Development and validation of a predictive model for COPD: a multicenter study - Wang, Yaqin; Lv, Yanyan; Li, Qiushuang; Zhang, Ronghua; Yan, Bohua; Xue, Hanrong; Qian, Xiangqin; Yang, Yu; Ni, Kaiwen; Zhong, Jiayan; Meng, Xiang; Gao, Rundi; et al. (Frontiers in Medicine, 2025)
BackgroundChronic obstructive pulmonary disease (COPD) is the third leading 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.MethodsWe collected data from inpatients and outpatients with COPD and non-COPD who were hospitalized between January 2018 and December 2022 at three different hospitals. The data were divided into a training set and an internal validation set, using logistic regression to build a COPD predictive model and perform internal validation. External validation of the model was performed using data from two additional units for the period November 2019 to June 2022.ResultsA 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 factors were identified: age (OR = 1.05, 95% CI: 1.02–1.08), second-hand smoke exposure (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: 1.02–118.28). The predictive model equation was: y = −5.920 + 0.047 (age) + 2.113 (smoke exposure) + 3.158 (cough) + 1.801 (wheezing 1) + 3.063 (wheezing 2) + 2.396 (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 critical cut-off value was 0.258.ConclusionWe have successfully developed a model for the diagnosis of COPD. The predictive model equation was: y = −5.920 + 0.047 (age) + 2.113 (smoke exposure) + 3.158 (cough) + 1.801 (wheezing 1) + 3.063 (wheezing 2) + 2.396 (wheezing 3).