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      An Alternative Spirometric Measurement. Area under the Expiratory Flow–Volume Curve

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          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⋅s −0.5 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 (FEV 1), forced vital capacity (FVC), and FEV 1/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.

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          Most cited references16

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          Lung function testing: selection of reference values and interpretative strategies. American Thoracic Society.

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            Lung volumes in healthy nonsmoking adults.

            Total lung capacity (TLC), functional residual capacity, residual volume, and corresponding 95% confidence intervals were measured in 245 healthy nonsmoking person (122 women, 123 men) using a single-breath helium technique. Prediction equations for lung volumes were generated by multiple linear regression. The resultant equations are similar to previously published equations using multiple-breath gas equilibration techniques. Measured 95% confidence intervals can be closely approximated by using two times the standard error of the estimate for each equation, but cannot be approximated by using +/- 20% of the predicted value. Radiographic TLC was not significantly different from the helium dilution TLC.
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              Measurement of lung volumes by plethysmography.

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                Author and article information

                Journal
                Ann Am Thorac Soc
                Ann Am Thorac Soc
                AnnalsATS
                Annals of the American Thoracic Society
                American Thoracic Society
                2329-6933
                2325-6621
                May 2020
                May 2020
                May 2020
                : 17
                : 5
                : 582-588
                Affiliations
                [ 1 ]Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, School of Medicine, Emory University, Atlanta VA Sleep Medicine Center, Atlanta, Georgia; and
                [ 2 ]Respiratory and Education Institutes, Cleveland Clinic, Cleveland, Ohio
                Author notes
                Correspondence and requests for reprints should be addressed to Octavian C. Ioachimescu, M.D., Ph.D., Atlanta VA Sleep Medicine Center, 250 North Arcadia Avenue, Atlanta, GA 30030. E-mail: oioac@ 123456yahoo.com .
                Author information
                http://orcid.org/0000-0001-9047-6894
                Article
                201908-613OC
                10.1513/AnnalsATS.201908-613OC
                7193817
                31899663
                1c70dc4d-519a-4ca0-b8de-a1252a35bbfe
                Copyright © 2020 by the American Thoracic Society

                This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 ( http://creativecommons.org/licenses/by-nc-nd/4.0/). For commercial usage and reprints, please contact Diane Gern ( dgern@ 123456thoracic.org ).

                History
                : 15 August 2019
                : 03 January 2020
                Page count
                Figures: 5, Tables: 1, Pages: 7
                Categories
                Original Research
                Adult Pulmonary

                spirometry,area under the flow–volume curve,neural networks,machine learning,artificial intelligence

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