An Alternative Spirometric Measurement Area under the Expiratory Flow-Volume Curve

• Type of Material: Article
• Authors: Octavian Ioachimescu, Emory University; James K. Stoller, Cleveland Clinic
• Language: English
• Date: 2020-05-01
• Publisher: AMER THORACIC SOC
• Publication Version: Final Published Version
• Copyright Statement: © 2020 by the American Thoracic Society
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1513/AnnalsATS.201908-613OC
• Title of Journal or Parent Work: ANNALS OF THE AMERICAN THORACIC SOCIETY
• Volume: 17
• Issue: 5
• Start Page: 582
• End Page: 588
• Keywords: spirometry; Respiratory System; Science & Technology; neural networks; PARAMETER; Life Sciences & Biomedicine; artificial intelligence; STANDARDIZATION; area under the flow-volume curve; LUNG-VOLUMES; machine learning; REFERENCE VALUES; CHILDREN; LOOP
• Research Categories: Biology, Neuroscience; Health Sciences, Medicine and Surgery

Abstract

Rationale: Interpretation of spirometry is influenced by inherent limitations and by the normal or predicted reference values used. For example, traditional spirometric parameters such as “distal” airflows do not provide sufficient differentiating capacity, especially for mixed patterns or small airway disease. Objectives: We assessed the utility of an alternative spirometric parameter (area under the expiratory flow–volume curve [AEX]) in differentiating between normal, obstruction, restriction, and mixed patterns, as well as in severity stratification of traditional functional impairments. Methods: We analyzed 15,308 spirometry tests in subjects who had same-day lung volume assessments in a pulmonary function laboratory. Using Global Lung Initiative predicted values and standard criteria for pulmonary function impairment, we assessed the diagnostic performance of AEX in best-split partition and artificial neural network models. Results: The average square root AEX values were 3.32, 1.81, 2.30, and 1.64 L$s20.5 in normal, obstruction, restriction, and mixed patterns, respectively. As such, in combination with traditional spirometric measurements, the square root of AEX differentiated well between normal, obstruction, restriction, and mixed defects. Using forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and FEV1/FVC z-scores plus the square root of AEX in a machine learning algorithm, diagnostic categorization of ventilatory impairments was accomplished with very low rates of misclassification (,9%). Especially for mixed ventilatory patterns, the neural network model performed best in improving the rates of diagnostic misclassification. Conclusions: Using a novel approach to lung function assessment in combination with traditional spirometric measurements, AEX differentiates well between normal, obstruction, restriction and mixed impairments, potentially obviating the need for more complex lung volume-based determinations.