Diagnostic value of combined detection of FeNO and pulmonary function indexes in children with cough variant asthma

联合检测呼出气一氧化氮(FeNO)和肺功能指标对咳嗽变异性哮喘患儿的诊断价值

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Abstract

Cough variant asthma (CVA) is a special type of asthma and one of the primary causes of chronic cough in children. The aim of this study was to investigate the diagnostic value of combined detection of fractional exhale nitric oxide (FeNO) and pulmonary function parameters in children with CVA. A retrospective study was conducted on 120 children with CVA. Sixty children (36 boys and 24 girls, age = 4.97 ± 0.928 years) with CVA admitted to the pediatric clinic of the hospital from June 2021 to June 2022 were selected for the CVA group, and 60 children (32 boys and 28 girls, age = 4.90 ± 0.706 years) with chronic cough without CVA who visited the pediatric clinic of the hospital during the same period were selected for the NCVA group. FeNO and pulmonary function parameters (maximum mid-expiratory flow [MMEF], maximal expiratory flow 25 [MEF25], maximal expiratory flow 50 [MEF50], maximal expiratory flow 75 [MEF75]) were compared between the 2 groups. Receiver operating characteristic curve was used to analyze the diagnostic value of FeNO and pulmonary function parameters for CVA, and the correlation between the severity of CVA and the above parameters was analyzed. The FeNO (41.90 ± 3.111 ppb) level in the CVA group was significantly higher than that in the NCVA group (19.98 ± 6.231 ppb), and the difference was statistically significant (P = .000). The MMEF, MEF75, MEF50, and MEF25 levels were lower than those in the NCVA group, and the differences were statistically significant (P = .000). Receiver operating characteristic curve analysis results showed that the area under the curve of FeNO, MMEF, MEF75, MEF50, and MEF25 in the diagnosis of CVA was 0.726, 0.638, 0.611, 0.709,and 0.734, respectively, and the area under the curve of the combined detection of the 5 indicators was 0.901. The combined detection has higher sensitivity and specificity, which can significantly improve the diagnostic efficacy of CVA.

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