The challenges of identifying pulmonary embolism in patients hospitalized for exacerbations of COPD

Background: Chronic obstructive pulmonary disease (COPD) is associated with airflow obstruction that threatens global health. During the hospitalization of patients with acute exacerbations of COPD (AECOPD), the high prevalence of pulmonary embolism (PE) seriously affects the prognosis of disease. This study aims to assess the differences in clinical data between patients with AECOPD and patients with AECOPD-PE, and to identify the relevant factors of PE. Methods: We performed a retrospective case-control study in AECOPD patients between January 2018 and December 2021. Due to suspected PE, all patients underwent radiological examination. Patients without PE were included as controls. Clinical data and laboratory tests were recorded. Univariate analysis and multivariate logistic regression analysis were used to investigate the independent predictors of PE. Receiver operating characteristics (ROC) curves was performed to evaluate the effect of risk factors on PE prediction. Results: A total of 191 patients were included for analysis, divided into the AECOPD group (96 cases) and AECOPD-PE group (95 cases). No statistic differences were detected in demographic characteristics between patients with AECOPD and patients with AECOPD and PE. Average PO 2 and PCO 2 levels, lung function, and Echocardiographic indicator were not associated with PE. The concentration of D-dimer, the proportion of simplified wells score >= 2, and the incidence rate of lower extremity deep vein thrombosis (DVT) remarkably increased in AECOPD-PE group than AECOPD individuals. At multivariate analysis, the above three indicators were closely relevant to the occurrence of PE. The AUC value for D-dimer combined with lower extremity DVT and Simplified Wells Score was 0.729. Conclusions: D-dimer, lower extremity DVT, and simplified wells score >= 2 were relevant to higher risks of PE, which will help to improve clinicians ' understanding of PE secondary to AECOPD. (c) 2024 SPLF and Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.