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

李阳, 张宇翔, 张蓉芳

李阳, 张宇翔, 张蓉芳. 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[J]. 实用临床医药杂志, 2024, 28(6): 74-78. DOI: 10.7619/jcmp.20233615
引用本文: 李阳, 张宇翔, 张蓉芳. 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[J]. 实用临床医药杂志, 2024, 28(6): 74-78. DOI: 10.7619/jcmp.20233615
LI Yang, ZHANG Yuxiang, ZHANG Rongfang. Diagnostic value of exhaled nitric oxide, alveolar nitric oxide and eosinophils in respiratory diseases among children aged 3 to 6 years[J]. Journal of Clinical Medicine in Practice, 2024, 28(6): 74-78. DOI: 10.7619/jcmp.20233615
Citation: LI Yang, ZHANG Yuxiang, ZHANG Rongfang. Diagnostic value of exhaled nitric oxide, alveolar nitric oxide and eosinophils in respiratory diseases among children aged 3 to 6 years[J]. Journal of Clinical Medicine in Practice, 2024, 28(6): 74-78. DOI: 10.7619/jcmp.20233615

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

基金项目: 

甘肃省兰州市科技局项目 2018-RC-97

详细信息
    通讯作者:

    张蓉芳, E-mail: lazyrfang@126.com

  • 中图分类号: R725.6;R446.1;R56

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

  • 摘要:
    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。

    Abstract:
    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.

  • 图  1   FeNO、CaNO、EOS的相关性分析热图

    图  2   变量重要性评分随机森林模型

    图  3   FeNO诊断3种疾病的ROC曲线

    图  4   CaNO诊断3种疾病的ROC曲线

    图  5   EOS诊断3种疾病的ROC曲线

    图  6   逻辑回归模型诊断哮喘的ROC曲线

    表  1   哮喘、过敏性鼻炎和下呼吸道感染疾病患儿的FeNO、CaNO、EOS的描述性统计分析

    指标 统计结果 下呼吸道感染(n=120) 哮喘(n=120) 过敏性鼻炎(n=120)
    FeNO/(×10-9mol/L) 平均值 14.05 26.60 21.37
    标准差 4.55 4.63 5.06
    最小值 2.20 13.90 10.87
    25%值 10.97 24.14 17.21
    中位数 13.92 26.63 21.93
    75%值 17.14 28.91 24.31
    最大值 24.67 39.32 40.26
    CaNO/(×10-9mol/L) 平均值 4.38 9.40 6.74
    标准差 1.46 1.57 1.42
    最小值 0.44 5.37 3.46
    25%值 3.56 8.23 5.74
    中位数 4.17 9.39 6.74
    75%值 5.41 10.57 7.75
    最大值 8.06 12.60 10.37
    EOS/% 平均值 11.75 2.75 2.17
    标准差 1.50 1.37 1.29
    最小值 7.65 0.34 0.47
    25%值 10.81 1.72 1.21
    中位数 11.80 2.83 2.00
    75%值 12.62 3.70 3.00
    最大值 15.47 6.42 6.24
    下载: 导出CSV
  • [1] 洪建国. 我国儿童哮喘流行病学和诊治状况[J]. 中华医学信息导报, 2020, 35(2): 22-22.
    [2]

    WISE S K, DAMASK C, ROLAND L T, et al. International consensus statement on allergy and rhinology: Allergic rhinitis-2023[J]. Int Forum Allergy Rhinol, 2023, 13(4): 293-859.

    [3]

    ZHU H Y, HAO C L, YU X M, et al. Fractional exhaled nitric oxide (FeNO) integrating airway hyperresponsiveness (AHR) examination promotes etiologic diagnosis and treatment for children with chronic cough[J]. Med Sci Monit, 2021, 27: e928502.

