Normative reference equations of airway dynamics assessed by whole‐body plethysmography during spontaneous breathing evaluated in infants, children, and adults

Kraemer, Richard; Smith, Hans-Jürgen; Matthys, H

doi:10.14814/phy2.15027

Cited by 5 publications

(17 citation statements)

References 51 publications

(63 reference statements)

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“…Moreover, the integral embodies the specific, aerodynamic work of breathing (sWOB) at rest. 57 For the parameters of airway dynamics (sWOB, sG eff , and sR eff ), normative reference equations were used, recently worked out, 34 and provided in the Supplemental Material (Section 2 ). 29 , 34 , 47 , 70–81…”

Section: Methodsmentioning

confidence: 99%

“…All data were transformed to z-scores as a standardized measure of the positioning of an observed measurement in the distribution of the reference population, taking both between-subject and age-and growthrelated variability into account. [34][35][36][37][38]43,[66][67][68][69] To allow clinical interpretation of test results numerically independent from sex, age and ethnicity, measurement results were transferred into z-scores, as a standardized measure of the positioning of an observed measurement in the distribution of the reference population taking both between-subject as well as age-and growth-related variability into account. [34][35][36][37][38]43,[66][67][68][69] Using this procedure, the lower limit of normal (LLN), identical with z = −1.645, or the 5th percentile in the distribution of FEV 1 , FEV 1 /FVC ratio, FEF 25-75 , as well as the upper limit of normal (ULN), identical with +1.645 z-scores or the 95th percentile for sR eff , sR tot , sWOB, TLC pleth , FRC pleth , RV/TLC and DL CO , K CO were obtained, respectively, and transformed into z-scores according to standard prediction equations 45,70,71 (Figure 1).…”

Section: Pulmonary Function Proceduresmentioning

confidence: 99%

“…International Journal of Chronic Obstructive Pulmonary Disease 2022:17 2726 dynamics (sWOB, sG eff , and sR eff ), normative reference equations were used, recently worked out, 34 and provided in the Supplemental Material (Section 2). 29,34,47,[70][71][72][73][74][75][76][77][78][79][80][81] Statistical Approach, Data Analysis and Statistical Methods…”

Section: Dovepressmentioning

confidence: 99%

“…29–31 There is, however, up-coming work indicating that the major mathematical component of airway dynamics (sR eff and sG eff ) is sWOB, 32 , 33 from which normalized reference equations have recently been defined. 34 …”

Section: Introductionmentioning

confidence: 99%

“…[29][30][31] There is, however, up-coming work indicating that the major mathematical component of airway dynamics (sR eff and sG eff ) is sWOB, 32,33 from which normalized reference equations have recently been defined. 34 Therefore, the purpose of the present study was to address the complexity of chronic obstructive airway diseases physiologically, selecting specific target parameter values expressed in z-scores, [35][36][37][38] with proven discriminatory statistical power, and hence to present a multidimensional perception of a treatable trait strategy 39 within a new concept of precision. 39,40…”

Section: Introductionmentioning

confidence: 99%

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Functional Predictors Discriminating Asthma–COPD Overlap (ACO) from Chronic Obstructive Pulmonary Disease (COPD)

Kraemer¹,

Gardin²,

Smith³

et al. 2022

COPD

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Background A significant proportion of patients with obstructive lung disease have clinical and functional features of both asthma and chronic obstructive pulmonary disease (COPD), referred to as the asthma–COPD overlap (ACO). The distinction of these phenotypes, however, is not yet well-established due to the lack of defining clinical and/or functional criteria. The aim of our investigations was to assess the discriminating power of various lung function parameters on the assessment of ACO. Methods From databases of 4 pulmonary centers, a total of 540 patients (231 males, 309 females), including 372 patients with asthma, 77 patients with ACO and 91 patients with COPD, were retrospectively collected, and gradients among combinations of explanatory variables of spirometric (FEV 1 , FEV 1 /FVC, FEF 25-75 ), plethysmographic (sR eff , sG eff , the aerodynamic work of breathing at rest; sWOB), static lung volumes, including trapped gases and measurements of the carbon monoxide transfer (DL CO , K CO ) were explored using multiple factor analysis (MFA). The discriminating power of lung function parameters with respect to ACO was assessed using linear discriminant analysis (LDA). Results LDA revealed that parameters of airway dynamics (sWOB, sR eff , sG eff ) combined with parameters of static lung volumes such as functional residual capacity (FRC pleth ) and trapped gas at FRC (V TG FRC ) are valuable and potentially important tools discriminating between asthma, ACO and COPD. Moreover, sWOB significantly contributes to the diagnosis of obstructive airway diseases, independent from the state of pulmonary hyperinflation, whilst the diffusion capacity for carbon monoxide (DL CO ) significantly differentiates between the 3 diagnostic classes. Conclusion The complexity of COPD with its components of interaction and their heterogeneity, especially in discrimination from ACO, may well be differentiated if patients are explored by a whole set of target parameters evaluating, interactionally, flow limitation, airway dynamics, pulmonary hyperinflation, small airways dysfunction and gas exchange disturbances assessing specific functional deficits.

