Development and validation of a predictive model for COPD: a multicenter study

Yaqin Wang^1*yanyan lv¹Li Qiushuang¹Ronghua Zhang^2,3Bohua Yan⁴hanrong xue⁵xiangqin qian⁶yu yang⁷kaiwen ni¹jiayan 钟¹xiang meng⁸Rundi Gao^1*Zhen Wang^1*

• ¹First Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, China
• ²College of Pharmacy, Jinan University, Guangzhou, China
• ³Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Jinan University, Guangzhou, China
• ⁴Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
• ⁵Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi Province, China
• ⁶Jiashan First People's Hospital, Jiaxing, China
• ⁷College of Information Science and Technology, Jinan University, Guangzhou, China
• ⁸Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Zhejiang Chinese Medical University, Wenzhou, Zhejiang Province, China

Background: Chronic obstructive pulmonary disease (COPD) is the third leadin g cause of death globally and a major public health issue in China. This study aims to develop a COPD predictive model and conduct risk stratification for key indicators not included.We collected data from inpatients and outpatients with COPD and n on-COPD who were hospitalized between January 2018 and December 2022 at three different hospitals. The data were divided into a training set and an inte rnal validation set, using logistic regression to build a COPD predictive model and perform internal validation. External validation of the model was performe d using data from two additional units for the period November 2019 to JuneResults: A total of 1,056 cases were included: 740 in the training set, 316 in the internal validation set, and 408 in the external validation set. Six risk facto rs were identified: age (OR=1.05, 95%CI: 1.02-1.08), second-hand smoke expo sure (OR=8.27, 95%CI: 2.70-25.34), cough (OR=23.52, 95%CI: 12.64-43.77), "occasional episodes of wheezing that are mild and do not interfere with sleep or activity" (OR=6.06, 95%CI: 2.59-14.19), "bouts of wheezing that worsen with movement" (OR=21.40, 95%CI: 10.32-44.37), and "persistent episodes of wheezing, occurring at rest, unable to lie down" (OR=10.97, 95%CI: 1.02-118.). The predictive model equation was: y = -5.920 + 0.047(age) + 2.113(smo ke exposure) + 3.158(cough) + 1.801(wheezing 1) + 3.063(wheezing 2) + 2.39 6(wheezing 3). The model achieved 94.1% accuracy, 98.5% sensitivity, and 89. 2% specificity, with an AUC of 0.976 (internal) and 0.691 (external). The criti cal cut-off value was 0.258.We have successfully developed a model for the diagnosis of CO PD. The predictive model equation was: y = -5.920 + 0.047(age) + 2.113(smo ke exposure) + 3.158(cough) + 1.801(wheezing 1) + 3.063(wheezing 2) + 2.39 6(wheezing 3).