Pulmonary physiology: Future directions for lung function testing in COPD

The total-breath method yields higher values of DLCO and TLC than the conventional method

BACKGROUND

The 2017 ATS/ERS technical standard for measuring the single-breath diffusing capacity (DLCO) proposed the "rapid-gas-analyzer" (RGA) or, equivalently, "total-breath" (TB) method for the determination of total lung capacity (TLC). In this study, we compared DLCO and TLC values estimated using the TB and conventional method, and how estimated TLC using these two methods compared to that determined by body plethysmography.

METHOD

A total of 95 people with COPD (GOLD grades 1-4) and 23 healthy subjects were studied using the EasyOne Pro (ndd Medical Technologies, Switzerland) and Master Screen Body (Vyaire Medical, Höchberg, Germany).

RESULTS

On average the TB method resulted in higher values of DLCO (mean ± SD Δ = 0.469 ± 0.267; 95%CI: 0.420; 0.517 mmol*min-1*kPa-1) and TLC (Δ = 0.495 ± 0.371; 95%CI: 0.427; 0.562 L) compared with the conventional method. In healthy subjects the ratio between TB and conventional DLCO was close to one. TLC estimated using both methods was lower than that determined by plethysmography. The difference was smaller for the TB method (Δ = 1.064 ± 0.740; 95%CI: 0.929; 1.199 L) compared with the conventional method (Δ = 1.558 ± 0.940; 95%CI: 1.387; 1.739 L). TLC from body plethysmography could be estimated as a function of TB TLC and FEV1 Z-Score with an accuracy (normalized root mean square difference) of 9.1%.

CONCLUSION

The total-breath method yielded higher values of DLCO and TLC than the conventional analysis, especially in subjects with COPD. TLC from the total-breath method can also be used to estimate plethysmographic TLC with better accuracy than the conventional method. The study is registered under clinicaltrial.gov NCT04531293.

The Effectiveness of Home-Based Inspiratory Muscle Training on Small Airway Function and Disease-Associated Symptoms in Patients with Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is characterized by persistent airflow limitations, occurring mainly in the small airways. Weakness in the respiratory muscles contributes to dyspnea and a decreased exercise capacity in COPD patients. This study aimed to investigate the effectiveness of home-based inspiratory muscle training (IMT) on small airway function and symptoms in COPD patients. This research adopted a non-randomized controlled-study quasi-experimental design. The IMT program consisted of two 15 min sessions·d-1, 5 d·wk-1, with 40% of the maximal inspiratory pressure (PImax) on each participant's assessment results and lasted for 12 weeks. Small airway function was assessed using plethysmography at baseline and after 12 weeks. The modified British Medical Research Council (mMRC), COPD assessment test (CAT), PImax, and 6 min walking distance (6MWD) were recorded at baseline as well as four, eight, and twelve weeks. Twenty-three participants with at least moderate COPD were enrolled in IMT (n = 16) or in the control group (n = 7) in this study. The study participants were mostly male (82.6%), and the average age was 68.29 ± 10.87 years, with a mean body mass index (BMI) of 23.54 ± 4.79. After 12 weeks, the ratios of the first second of forced expiration to the forced vital capacity (FEV1/FVC%) (B coefficient [95% Wald confidence interval] of 5.21 [0.46 to 9.96], p = 0.032), forced expiratory flow (FEF25-75%) (0.20 [0.04 to 0.35] L/s, p = 0.012), and FEF50% (0.26 [0.08 to 0.43] L/s, p = 0.004) in the IMT group were significantly better than in the control group. The IMT group showed significantly lower CAT scores at week 8 (-5.50 [-10.31 to -0.695] scores, p = 0.025) than the control group. The mMRC grade, CAT score, PImax, and 6MWD were significantly improved compared to their values at baseline in the IMT group. Home-based IMT effectively improved post-bronchodilator small airway function and disease-associated symptoms in COPD patients.

The landscape of aging

Aging is characterized by a progressive deterioration of physiological integrity, leading to impaired functional ability and ultimately increased susceptibility to death. It is a major risk factor for chronic human diseases, including cardiovascular disease, diabetes, neurological degeneration, and cancer. Therefore, the growing emphasis on “healthy aging” raises a series of important questions in life and social sciences. In recent years, there has been unprecedented progress in aging research, particularly the discovery that the rate of aging is at least partly controlled by evolutionarily conserved genetic pathways and biological processes. In an attempt to bring full-fledged understanding to both the aging process and age-associated diseases, we review the descriptive, conceptual, and interventive aspects of the landscape of aging composed of a number of layers at the cellular, tissue, organ, organ system, and organismal levels.

