呼出气一氧化氮、肺泡一氧化氮和嗜酸性粒细胞对3~6岁儿童呼吸系统疾病的鉴别诊断价值

Diagnostic value of exhaled nitric oxide,alveolar nitric oxide and eosinophils in respiratory diseases among children aged 3 to 6 years

目的探讨呼出气一氧化氮(FeNO)、肺泡一氧化氮(CaNO)和嗜酸性粒细胞(EOS)在甘肃省兰州市3~6岁儿童呼吸系统疾病鉴别诊断中的应用价值。方法选取确诊哮喘或过敏性鼻炎或下呼吸道感染的360例3~6岁儿童作为研究对象,采用斯皮尔曼秩相关系数评估FeNO、CaNO、EOS的相关性,通过随机森林模型、受试者工作特征(ROC)曲线、多因素逻辑回归分析评估FeNO、CaNO和EOS对3种疾病的鉴别诊断价值。结果哮喘患儿的FeNO、CaNO中位数高于其他疾病患儿,过敏性鼻炎患儿的EOS中位数最低,下呼吸道感染患儿的FeNO、CaNO中位数最低。相关性分析结果显示,FeNO与CaNO呈正相关(r=0.59,P<0.05),FeNO与EOS呈负相关(r=-0.61,P<0.05),CaNO与EOS呈负相关(r=-0.63,P<0.05)。随机森林模型显示,FeNO在疾病分类中的重要性最高。ROC曲线分析结果显示,3种疾病中,FeNO、CaNO、EOS对下呼吸道感染的诊断效能均最高(曲线下面积分别为0.86、0.91、1.00)。多因素逻辑回归模型诊断哮喘的曲线下面积为0.96,灵敏度为0.902,特异度为0.881。结论FeNO、CaNO和EOS在鉴别诊断兰州地区3~6岁儿童哮喘、过敏性鼻炎、下呼吸道感染方面展现出较好的潜力,且基于三者构建的多因素逻辑回归模型可有效提升对哮喘的诊断准确性。

Objective To investigate the application value of fractional exhaled nitric oxide(FeNO),alveolar nitric oxide(CaNO),and eosinophils(EOS)in the differential diagnosis of respiratory diseases in children aged 3 to 6 years in Lanzhou,Gansu Province.Methods A total of 360 children aged 3 to 6 years with confirmed asthma,allergic rhinitis,or lower respiratory tract infection were selected as research subjects.Spearman′s rank correlation coefficient was used to evaluate the correlation between FeNO,CaNO and EOS.The diagnostic value of FeNO,CaNO,and EOS for the differential diagnosis of the three diseases was assessed through random forest models,receiver operating characteristic(ROC)curves,and multivariate Logistic regression analysis.Results The median valuesof FeNO and CaNO were higher in asthmatic children than in those with other diseases.The median value of EOS was the lowest in children with allergic rhinitis,and the median values of FeNO and CaNO were the lowest in children with lower respiratory tract infection.Correlation analysis showed a positive correlation between FeNO and CaNO(r=0.59,P<0.05),a negative correlation between FeNO and EOS(r=-0.61,P<0.05),and a negative correlation between CaNO and EOS(r=-0.63,P<0.05).The random forest model indicated that FeNO had the highest importance in disease classification.ROC curve analysis revealed that FeNO,CaNO,and EOS had the highest diagnostic efficiency for lower respiratory tract infection among the three diseases(with areas under the curve of 0.86,0.91,and 1.00,respectively).The area under the curve of the multivariate Logistic regression model for diagnosing asthma was 0.96,with a sensitivity of 0.902 and a specificity of 0.881.Conclusion FeNO,CaNO and EOS demonstrate good potential in the differential diagnosis of asthma,allergic rhinitis,and lower respiratory tract infection in children aged 3 to 6 years in Lanzhou.Furthermore,the multivariate Logistic regression model based on these three factors can effectively improve the diagnostic accuracy of asthma.