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Section: Methodsmentioning

confidence: 99%

Section: Pulmonary Function Proceduresmentioning

confidence: 99%

Section: Dovepressmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Functional Predictors Discriminating Asthma–COPD Overlap (ACO) from Chronic Obstructive Pulmonary Disease (COPD)

Kraemer¹,

Gardin²,

Smith³

et al. 2022

COPD

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Ventilation Mechanics

Farré

Navajas

2023

Semin Respir Crit Care Med

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A fundamental task of the respiratory system is to operate as a mechanical gas pump ensuring that fresh air gets in close contact with the blood circulating through the lung capillaries to achieve O2 and CO2 exchange. To ventilate the lungs, the respiratory muscles provide the pressure required to overcome the viscoelastic mechanical load of the respiratory system. From a mechanical viewpoint, the most relevant respiratory system properties are the resistance of the airways (R aw), and the compliance of the lung tissue (C L) and chest wall (C CW). Both airflow and lung volume changes in spontaneous breathing and mechanical ventilation are determined by applying the fundamental mechanical laws to the relationships between the pressures inside the respiratory system (at the airway opening, alveolar, pleural, and muscular) and R aw, C L, and C CW. These relationships also are the basis of the different methods available to measure respiratory mechanics during spontaneous and artificial ventilation. Whereas a simple mechanical model (R aw, C L, and C CW) describes the basic understanding of ventilation mechanics, more complex concepts (nonlinearity, inhomogeneous ventilation, or viscoelasticity) should be employed to better describe and measure ventilation mechanics in patients.

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Predicting parameters of airway dynamics generated from inspiratory and expiratory plethysmographic airway loops, differentiating subtypes of chronic obstructive diseases

Kraemer,

Smith,

Reinstaedtler

et al. 2024

BMJ Open Resp Res

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BackgroundThe plethysmographic shift volume–flow loop (sRaw-loop) measured during tidal breathing allows the determination of several lung function parameters such as the effective specific airway resistance (sReff), calculated from the ratio of the integral of the resistive aerodynamic specific work of breathing (sWOB) and the integral of the corresponding flow–volume loop. However, computing the inspiratory and expiratory areas of the sRaw-loop separately permits the determination of further parameters of airway dynamics. Therefore, we aimed to define the discriminating diagnostic power of the inspiratory and expiratory sWOB (sWOBin, sWOBex), as well as of the inspiratory and expiratory sReff(sReffINand sReffEX), for discriminating different functional phenotypes of chronic obstructive lung diseases.MethodsReference equations were obtained from measurement of different databases, incorporating 194 healthy subjects (35 children and 159 adults), and applied to a collective of 294 patients with chronic lung diseases (16 children with asthma, aged 6–16 years, and 278 adults, aged 17–92 years). For all measurements, the same type of plethysmograph was used (Jaeger Würzburg, Germany).ResultsBy multilinear modelling, reference equations of sWOBin, sWOBex, sReffINand sReffEXwere derived. Apart from anthropometric indices, additional parameters such as tidal volume (VT), the respiratory drive (P0.1), measured by means of a mouth occlusion pressure measurement 100 ms after inspiration and the mean inspiratory flow (VT/TI) were found to be informative. The statistical approach to define reference equations for parameters of airway dynamics reveals the interrelationship between covariants of the actual breathing pattern and the control of breathing.ConclusionsWe discovered that sWOBin, sWOBex, sReffINand sReffEXare new discriminating target parameters, that differentiate much better between chronic obstructive diseases and their subtypes, especially between chronic obstructive pulmonary disease (COPD) and asthma–COPD overlap (ACO), thus strengthening the concept of precision medicine.

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Normative reference equations of airway dynamics assessed by whole‐body plethysmography during spontaneous breathing evaluated in infants, children, and adults

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 5 publications

References 51 publications

Functional Predictors Discriminating Asthma–COPD Overlap (ACO) from Chronic Obstructive Pulmonary Disease (COPD)

Functional Predictors Discriminating Asthma–COPD Overlap (ACO) from Chronic Obstructive Pulmonary Disease (COPD)

Ventilation Mechanics

Predicting parameters of airway dynamics generated from inspiratory and expiratory plethysmographic airway loops, differentiating subtypes of chronic obstructive diseases

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