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Ioachimescu OC, Stoller JK. Area under the inspiratory flow-volume curve (AIN): Proposed normative values. PLoS One 2024; 19:e0307966. [PMID: 39088417 PMCID: PMC11293737 DOI: 10.1371/journal.pone.0307966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 07/15/2024] [Indexed: 08/03/2024] Open
Abstract
RATIONALE Area under expiratory flow-volume curve (AEX) has been shown to be a valuable functional measurement in respiratory physiology. Area under inspiratory flow-volume loop (AIN) also shows promise in characterizing upper and/or lower airflow obstruction. OBJECTIVES we aimed here to develop normative reference values for AIN, able to ascertain deviations from normal. METHODS We analyzed AIN in 4,980 spirometry tests recorded in non-smoking, healthy individuals in the Pulmonary Function Testing Laboratory. RESULTS The mean (95% confidence interval, CI), standard deviation and median (25th-75th interquartile range) AIN were 16.05 (15.79-16.31), 9.08 and 14.72 (9.12-21.42) L2·sec-1, respectively. The mean (95% CI) and standard deviation of the best-trial measurements for square root of AIN (Sqrt AIN) were 3.84 (3.81-3.87) and 1.14; 4.15 (4.12-4.18) and 1.03 in men, and 2.68 (2.63-2.72) and 0.72 L·sec-1/2 in women. The mean (standard deviation) of pre- and post-bronchodilator Sqrt AIN were 3.71 (1.17) and 3.81 (1.19) L·sec-1/2, respectively. The mean (95% CI), standard deviation and lowest 5th percentile (lower limit of normal, LLN) of Sqrt AIN/Sqrt AEX (%) were 101.3 (100.82-101.88), 18.7, and 71.8%; stratified by gender, it was 102.2 (101.6-102.8), 18.6, and 72.8% in men, and 98 (96.9-99.2), 18.8, and 68.6% in women, respectively. CONCLUSIONS The availability of area under the inspiratory flow-volume curve (AIN) and the derived indices offers a promising opportunity to assess upper airway disease (e.g., involvement of larynx, trachea or major bronchi), especially because some of these measurements appear to be independent of age, race, height, and weight.
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Affiliation(s)
- Octavian C. Ioachimescu
- Professor of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine and Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
- Adjunct Professor of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia, United States of America
- Staff Physician, Clement J. Zablocki Veteran Affairs Medical Center, Milwaukee, Wisconsin, United States of America
| | - James K. Stoller
- Jean Wall Bennett Professor of Medicine and Chair, Education Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
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Maldonado-Franco A, Giraldo-Cadavid LF, Tuta-Quintero E, Bastidas Goyes AR, Botero-Rosas DA. The Challenges of Spirometric Diagnosis of COPD. Can Respir J 2023; 2023:6991493. [PMID: 37808623 PMCID: PMC10558269 DOI: 10.1155/2023/6991493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/09/2023] [Accepted: 03/28/2023] [Indexed: 10/10/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is one of the top causes of morbidity and mortality worldwide. Although for many years its accurate diagnosis has been a focus of intense research, it is still challenging. Due to its simplicity, portability, and low cost, spirometry has been established as the main tool to detect this condition, but its flawed performance makes it an imperfect COPD diagnosis gold standard. This review aims to provide an up-to-date literature overview of recent studies regarding COPD diagnosis; we seek to identify their limitations and establish perspectives for spirometric diagnosis of COPD in the XXI century by combining deep clinical knowledge of the disease with advanced computer analysis techniques.
