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

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.