Discordant Spirometry and Impulse Oscillometry Assessments in the Diagnosis of Small Airway Dysfunction

Background and objective: Spirometry is commonly used to assess small airway dysfunction (SAD). Impulse oscillometry (IOS) can complement spirometry. However, discordant spirometry and IOS in the diagnosis of SAD were not uncommon. We examined the association between spirometry and IOS within a large cohort of subjects to identify variables that may explain discordant spirometry and IOS findings. Methods: 1,836 subjects from the ECOPD cohort underwent questionnaires, symptom scores, spirometry, and IOS, and 1,318 subjects were examined by CT. We assessed SAD with R₅-R₂₀ > the upper limit of normal (ULN) by IOS and two of the three spirometry indexes (maximal mid-expiratory flow (MMEF), forced expiratory flow (FEF)_50%, and FEF_75%) < 65% predicted. Multivariate regression analysis was used to analyze factors associated with SAD diagnosed by only spirometry but not IOS (spirometry-only SAD) and only IOS but not spirometry (IOS-only SAD), and line regression was used to assess CT imaging differences. Results: There was a slight agreement between spirometry and IOS in the diagnosis of SAD (kappa 0.322, p < 0.001). Smoking status, phlegm, drug treatment, and family history of respiratory disease were factors leading to spirometry-only SAD. Spirometry-only SAD had more severe emphysema and gas-trapping than IOS-only SAD in abnormal lung function. However, in normal lung function subjects, there was no statistical difference in emphysema and gas-trapping between discordant groups. The number of IOS-only SAD was nearly twice than that of spirometry. Conclusion: IOS may be more sensitive than spirometry in the diagnosis of SAD in normal lung function subjects. But in patients with abnormal lung function, spirometry may be more sensitive than IOS to detect SAD patients with clinical symptoms and CT lesions.

Respiratory Oscillometry in Chronic Obstructive Pulmonary Disease: Association with Functional Capacity as Evaluated by Adl Glittre Test and Hand Grip Strength Test

Purpose

Respiratory oscillometry has emerged as a powerful method for detecting respiratory abnormalities in COPD. However, this method has not been widely introduced into clinical practice. This limitation arises, at least in part, because the clinical meaning of the oscillometric parameters is not clear. In this paper, we evaluated the association of oscillometry with functional capacity and its ability to predict abnormal functional capacity in COPD.

Patients and Methods

This cross-sectional study investigated a control group formed by 30 healthy subjects and 30 outpatients with COPD. The subjects were classified by the Glittre‑ADL test and handgrip strength according to the functional capacity.

Results

This study has shown initially that subjects with abnormal functional capacity had a higher value for resistance (p < 0.05), reactance area (Ax, p < 0.01), impedance modulus (Z4, p < 0.05), and reduced dynamic compliance (Cdyn, p < 0.05) when compared with subjects with normal functional capacity. This resulted in significant and consistent correlations among resistive oscillometric parameters (R=−0.43), Cdyn (R=−0.40), Ax (R = 0.42), and Z4 (R = 0.41) with exercise performance. Additionally, the effects of exercise limitation in COPD were adequately predicted, as evaluated by the area under the curve (AUC) obtained by receiver operating characteristic analysis. The best parameters for this task were R4-R20 (AUC = 0.779) and Ax (AUC = 0.752).

Conclusion

Respiratory oscillometry provides information related to functional capacity in COPD. This method is also able to predict low exercise tolerance in these patients. These findings elucidate the physiological and clinical meaning of the oscillometric parameters, improving the interpretation of these parameters in COPD patients.

Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis

INTRODUCTION

The use of machine learning (ML) methods would improve the diagnosis of respiratory changes in systemic sclerosis (SSc). This paper evaluates the performance of several ML algorithms associated with the respiratory oscillometry analysis to aid in the diagnostic of respiratory changes in SSc. We also find out the best configuration for this task.

METHODS

Oscillometric and spirometric exams were performed in 82 individuals, including controls (n = 30) and patients with systemic sclerosis with normal (n = 22) and abnormal (n = 30) spirometry. Multiple instance classifiers and different supervised machine learning techniques were investigated, including k-Nearest Neighbors (KNN), Random Forests (RF), AdaBoost with decision trees (ADAB), and Extreme Gradient Boosting (XGB).

