AUTHOR=Wu Ji , Lu Yao , Dong Sunbin , Wu Luyang , Shen Xiping 

TITLE=Predicting COPD exacerbations based on quantitative CT analysis: an external validation study

JOURNAL=Frontiers in Medicine

VOLUME=Volume 11 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1370917

DOI=10.3389/fmed.2024.1370917

ISSN=2296-858X

ABSTRACT=Purpose: Quantitative computed tomography (CT) analysis is an important method for diagnosis and severity evaluation of lung diseases. However, the association between CT-derived biomarkers and chronic obstructive pulmonary disease (COPD) exacerbations remains unclear. We aimed to investigate its potential in predicting COPD exacerbations.Methods: Patients with COPD were consecutively enrolled and their data was analyzed in this retrospective study. Body composition and thoracic abnormalities were analyzed from chest CT scans. Logistic regression analysis was performed to identify independent risk factors of exacerbation. Based on 2-year follow-up data, the deep learning system (DLS) was developed for predicting future exacerbations. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance. Finally, survival analysis was performed to further evaluate the potential of the DLS in risk stratification.Results: 1150 eligible patients were included and followed up for two years.Multivariate analysis revealed that CT-derived high affected lung volume /total lung capacity (ALV/TLC) ratio, high visceral adipose tissue area (VAT) and low pectoralis muscle cross-sectional area (CSA) were independent risk factors causing COPD exacerbations. The DLS outperformed exacerbation history and the BMI, airflow obstruction, dyspnea, exercise capacity (BODE) index, with an area under of ROC (AUC) value of 0.88(95%CI, 0.82-0.92) in the internal cohort, and 0.86(95%CI, 0.81-0.89) in the external cohort. Delong test revealed significance between this system and conventional scores in the test cohorts(P<0.05). In survival analysis, patients with higher risk were susceptible to exacerbations events.The DLS could allow accurate prediction of COPD exacerbations. The newly identified CT biomarkers (ALV/TLC ratio, VAT and pectoralis muscle CSA) could potentially enable investigation into underlying mechanisms responsible for exacerbations.