Demographic and Clinical Characteristics of COPD Patients at Different Blood Eosinophil Levels in the UK Clinical Practice Research Datalink.

COPD-JOURNAL OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE(2018)

引用 39|浏览7
暂无评分
摘要
Blood eosinophil count may be a useful biomarker for predicting response to inhaled corticosteroids and exacerbation risk in chronic obstructive pulmonary disease (COPD) patients. The optimal cut point for categorizing blood eosinophil counts in these contexts remains unclear. We aimed to determine the distribution of blood eosinophil count in COPD patients and matched non-COPD controls, and to describe demographic and clinical characteristics at different cut points. We identified COPD patients within the UK Clinical Practice Research Database aged >= 40 years with a FEV1/FVC <0.7, and >= 1 blood eosinophil count recorded during stable disease between January 1, 2010 and December 31, 2012. COPD patients were matched on age, sex, and smoking status to non-COPD controls. Using all blood eosinophil counts recorded during a 12-month period, COPD patients were categorized as "always above," "fluctuating above and below," and " never above" cut points of 100, 150, and 300 cells/mu L. The geometric mean blood eosinophil count was statistically significantly higher in COPD patients versus matched controls (196.6 cells/mu L vs. 182.1 cells/mu L; mean difference 8%, 95% CI: 6.8, 9.2), and in COPD patients with versus without a history of asthma (205.0 cells/mu L vs. 192.2 cells/mu L; mean difference 6.7%, 95%, CI: 4.9, 8.5). About half of COPD patients had all blood eosinophil counts above 150 cells/mu L; this persistent higher eosinophil phenotype was associated with being male, higher body mass index, and history of asthma. In conclusion, COPD patients demonstrated higher blood eosinophil count than non-COPD controls, although there was substantial overlap in the distributions. COPD patients with a history of asthma had significantly higher blood eosinophil count versus those without.
更多
查看译文
关键词
Biomarker distribution,disease severity,disease characteristics,GOLD,demographic variables,comorbidities
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要