Neutrophil-to-Lymphocyte Ratio Predicts Clinical Outcome of Severe Acute Exacerbation of COPD in Frequent Exacerbators

Fang-Ying Lu,1,2,* Rong Chen,1,2,* Ning Li,1,2 Xian-Wen Sun,1,2 Min Zhou,1,2 Qing-Yun Li,1,2 Yi Guo1,2 1Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 2Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yi GuoDepartment of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Rui Jin 2nd Road, Shanghai, 200025, People’s Republic of ChinaFax +86 21 6467 4301Email guoyi621@qq.comQing-Yun LiDepartment of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Rui Jin 2nd Road, Shanghai, 200025, People’s Republic of ChinaFax +86 21 6467 4301Email liqingyun68@hotmail.comPurpose: Frequent exacerbators are a specific phenotype of chronic obstructive pulmonary disease (COPD), whose clinical characteristics and prognostic biomarkers during severe acute exacerbation (AECOPD) have not yet been fully elucidated. The aim of this study was to investigate the clinical features of severe AECOPD in frequent exacerbators and explore the predictive value of the neutrophil-to-lymphocyte ratio (NLR) for outcome in this phenotype during severe exacerbation.Patients and Methods: A total of 604 patients with severe AECOPD were retrospectively included in the study. Subjects were defined as frequent exacerbators if they experienced two or more exacerbations in the past year. Clinical characteristics and worse outcome (ICU admission, or invasive ventilation, or in-hospital mortality) during severe AECOPD were compared between frequent exacerbators and non-frequent ones. Furthermore, the relationship between NLR and worse outcome in frequent exacerbators was analyzed using logistic regression