Logistic regression model for prediction of airway reversibility using peak expiratory flow

ABSTRACT

Using peak expiratory flow [PEF] as an alternative to spirometry parameters [FEV1 and FVC], for detection of airway reversibility in diseases with airflow limitation is challenging. We developed logistic regression [LR] model to discriminate bronchodilator responsiveness [BDR] and then compared the results of models with a performance of >18%, >20%, and >22% increase in delta PEF% [PEF change relative to baseline], as a predictor for bronchodilator responsiveness [BDR]. PEF measurements of pre-bronchodilator, postbronchodilator and delta PEF% of 90 patients with asthma [44] and chronic obstructive pulmonary disease [46] were used as inputs of model and the output was presence or absence of the BDR. Although delta PEF% was a poor discriminator, LR model could improve the accuracy of BDR. Sensitivity, specificity, positive predictive value, and negative predictive value of LR were 68.89%, 67.27%, 71.43%, and 78.72%, respectively. The LR is a reliable method that can be used clinically to predict BDR based on PEF measurements