    [4]

    CELIS-PRECIADO C A, LACHAPELLE P, COUILLARD S. Exhaled nitric oxide (FeNO): bridging a knowledge gap in asthma diagnosis and treatment[J]. Clin Exp Allergy, 2023, 53(8): 791-793. doi: 10.1111/cea.14374

    [5]

    BARAÑSKI K, ZEJDA J E. Screening accuracy of FeNO measurement for childhood asthma in a community setting[J]. Children, 2022, 9(6): 858. doi: 10.3390/children9060858

    [6]

    SCHNEIDER A, FADERL B, SCHWARZBACH J, et al. Prognostic value of bronchial provocation and FENO measurement for asthma diagnosis: results of a delayed type of diagnostic study[J]. Respir Med, 2014, 108(1): 34-40. doi: 10.1016/j.rmed.2013.11.008

    [7] 李芮, 董晓艳, 蒋鲲, 等. 口鼻呼出气一氧化氮检测在儿童支气管哮喘控制评估及过敏性鼻炎诊断中的应用[J]. 中国当代儿科杂志, 2022, 24(1): 90-95. https://www.cnki.com.cn/Article/CJFDTOTAL-DDKZ202201013.htm
    [8]

    SCHNEIDER A, BRUNN B, HAPFELMEIER A, et al. Diagnostic accuracy of FeNO in asthma and predictive value for inhaled corticosteroid responsiveness: a prospective, multicentre study[J]. EClinicalMedicine, 2022, 50: 101533. doi: 10.1016/j.eclinm.2022.101533

    [9]

    MUNTEAN I A, BOCSAN I C, VESA S, et al. Could FeNO predict asthma in patients with house dust mites allergic rhinitis[J]. Medicina, 2020, 56(5): 235. doi: 10.3390/medicina56050235

    [10]

    NAKWAN N, THIDARAT RUKLERD T, PERKLEANG T, et al. The levels and correlations of FeNO, blood eosinophils and lung function in well-controlled asthma[J]. Adv Respir Med, 2022, 90(3): 183-192. doi: 10.5603/ARM.a2022.0015

    [11] 高永伟. 呼出气一氧化氮检测在小儿支气管哮喘早期诊断及病情评估中的应用[J]. 实用临床医药杂志, 2020, 24(3): 90-93. doi: 10.7619/jcmp.202003026
    [12]

    LIPWORTH B, KUO C R, CHAN R. 2020 Updated Asthma Guidelines: clinical utility of fractional exhaled nitric oxide (Feno) in asthma management[J]. J Allergy Clin Immunol, 2020, 146(6): 1281-1282. doi: 10.1016/j.jaci.2020.03.006

    [13]

    WANG J, WANG W T, LIN H, et al. Role of pulmonary function and FeNO detection in early screening of patients with ACO[J]. Exp Ther Med, 2020, 20(2): 830-837.

    [14] 任莉, 杨俊. 呼出气一氧化氮在儿童哮喘管理中的临床价值[J]. 实用临床医药杂志, 2022, 26(5): 96-99. doi: 10.7619/jcmp.20214064
  • 期刊类型引用(2)

    1. 贺婵婵,邓路路,路昭颖,郝瑛子,段雪蒙,张能,吴敬恒,李珊. 外周血S-100β水平鉴别大动脉粥样硬化型脑梗死的价值分析及对神经功能预后的影响. 卒中与神经疾病. 2024(06): 551-556 . 百度学术
    2. 林忠如. 恶性大脑中动脉梗死的预测和重症监护治疗方案分析. 医学食疗与健康. 2022(13): 5-7+28 . 百度学术

    其他类型引用(1)

图(6)  /  表(1)
计量
  • 文章访问数:  141
  • HTML全文浏览量:  31
  • PDF下载量:  7
  • 被引次数: 3
出版历程
  • 收稿日期:  2023-11-11
  • 修回日期:  2023-12-29
  • 网络出版日期:  2024-04-01
  • 刊出日期:  2024-03-27

目录

    /

    返回文章
    返回
    x 关闭 永久关闭