Correlation of Serum Clara Cell Secretory Protein 16, Plasma Fibrinogen and Serum Amyloid A with the Severity of Acute Exacerbated COPD and Their Combination in Prognosis Assessment

Abstract

Xiaojuan Hu,1,* Jin Xu,1,* Pei Li,2 Hui Zheng3 1Department of Pulmonary and Critical Care Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200434, People’s Republic of China; 2Department of Nephrology, the Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050004, People’s Republic of China; 3Department of Clinical Laboratory, Fudan University Shanghai Cancer Center, Shanghai, 200032, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaojuan Hu, Department of Pulmonary and Critical Care Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200434, People’s Republic of China, Tel [phone], Email [email protected] Hui Zheng, Department of Clinical Laboratory, Fudan University Shanghai Cancer Center, Shanghai, 200032, People’s Republic of China, Tel [phone], Email [email protected]: Chronic obstructive pulmonary disease (COPD) has tremendous detrimental effects on patients’ quality of life, lung function, disease progression and socioeconomic burden. This study aimed to investigate new serum biomarkers for COPD detection. Three recently emerging biomarkers, including Clara cell secretory protein⁃16 (CC16), plasma fibrinogen (FIB) and serum amyloid A (SAA), were investigated for their potential in stratifying the severity of COPD.Methods: A total of 220 patients with AECOPD were recruited. Multivariate logistical regression was used to analyze odds ratios of an array of characteristic of patients, including age, global initiative for chronic obstructive lung disease (GOLD), diabetes mellitus, heart diseases, PaCO2, CC16, FIB, and SAA. Correlations of CC16, FIB and SAA levels to each other, GOLD, and PaCO2 were also measured using Spearman correlation. Receiver operating characteristic (ROC)/curve analysis was used to assess sensitivity and specificity of CC16, FIB, SAA and the combination of the three markers in identifying AECOPD patients with poor prognosis.Results: Our data suggested that age, GOLD, diabetes mellitus, heart diseases, PaCO2, CC16, FIB, and SAA are all significant risk factors for poor prognosis of AECOPD. CC16, FIB and SAA were positively correlated to each other and to GOLD and PaCO2 levels. CC16, FIB and SAA all had a high sensitivity and specificity in identifying patients with a poor prognosis. CC16, FIB and SAA are new markers with potentially high predictive value in AECOPD.Discussion: Our data support further development of these biomarkers to improve clinical management of AECOPD through providing more accurate prognosis of AECOPD patients that enable timely adjustment of treatment plans.Keywords: AECOPD, CC16, FIB, SAA

Keywords

• aecopd
• cc16
• fib
• saa