to air pollution by cooking fuel,family history of COPD,history of tuberculosis,body mass index,shortness of breath,sputum and *** model showed an area under curve(AUC)of 0.72(95%confidence interval[CI]:0.72-0.73)for detecting undiagnosed COPD patients,with the cutoff of predicted probability of COPD=0.22,presenting a sensitivity of 70.13%and a specificity of 62.25%.The AUROC value for screening undiagnosed patients with clinically significant COPD was 0.68(95%CI:0.66-0.69).Moreover,the ten-fold cross validation reported an AUC of 0.72(95%CI:0.71-0.73),and the external validation presented an AUC of 0.69(95%CI:0.68-0.71).Conclusion:This prediction model can serve as a first-stage screening tool for undiagnosed COPD patients in primary care settings.