Area under the expiratory flow-volume curve: predicted values by artificial neural networks

Abstract Area under expiratory flow-volume curve (AEX) has been proposed recently to be a useful spirometric tool for assessing ventilatory patterns and impairment severity. We derive here normative reference values for AEX, based on age, gender, race, height and weight, and by using artificial neur...

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Autores principales:	Octavian C. Ioachimescu, James K. Stoller, Francisco Garcia-Rio
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/623f35e21f044b13a7a349d9d4de0e95
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spelling	oai:doaj.org-article:623f35e21f044b13a7a349d9d4de0e952021-12-02T18:37:07ZArea under the expiratory flow-volume curve: predicted values by artificial neural networks10.1038/s41598-020-73925-02045-2322https://doaj.org/article/623f35e21f044b13a7a349d9d4de0e952020-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-73925-0https://doaj.org/toc/2045-2322Abstract Area under expiratory flow-volume curve (AEX) has been proposed recently to be a useful spirometric tool for assessing ventilatory patterns and impairment severity. We derive here normative reference values for AEX, based on age, gender, race, height and weight, and by using artificial neural network (ANN) algorithms. We analyzed 3567 normal spirometry tests with available AEX values, performed on subjects from two countries (United States and Spain). Regular linear or optimized regression and ANN models were built using traditional predictors of lung function. The ANN-based models outperformed the de novo regression-based equations for AEXpredicted and AEX z scores using race, gender, age, height and weight as predictor factors. We compared these reference values with previously developed equations for AEX (by gender and race), and found that the ANN models led to the most accurate predictions. When we compared the performance of ANN-based models in derivation/training, internal validation/testing, and external validation random groups, we found that the models based on pooling samples from various geographic areas outperformed the other models (in both central tendency and dispersion of the residuals, ameliorating any cohort effects). In a geographically diverse cohort of subjects with normal spirometry, we computed by both regression and ANN models several predicted equations and z scores for AEX, an alternative measurement of respiratory function. We found that the dynamic nature of the ANN allows for continuous improvement of the predictive models’ performance, thus promising that the AEX could become an essential tool in assessing respiratory impairment.Octavian C. IoachimescuJames K. StollerFrancisco Garcia-RioNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Octavian C. Ioachimescu James K. Stoller Francisco Garcia-Rio Area under the expiratory flow-volume curve: predicted values by artificial neural networks
description	Abstract Area under expiratory flow-volume curve (AEX) has been proposed recently to be a useful spirometric tool for assessing ventilatory patterns and impairment severity. We derive here normative reference values for AEX, based on age, gender, race, height and weight, and by using artificial neural network (ANN) algorithms. We analyzed 3567 normal spirometry tests with available AEX values, performed on subjects from two countries (United States and Spain). Regular linear or optimized regression and ANN models were built using traditional predictors of lung function. The ANN-based models outperformed the de novo regression-based equations for AEXpredicted and AEX z scores using race, gender, age, height and weight as predictor factors. We compared these reference values with previously developed equations for AEX (by gender and race), and found that the ANN models led to the most accurate predictions. When we compared the performance of ANN-based models in derivation/training, internal validation/testing, and external validation random groups, we found that the models based on pooling samples from various geographic areas outperformed the other models (in both central tendency and dispersion of the residuals, ameliorating any cohort effects). In a geographically diverse cohort of subjects with normal spirometry, we computed by both regression and ANN models several predicted equations and z scores for AEX, an alternative measurement of respiratory function. We found that the dynamic nature of the ANN allows for continuous improvement of the predictive models’ performance, thus promising that the AEX could become an essential tool in assessing respiratory impairment.
format	article
author	Octavian C. Ioachimescu James K. Stoller Francisco Garcia-Rio
author_facet	Octavian C. Ioachimescu James K. Stoller Francisco Garcia-Rio
author_sort	Octavian C. Ioachimescu
title	Area under the expiratory flow-volume curve: predicted values by artificial neural networks
title_short	Area under the expiratory flow-volume curve: predicted values by artificial neural networks
title_full	Area under the expiratory flow-volume curve: predicted values by artificial neural networks
title_fullStr	Area under the expiratory flow-volume curve: predicted values by artificial neural networks
title_full_unstemmed	Area under the expiratory flow-volume curve: predicted values by artificial neural networks
title_sort	area under the expiratory flow-volume curve: predicted values by artificial neural networks
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/623f35e21f044b13a7a349d9d4de0e95
work_keys_str_mv	AT octaviancioachimescu areaundertheexpiratoryflowvolumecurvepredictedvaluesbyartificialneuralnetworks AT jameskstoller areaundertheexpiratoryflowvolumecurvepredictedvaluesbyartificialneuralnetworks AT franciscogarciario areaundertheexpiratoryflowvolumecurvepredictedvaluesbyartificialneuralnetworks
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Area under the expiratory flow-volume curve: predicted values by artificial neural networks

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