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Affiliation(s)
| | - Luis F. Giraldo-Cadavid
- Departments of Epidemiology and Internal Medicine, School of Medicine, Universidad de La Sabana, Chía, Colombia
- Director of Interventional Pulmonology Service, Fundación Neumológica Colombiana, Bogotá, Colombia
| | - Eduardo Tuta-Quintero
- Candidate for Master's Degree in Epidemiology, Universidad de La Sabana, Chía, Colombia
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Ioachimescu OC, McCarthy K, Stoller JK. Area under the expiratory flow-volume curve: normative values in the National Health and Nutrition Survey (NHANES) study. J Investig Med 2022; 70:1247-1257. [PMID: 35190486 PMCID: PMC9240334 DOI: 10.1136/jim-2021-002057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/27/2022] [Indexed: 11/17/2022]
Abstract
The area under the expiratory flow-volume (AEX-FV) loop has been evaluated before as a spirometric tool for assessing respiratory functional impairment. We computed the AEX-FV curves in spirometry tests performed on 20,313 participants in the National Health and Nutrition Examination Survey (NHANES) study. We analyzed 108,939 spirometry tests performed between 2007 and 2012 (5964 children; 14,349 adults). In these tests, we computed the three areas from existing NHANES raw data on instantaneous expiratory flows measured at 0.01 s intervals. Mean best-trial measurements for AEX-FV were 3.4 in boys, 2.8 in girls, 11.8 in men and 7.7 L2/s in women. We characterized indices of central tendency and dispersion of the measurements (eg, means and fifth percentiles—lower limits of normal) by age group (children vs adults), gender, race or ethnicity group and effort grading. Simple regression equations using logarithmic transformations of the above areas and using age, gender and height as inputs provided good predictive ability for the variable AEX-FV. Regular, digital spirometry could and should make available to clinicians and researchers the area under the curves for flow versus volume graph, providing additional tools in our armamentarium to evaluate ventilatory impairments and patterns, and possibly respiratory disability.
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Affiliation(s)
- Octavian C Ioachimescu
- Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, Georgia, USA
- Medicine, Sleep Medicine Section, Atlanta VA Health Care System, Decatur, Georgia, USA
| | - Kevin McCarthy
- Retired Manager, Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - James K Stoller
- Respiratory and Education Institutes, Cleveland Clinic, Cleveland, Ohio, USA
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Ioachimescu OC, Ramos JA, Hoffman M, Stoller JK. Area under the expiratory flow-volume curve: predicted values by regression and deep learning methods and recommendations for clinical practice. BMJ Open Respir Res 2021; 8:8/1/e000925. [PMID: 33926960 PMCID: PMC8094381 DOI: 10.1136/bmjresp-2021-000925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/19/2021] [Accepted: 04/20/2021] [Indexed: 11/05/2022] Open
Abstract
Background In spirometry, the area under expiratory flow-volume curve (AEX-FV) was found to perform well in diagnosing and stratifying physiologic impairments, potentially lessening the need for complex lung volume testing. Expanding on prior work, this study assesses the accuracy and the utility of several models of estimating AEX-FV based on forced vital capacity (FVC) and several instantaneous flows. These models could be incorporated in regular spirometry reports, especially when actual AEX-FV measurements are not available. Methods We analysed 4845 normal spirometry tests, performed on 3634 non-smoking subjects without known respiratory disease or complaints. Estimated AEX-FV was computed based on FVC and several flows: peak expiratory flow, isovolumic forced expiratory flow at 25%, 50% and 75% of FVC (FEF25, FEF50 and FEF75, respectively). The estimations were based on simple regression with and without interactions, by optimised regression models and by a deep learning algorithm that predicted the response surface of AEX-FV without interference from any predictor collinearities or normality assumption violations. Results Median/IQR of actual square root of AEX-FV was 3.8/3.1–4.5 L2/s. The per cent of variance (R2) explained by the models selected was very high (>0.990), the effect of collinearities was negligible and the use of deep learning algorithms likely unnecessary for regular or routine pulmonary function testing laboratory usage. Conclusions In the absence of actual AEX-FV, a simple regression model without interactions between predictors or use of optimisation techniques can provide a reasonable estimation for clinical practice, thus making AEX-FV an easily available additional tool for interpreting spirometry.
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Affiliation(s)
- Octavian C Ioachimescu
- School of Medicine, Department of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, Georgia, USA .,Department of Medicine, Sleep Medicine Section, Atlanta Veteran Affairs Healthcare System, Decatur, Georgia, USA
| | - José A Ramos
- Cleveland Clinic, Respiratory Institute, Cleveland, Ohio, USA
| | - Michael Hoffman
- Cleveland Clinic, Respiratory Institute, Cleveland, Ohio, USA
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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 (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.
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Ioachimescu OC, Stoller JK, Garcia-Rio F. Area under the expiratory flow-volume curve: predicted values by artificial neural networks. Sci Rep 2020; 10:16624. [PMID: 33024243 PMCID: PMC7538954 DOI: 10.1038/s41598-020-73925-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 09/23/2020] [Indexed: 01/08/2023] Open
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.
