The author carried out a comparative study of various forced expiratory volume measures focusing on childhood asthma, in search of the best indicator of airway obstruction in children. The measures compared included percent predicted values of forced vital capacity, forced expiratory flow between 25% and 75% of vital capacity, forced expiratory volume in
t= 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 seconds, written as %FVC, %FEF
25-75%, %FEVt and FEVt%, which denoted the percentage ratio of FEVt to FVC.
Standardized spirometry was performed using a 9-liter, water-sealed spirometer. Predicted values were calculated by means of regression equations, using the power function of standing height, which were derived from 432 healthy subjects consisted of 232 males and 200 females between 9 and 14 years of age. A total of 240 asthmatic subjects, subdivided into equal groups A and B, and composed of even age and sex distributions, were selected from a larger group of children with physician-diagnosed asthma. Group A had been asymptomatic for the past year, while group B had been symptomatic. Between-group differences for various measures were quantified by means of the Mann-Whitney U-test. Using the lower end of a 95% confidence limit for each measure calculated for the healthy subjects by sex, the abnormality rates on the different measures were compared between the healthy and the asthmatic subjects, as well as between groups A and B by a chi-square test.
In conclusion, FEV
1.0% proved to be the most powerful single measure for the detection of mild airway obstruction in attack-free asthmatic children. The rates of abnormality revealed by FEV
1.0% were 47.5% for group A and 70.0% for group B. The use of FEVt% with shorter time periods did not increase the sensitivity. FEV
1.0% was constant within the age-range studied, but about 2% higher in females than in males. Although, the mean absolute difference between the healthy and the asthmatic subjects was largest at %FEF
25-75%, it fell behind FEV
1.0% in terms of discriminatory power because of its larger data variance.
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