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Eosinopenia as a biomarker for antibiotic use in COPD exacerbations: protocol for a retrospective hospital-based cohort study

INTRODUCTION: The acute exacerbation of chronic obstructive pulmonary disease (AECOPD) has a seriously negative impact on patients’ healths condition and disease progression. Bacterial infection is closely related to AECOPD, and antibiotics are frequently used in clinical practice. The lack of speci...

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Detalles Bibliográficos
Autores principales: Yang, Mei, Liu, Xuemei, Hu, Qiongqiong, Li, Junjie, Fu, Sijia, Chen, Daohong, Wu, Yanqing, Luo, Ai, Zhang, Xiawei, Feng, Ruizhi, Xu, Guo, Liu, Can, Jiang, Hongli, Liu, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8783821/
https://www.ncbi.nlm.nih.gov/pubmed/35058259
http://dx.doi.org/10.1136/bmjopen-2021-051939
Descripción
Sumario:INTRODUCTION: The acute exacerbation of chronic obstructive pulmonary disease (AECOPD) has a seriously negative impact on patients’ healths condition and disease progression. Bacterial infection is closely related to AECOPD, and antibiotics are frequently used in clinical practice. The lack of specific biomarkers for rational antibiotics use always leads to antibiotics abuse in chronic obstructive pulmonary disease (COPD) flare-ups. Eosinopenia has been considered to be related to increased bacterial load of potentially pathogenic organisms at the onset of COPD exacerbations. Therefore, this study aims to investigate whether eosinopenia could be used as a reference for the use of antibiotics in AECOPD. METHODS AND ANALYSIS: In this study, a hospital-based retrospective cohort design will be adopted to analyse the clinical data of inpatients who are primarily diagnosed with AECOPD in West China Hospital of Sichuan University from 1 January 2010 to 31 December 2020. Relevant data will be extracted from the Clinical Big Data Platform for Scientific Research in West China Hospital, including demographic characteristics, blood eosinophil count, procalcitonin, C reactive protein, microbial cultivation, antibiotics use, length of hospital stay, non-invasive ventilation use, intensive care unit transfer and mortality, etc. The collected data will be described and inferred by corresponding statistical methods according to the data type and their distributions. Multiple binary logistic regression models will be used to analyse the relationship between blood eosinophil count and bacterial infection. The antibiotics use, and patient morbidity and mortality will be compared between patients with or without eosinopenia. ETHICS AND DISSEMINATION: This study has been approved by the Biomedical Ethics Review Board of West China Hospital of Sichuan University (Approval No. 2020-1056). And the research results will be published in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: ChiCTR2000039379.