医学
慢性阻塞性肺病
恶化
逻辑回归
接收机工作特性
内科学
曲线下面积
重症监护医学
作者
Dan Zhu,Huiling Dai,Haiyan Zhu,Yuang Fang,Huihui Zhou,Qian Zhang,Shuguang Chu,Qian Xi
标识
DOI:10.1016/j.rmed.2023.107150
摘要
Background Chronic obstructive pulmonary disease (COPD) is a common disease with high morbidity, with acute exacerbations manifesting as a worsening of respiratory symptoms. This study aimed to identify the frequent acute exacerbation phenotype in patients with COPD based on imaging and clinical characteristics. Methods Patients with COPD (n = 201) were monitored for acute exacerbations one year after their initial hospital admission and further divided into frequent and non-frequent exacerbation groups according to the frequency and severity of acute exacerbations. All patients underwent high resolution CT scans and low attenuation area less than −950Hu (LAA-950) in the whole lung was measured. Differences in visual subtypes, LAA-950, and clinical basic characteristics were compared between groups. The clinical factors influencing frequent exacerbation were determined using binary logistic regression. Finally, based on imaging and clinical factors, the receiver operating characteristic curve was used to identify the phenotype of COPD with frequent acute exacerbations. Results Patients with frequent exacerbations had a larger LAA-950 than those non-frequent exacerbations patients (p<0.001). Frequent acute exacerbations were associated with worsening visual subtypes. Multivariate binary logistic regression illustrated that age, smoking status, BMI, FEV1 pred, and LAA-950 were associated with frequent exacerbations of COPD. The area under the receiver operating characteristic curve for predicting frequent exacerbations based on age, smoking status, BMI, FEV1 pred, and LAA-950 was 0.907 (p<0.001). Conclusion The combination of imaging and clinical characteristics reached high diagnostic efficacy in the identification of frequent acute exacerbations in patients with COPD.
科研通智能强力驱动
Strongly Powered by AbleSci AI