Aiyuan Zhou1,2,3, Lijuan Luo1,2,3, Nian Liu4, Cheng Zhang5, Yahong Chen6, Yan Yin7, Jing Zhang8, Zhiyi He9, Lihua Xie10, Jungang Xie11, Jinhua Li1,2,3, Zijing Zhou1,2,3, Yan Chen1,2,3, Ping Chen1,2,3. 1. Department of Respiratory and Critical Care Medicine, Second Xiangya Hospital, Central South University, Changsha, China. 2. Research Unit of Respiratory Disease, Central South University, Changsha, China. 3. Diagnosis and Treatment Center of Respiratory Disease, Central South University, Changsha, China. 4. Department of Respiratory Medicine, Hunan Provincial People's Hospital, Hunan Normal University, Changsha, China. 5. Department of Respiratory Medicine, The People's Hospital of Guizhou Province, Guiyang, China. 6. Department of Respiratory Medicine, Third Hospital of Peking University, Beijing, China. 7. Department of Respiratory Medicine, First Hospital of China Medical University, Shenyang, China. 8. Department of Respiratory Medicine, Zhong Shan Hospital of Fudan University, Shanghai, China. 9. Evidence-Based Medical Center, First Affiliated Hospital of Guangxi Medical University, Nanning, China. 10. Department of Respiratory Medicine, Third Xiangya Hospital, Central South University, Changsha, China. 11. Department of Respiratory and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Abstract
BACKGROUND AND OBJECTIVE: ACO is a syndrome with high prevalence. However, a pragmatic diagnostic criterion to differentiate ACO is non-existent. We aimed to establish an effective model for screening ACO. METHODS: A multicentre survey was developed to assess the clinical criteria considered important and applicable by pulmonologists for screening ACO. These experts were asked to take the surveys twice. The expert grading method, analytic hierarchy process and ROC curve were used to establish the model, which was then validated by a cross-sectional study of 1066 patients. The GINA/GOLD document was the gold standard in assessing this model. RESULTS: Increased variability of symptoms, paroxysmal wheezing, dyspnoea, historical diagnosis of COPD or asthma, allergic constitution, exposure to risk factors, the FEV1 /FVC < 70% and a positive BDT were important for screening ACO. According to the weight of each criterion, we confirmed that patients meeting six or more of these eight criteria should be considered to have ACO. We called this Chinese screening model for ACO 'CSMA'. It differentiated patients with ACO with a sensitivity of 83.33%, while the sensitivity of clinician-driven diagnosis had a sensitivity of only 42.73%. CONCLUSION: CSMA is a workable model for screening ACO and provides a simple tool for clinicians to efficiently diagnose ACO.
BACKGROUND AND OBJECTIVE: ACO is a syndrome with high prevalence. However, a pragmatic diagnostic criterion to differentiate ACO is non-existent. We aimed to establish an effective model for screening ACO. METHODS: A multicentre survey was developed to assess the clinical criteria considered important and applicable by pulmonologists for screening ACO. These experts were asked to take the surveys twice. The expert grading method, analytic hierarchy process and ROC curve were used to establish the model, which was then validated by a cross-sectional study of 1066 patients. The GINA/GOLD document was the gold standard in assessing this model. RESULTS: Increased variability of symptoms, paroxysmal wheezing, dyspnoea, historical diagnosis of COPD or asthma, allergic constitution, exposure to risk factors, the FEV1 /FVC < 70% and a positive BDT were important for screening ACO. According to the weight of each criterion, we confirmed that patients meeting six or more of these eight criteria should be considered to have ACO. We called this Chinese screening model for ACO 'CSMA'. It differentiated patients with ACO with a sensitivity of 83.33%, while the sensitivity of clinician-driven diagnosis had a sensitivity of only 42.73%. CONCLUSION: CSMA is a workable model for screening ACO and provides a simple tool for clinicians to efficiently diagnose ACO.