scholarly journals Area under the expiratory flow-volume curve: predicted values by regression and deep learning methods and recommendations for clinical practice

2021 ◽  
Vol 8 (1) ◽  
pp. e000925
Author(s):  
Octavian C Ioachimescu ◽  
José A Ramos ◽  
Michael Hoffman ◽  
James K Stoller
Keyword(s):  
Deep Learning ◽  
Clinical Practice ◽  
Learning Algorithm ◽  
Flow Volume ◽  
Additional Tool ◽  
Volume Curve ◽  
Simple Regression Model ◽  
Flow Volume Curve ◽  
Expiratory Flow ◽  
Simple Regression

BackgroundIn 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.MethodsWe 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.ResultsMedian/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.ConclusionsIn 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.

Dynamic mechanisms determine functional residual capacity in mice, Mus musculus

1979 ◽  
Vol 46 (5) ◽  
pp. 867-871 ◽  
Author(s):  
A. Vinegar ◽  
E. E. Sinnett ◽  
D. E. Leith
Keyword(s):  
Tidal Volume ◽  
Functional Residual Capacity ◽  
Flow Volume ◽  
Linear Portion ◽  
Volume Curve ◽  
Flow Volume Curve ◽  
Residual Capacity ◽  
Expiratory Flow ◽  
The Difference ◽  
Pentobarbital Sodium Anesthesia

Awake mice (22.6--32.6 g) were anesthetized intravenously during head-out body plethysmography. One minute after pentobarbital sodium anesthesia, tidal volume had fallen from 0.28 +/- 0.04 to 0.14 +/- 0.02 ml and frequency from 181 +/- 20 to 142 +/- 8. Functional residual capacity (FRC) decreased by 0.10 +/- 0.02 ml. Expiratory flow-volume curves were linear, highly repeatable, and submaximal over substantial portions of expiration in awake and anesthetized mice; and expiration was interrupted at substantial flows that abruptly fell to and crossed zero as inspiration interrupted relaxed expiration. FRC is maintained at a higher level in awake mice due to a higher tidal volume and frequency coupled with expiratory braking (persistent inspiratory muscle activity or increased glottal resistance). In anesthetized mice, the absence of braking, coupled with reductions in tidal volume and frequency and a prolonged expiratory period, leads to FRCs that approach relaxation volume (Vr). An equation in derived to express the difference between FRC and Vr in terms of the portion of tidal volume expired without braking, the slope of the linear portion of the expiratory flow-volume curve expressed as V/V, the time fraction of one respiratory cycle spent in unbraked expiration, and respiratory frequency.

scholarly journals Relationship Between Spontaneous Expiratory Flow-Volume Curve Pattern and Air-Flow Obstruction in Elderly COPD Patients

2013 ◽  
Vol 58 (10) ◽  
pp. 1643-1648 ◽  
Author(s):  
M. Nozoe ◽  
K. Mase ◽  
S. Murakami ◽  
M. Okada ◽  
T. Ogino ◽  
...  
Keyword(s):  
Air Flow ◽  
Flow Volume ◽  
Curve Pattern ◽  
Volume Curve ◽  
Copd Patients ◽  
Flow Volume Curve ◽  
Expiratory Flow

Simulation of the effects of mechanical nonhomogeneities on expiratory flow from human lungs

1990 ◽  
Vol 68 (6) ◽  
pp. 2550-2563 ◽  
Author(s):  
R. K. Lambert
Keyword(s):  
Fluid Mechanics ◽  
Left Lung ◽  
Flow Volume ◽  
Alveolar Pressure ◽  
Peripheral Airways ◽  
Volume Curve ◽  
Human Lungs ◽  
Flow Volume Curve ◽  
Expired Gas ◽  
Expiratory Flow

A computational model for expiration from lungs with mechanical nonhomogeneities was used to investigate the effect of such nonhomogeneities on the distribution of expiratory flow and the development of alveolar pressure differences between regions. The nonhomogeneities used were a modest constriction of the peripheral airways and a 50% difference in compliance between regions. The model contains only two mechanically different regions but allows these to be as grossly distributed as left lung-right lung or to be distributed as a set of identical pairs of parallel nonhomogeneous regions with flows from each merging in a specified bronchial generation. The site of flow merging had no effect on the flow-volume curve but had a significant effect on the development of alveolar pressure differences (delta PA). With the peripheral constriction, greater values of delta PA developed when flows were merged peripherally rather than centrally. The opposite was true in the case of a compliance nonhomogeneity. The delta PA values were smaller at submaximal flows. Plots of delta PA vs. lung volume were similar to those obtained experimentally. These results were interpreted in terms of the expression used for the fluid mechanics of the merging flows. delta PA was greater when the viscosity of the expired gas was increased or when its density was reduced. Partial forced expirations were shown to indicate the presence of mechanical nonhomogeneity.

