Abstract
OBJECTIVE
This study aimed to assess the diagnostic utility of spirometry, particularly focusing on small airway parameters, in children with cough variant asthma (CVA).
METHODS
This study included children aged 5-12 years with a diagnosis of CVA. Pre- and postbronchodilation spirometry parameters, including FEV1 %pred, FVC%pred, FEV1 /FVC%pred, PEF%pred, FEF25 %pred, FEF50 %pred, FEF75 %pred, MMEF%pred, were recorded. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) was calculated to assess the discriminatory potential of these spirometry parameters for CVA. A prediction model based on logistic regression (LR) was performed.
RESULTS
A total of 200 patients with CVA and 73 control subjects were included. Baseline spirometry parameters in the CVA group, except for FVC%pred, were significantly lower compared to the control group. After inhalation of salbutamol sulfate, all parameters showed significant improvement in the CVA group. However, these parameters, except for FEV1 %pred and FVC%pred, remained lower in the CVA group compared to the control group. The improvement rate of each parameter in the CVA group, except for ∆ FVC%, was significantly higher than that in the control group. △ MMEF% achieved the highest AUC of 0.797 with a threshold value of 16.09%, followed by △ FEF75 % (0.792), △ FEV1 % (0.756), and △ FEF50 % (0.747) with threshold values of 19.01%, 4.48%, and 19.4%, respectively. The clinical prediction model included four variables (age, △ FEF25 %, △ FEF75 %, and △ MMEF%) and demonstrated excellent performance distinguishing patients with and without CVA (AUC = 0.850). In the CVA group, the △ FEV1 % showed a positive correlation with small airway parameters.
CONCLUSIONS
This study highlights that children with CVA exhibit lower pulmonary function parameters compared to healthy children. Changes in small airway parameters during bronchodilator tests can be valuable in diagnosing CVA, and the LR prediction model incorporating age and several pulmonary parameters can assist physicians in accurately identifying CVA in clinical practice.
Collapse