Background: Spirometry is conventionally used to diagnose airway diseases in children with sickle cell disease (C-SCD). However, spirometry is difficult for younger children to perform, is effort dependent, and it provides limited information on respiratory mechanics. Impulse oscillometry (IOS) is an effort-independent pulmonary function test (PFT), which measures total airway resistance (R5Hz) and reactance (AX). IOS could be advantageous without certain limitations of spirometry.
Aim: To compare the accuracy of IOS vs spirometry in making the diagnosis of asthma and assessing age-related pulmonary changes in C-SCD.
Study design: Retrospective chart review.
Subject selection: Fifty-six C-SCD and thirty-six controls (asthmatics without SCD) followed at Penn State with PFTs obtained during the initial pulmonary evaluation.
Methodology: We grouped C-SCD into asthmatics and non-asthmatics based on pre-referral diagnosis and compared PFTs between two groups. Receiver operating characteristic (ROC) curve analyses and machine learning tools (XGBoost and artificial neural network) were used to rank the spirometry and IOS measures based on their ability to predict a diagnosis of asthma. Robust linear regression was used to analyze association among height/age with various PFT measures.
Results: Both ROC and XGBoost indicated that FEF25-75 %, forced expiratory volume in 1 second (FEV1)/forced vital capacity, and R5Hz(%) were the top three predictors for asthma diagnosis. R5Hz(%) and AX had superior bronchodilator response (BDR) than FEV1. IOS parameters had significant association with height/age in C-SCD (possibly due to the stiff lungs) but not in controls.
Conclusion: IOS had advantages over spirometry in C-SCD because it is feasible in early childhood, provides insights into the pulmonary mechanics, and is more sensitive to detect BDR.
Keywords: XGBoost; impulse oscillometry; machine learning; prediction model; pulmonary function testing; sickle cell disease; spirometry.
© 2019 Wiley Periodicals, Inc.