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Affiliation(s)
- Octavian C Ioachimescu
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, School of Medicine, Emory University, Atlanta VA Sleep Medicine Center, 250 N Arcadia Ave, Decatur, GA, 30030, USA.
| | - James K Stoller
- Jean Wall Bennett Professor of Medicine, Chair-Education Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH, USA
| | - Francisco Garcia-Rio
- Servicio de Neumología, Hospital Universitario La Paz, IdiPAZ-Departamento de Medicina, Universidad Autónoma de Madrid-Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), Madrid, Spain
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Area Under the Expiratory Flow-Volume Curve (AEX): Assessing Bronchodilator Responsiveness. Lung 2020; 198:471-480. [PMID: 32211978 PMCID: PMC7242267 DOI: 10.1007/s00408-020-00345-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 03/11/2020] [Indexed: 11/30/2022]
Abstract
Background Area under expiratory flow–volume curve (AEX) is a useful spirometric tool in stratifying respiratory impairment. The AEX approximations based on isovolumic flows can be used with reasonable accuracy when AEX is unavailable. We assessed here pre- to post-bronchodilator (BD) variability of AEX4 as a functional assessment tool for lung disorders. Methods The BD response was assessed in 4330 subjects by changes in FEV1, FVC, and AEX4, which were derived from FVC, peak expiratory flow, and forced expiratory flow at 25%, 50%, and 75% FVC. Newly proposed BD response categories (negative, minimal, mild, moderate and marked) have been investigated in addition to standard criteria. Results Using standard BD criteria, 24% of subjects had a positive response. Using the new BD response categories, only 23% of subjects had a negative response; 45% minimal, 18% mild, 9% moderate, and 5% had a marked BD response. Mean percent change of the square root AEX4 was 0.3% and 14.3% in the standard BD-negative and BD-positive response groups, respectively. In the new BD response categories of negative, minimal, mild, moderate, and marked, mean percent change of square root AEX4 was − 8.2%, 2.9%, 9.2%, 15.0%, and 24.8%, respectively. Conclusions Mean pre- to post-BD variability of AEX4 was < 6% and stratified well between newly proposed categories of BD response (negative, minimal, mild, moderate and marked). We suggest that AEX4 (AEX) could become a useful measurement for stratifying dysfunction in obstructive lung disease and invite further investigation into indications for using bronchodilator agents or disease-modifying, anti-inflammatory therapies.
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Ioachimescu OC, Stoller JK. Assessing small airway disease in GLI versus NHANES III based spirometry using area under the expiratory flow-volume curve. BMJ Open Respir Res 2019; 6:e000511. [PMID: 31803477 PMCID: PMC6890381 DOI: 10.1136/bmjresp-2019-000511] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 10/31/2019] [Accepted: 11/01/2019] [Indexed: 11/08/2022] Open
Abstract
Background Spirometry interpretation is influenced by the predictive equations defining lower limit of normal (LLN), while 'distal' expiratory flows such as forced expiratory flow at 50% FVC (FEF50) are important functional parameters for diagnosing small airway disease (SAD). Area under expiratory flow-volume curve (AEX) or its approximations have been proposed as supplemental spirometric assessment tools. We compare here the performance of AEX in differentiating between normal, obstruction, restriction, mixed defects and SAD, as defined by Global Lung Initiative (GLI) or National Health and Nutrition Examination Survey (NHANES) III reference values, and using various predictive equations for FEF50. Methods We analysed 15 308 spirometry-lung volume tests. Using GLI versus NHANES III LLNs, and diagnosing SAD by the eight most common equation sets for forced expiratory flow at 50% of vital capacity lower limits of normal (FEF50 LLN), we assessed the degree of diagnostic concordance and the ability of AEX to differentiate between various definition-dependent patterns. Results Concordance rates between NHANES III and GLI-based classifications were 93.7%, 78.6%, 86.8%, 88.0%, 93.8% and 98.8% in those without, with mild, moderate, moderately severe, severe and very severe obstruction, respectively (agreement coefficient 0.81 (0.80-0.82)). The prevalence of SAD was 0.6%-6.9% of the cohort, depending on the definition used. The AEX differentiated well between normal, obstruction, restriction, mixed pattern and SAD, as defined by most equations. Conclusions If the SAD diagnosis is established by using mean FEF50 LLN or a set number of predictive equations, AEX is able to differentiate well between various spirometric patterns. Using the most common predictive equations (NHANES III and GLI), the diagnostic concordance for functional type and obstruction severity is high.
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Affiliation(s)
- Octavian C Ioachimescu
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA
- Section of Sleep Medicine, Atlanta VAMC, Atlanta, Georgia, USA
| | - James K Stoller
- Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, USA
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