scholarly journals Shape Analysis of the Maximal Expiratory Flow Volume Curve Among the Patients with Various Kind of Pulmonary Diseases

1982 ◽  
Vol 29 (1) ◽  
pp. 30-36
Author(s):  
Song Hyun Nam ◽  
Hyun Ha Park ◽  
Re Hwe Kim ◽  
Sung Koo Han ◽  
Ye Won Kim ◽  
...  
Keyword(s):  
Shape Analysis ◽  
Pulmonary Diseases ◽  
Flow Volume ◽  
Maximal Expiratory Flow ◽  
Volume Curve ◽  
Flow Volume Curve ◽  
Expiratory Flow

scholarly journals Longitudinal Examination of the Shape of the Maximum Expiratory Flow‐Volume Curve in Young Adults Following SARS‐CoV‐2 Infection

2021 ◽  
Vol 35 (S1) ◽  
Author(s):  
Jonathon Stickford ◽  
Marc Augenreich ◽  
Valesha Province ◽  
Nina Stute ◽  
Abigail Stickford ◽  
...  
Keyword(s):  
Young Adults ◽  
Flow Volume ◽  
Volume Curve ◽  
Maximum Expiratory Flow ◽  
Flow Volume Curve ◽  
Expiratory Flow ◽  
Longitudinal Examination

The Shape of the Maximum Expiratory Flow Volume Curve

CHEST Journal ◽  
1988 ◽  
Vol 94 (4) ◽  
pp. 799-806 ◽  
Author(s):  
Mary C. Kapp ◽  
E.Neil Schachter ◽  
Gerald J. Beck ◽  
Lucinda R. Maunder ◽  
Theodore J. Witek
Keyword(s):  
Flow Volume ◽  
Volume Curve ◽  
Maximum Expiratory Flow ◽  
Flow Volume Curve ◽  
Expiratory Flow

scholarly journals Deep learning algorithm helps to standardise ATS/ERS spirometric acceptability and usability criteria

2020 ◽  
pp. 2000603 ◽  
Author(s):  
Nilakash Das ◽  
Kenneth Verstraete ◽  
Sanja Stanojevic ◽  
Marko Topalovic ◽  
Jean-Marie Aerts ◽  
...  
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Learning Algorithm ◽  
High Sensitivity ◽  
Current Approach ◽  
Deep Learning Algorithm ◽  
Volume Curve ◽  
Flow Volume Curve ◽  
Shapley Values ◽  
Quality Control Criteria

RationaleWhile ATS/ERS quality control criteria for spirometry include several quantitative limits, it also requires manual visual inspection. The current approach is time consuming and leads to high inter-technician variability. We propose a deep learning approach called convolutional neural network (CNN), to standardise spirometric manoeuvre acceptability and usability.Methods and methodsIn 36 873 curves from the national health and nutritional examination survey (NHANES) USA 2011–12, technicians labelled 54% of curves as meeting ATS/ERS 2005 acceptability criteria with satisfactory start and end of test but identified 93% of curves with a usable FEV1. We processed raw data into images of maximal expiratory flow-volume curve (MEFVC), calculated ATS/ERS quantifiable criteria, and developed CNNs to determine manoeuvre acceptability and usability on 90% of the curves. The models were tested on the remaining 10% of curves. We calculated Shapley values to interpret the models.ResultsIn the test set (N=3738), CNN showed an accuracy of 87% for acceptability and 92% for usability, with the latter demonstrating a high sensitivity (92%) and specificity (96%). They were significantly superior (p<0.0001) to ATS/ERS quantifiable rule-based models. Shapley interpretation revealed MEFVC<1 s (MEFVC pattern within first second of exhalation) and plateau in volume-time were most important in determining acceptability, while MEFVC<1 s entirely determined usability.ConclusionThe CNNs identified relevant attributes in spirometric curves to standardise ATS/ERS manoeuvre acceptability and usability recommendations, and further provides individual manoeuvre feedback. Our algorithm combines the visual experience of skilled technicians and ATS/ERS quantitative rules in automating the critical phase of spirometry quality control.

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