Blood Eosinophil Stability Predicts Clinical Outcomes in Hospitalized Patients with Acute Exacerbations of COPD

Blood eosinophil (EOS) levels are considered a potential biomarker for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) management, but the impact of EOS stability during hospitalization on outcomes is unclear. This study examined the relationship between EOS stability and clinical outcomes in these patients. A retrospective analysis was conducted on 2105 AECOPD patients hospitalized at Beijing Chao-Yang Hospital from 2013 to 2022. Patients were classified into four groups according to EOS counts (2%) at admission and discharge: persistent high, decreased, increased, and persistent low. Clinical characteristics and outcomes were compared between EOS stability groups. Multivariable logistic regression was used to evaluate the association between EOS stability and adverse hospital outcomes. Cox regression analysis was performed to assess the risk of AECOPD-related readmission within three years. Receiver operating characteristic (ROC) curves and nomograms were used to evaluate the predictive performance of the models. There were 586 (27.8%), 154 (7.3%), 593 (28.2%), and 772 (36.7%) patients in the persistent high, decreased, increased, and persistent low groups, respectively. Multivariable logistic regression analysis showed that the persistent high EOS group had a significantly lower risk of adverse hospital outcomes compared to the persistent low EOS group (aOR: 0.77, 95% CI: 0.59-0.99, P = 0.040). The nomogram developed based on the multivariate model demonstrated good predictive accuracy for adverse hospital outcomes, with an AUC of 0.67, and was well-calibrated. Cox regression analysis revealed that the persistent high EOS group had a higher risk of AECOPD-related readmission within three years compared to the persistent low EOS group (aHR: 1.38, 95% CI: 1.13-1.68, P = 0.001). Blood eosinophil stability during hospitalization is associated with AECOPD prognosis and may help guide inpatient treatment and identify patients at higher risk of future readmission.