RESULTS AND DISCUSSION

The first experiment of this study showed that the best oscillometric parameter (BOP) was dynamic compliance, which provided moderate accuracy (AUC = 0.77) in the scenario control group versus patients with sclerosis and normal spirometry (CGvsPSNS). In the scenario control group versus patients with sclerosis and altered spirometry (CGvsPSAS), the BOP obtained high accuracy (AUC = 0.94). In the second experiment, the ML techniques were used. In CGvsPSNS, KNN achieved the best result (AUC = 0.90), significantly improving the accuracy in comparison with the BOP (p < 0.01), while in CGvsPSAS, RF obtained the best results (AUC = 0.97), also significantly improving the diagnostic accuracy (p < 0.05). In the third, fourth, fifth, and sixth experiments, different feature selection techniques allowed us to spot the best oscillometric parameters. They resulted in a small increase in diagnostic accuracy in CGvsPSNS (respectively, 0.87, 0.86, 0.82, and 0.84), while in the CGvsPSAS, the best classifier's performance remained the same (AUC = 0.97).

CONCLUSIONS

Oscillometric principles combined with machine learning algorithms provide a new method for diagnosing respiratory changes in patients with systemic sclerosis. The present study's findings provide evidence that this combination may help in the early diagnosis of respiratory changes in these patients.

Combined forced oscillation and fractional-order modeling in patients with work-related asthma: a case-control study analyzing respiratory biomechanics and diagnostic accuracy

BACKGROUND

Fractional-order (FrOr) models have a high potential to improve pulmonary science. These models could be useful for biomechanical studies and diagnostic purposes, offering accurate models with an improved ability to describe nature. This paper evaluates the performance of the Forced Oscillation (FO) associated with integer (InOr) and FrOr models in the analysis of respiratory alterations in work-related asthma (WRA).

METHODS

Sixty-two individuals were evaluated: 31 healthy and 31 with WRA with mild obstruction. Patients were analyzed pre- and post-bronchodilation. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC). To evaluate how well do the studied models correspond to observed data, we analyzed the mean square root of the sum (MSEt) and the relative distance (Rd) of the estimated model values to the measured resistance and reactance measured values.

RESULTS AND DISCUSSION

Initially, the use of InOr and FrOr models increased our understanding of the WRA physiopathology, showing increased peripheral resistance, damping, and hysteresivity. The FrOr model (AUC = 0.970) outperformed standard FO (AUC = 0.929), as well as InOr modeling (AUC = 0.838) in the diagnosis of respiratory changes, achieving high accuracy. FrOr improved the curve fitting (MSEt = 0.156 ± 0.340; Rd = 3.026 ± 1.072) in comparison with the InOr model (MSEt = 0.367 ± 0.991; Rd = 3.363 ± 1.098). Finally, we demonstrated that bronchodilator use increased dynamic compliance, as well as reduced damping and peripheral resistance.

CONCLUSIONS

Taken together, these results show clear evidence of the utility of FO associated with fractional-order modeling in patients with WRA, improving our knowledge of the biomechanical abnormalities and the diagnostic accuracy in this disease.

Differential diagnosis of asthma and restrictive respiratory diseases by combining forced oscillation measurements, machine learning and neuro-fuzzy classifiers

To design machine learning classifiers to facilitate the clinical use and increase the accuracy of the forced oscillation technique (FOT) in the differential diagnosis of patients with asthma and restrictive respiratory diseases. FOT and spirometric exams were performed in 97 individuals, including controls (n = 20), asthmatic patients (n = 38), and restrictive (n = 39) patients. The first experiment of this study showed that the best FOT parameter was the resonance frequency, providing moderate accuracy (AUC = 0.87). In the second experiment, a neuro-fuzzy classifier and different supervised machine learning techniques were investigated, including k-nearest neighbors, random forests, AdaBoost with decision trees, and support vector machines with a radial basis kernel. All classifiers achieved high accuracy (AUC ≥ 0.9) in the differentiation between patient groups. In the third and fourth experiments, the use of different feature selection techniques allowed us to achieve high accuracy with only three FOT parameters. In addition, the neuro-fuzzy classifier also provided rules to explain the classification. Neuro-fuzzy and machine learning classifiers can aid in the differential diagnosis of patients with asthma and restrictive respiratory diseases. They can assist clinicians as a support system providing accurate diagnostic options.

Identification of breath-prints for the COPD detection associated with smoking and household air pollution by electronic nose

PURPOSE

The analysis of breath-print, has been proposed as an attractive alternative to investigate possible biomarkers of Chronic Obstructive Pulmonary Disease (COPD). The aim of the present study was to discriminate between healthy subjects, patients with COPD associated with smoking (COPD-S) and patients with COPD associated with household air pollution (COPD-HAP).

METHODS

A cross-sectional study of 294 participants was conducted, 88 with smoking associated COPD, 28 associated with HAP and 178 healthy subjects. Breath-print analysis was performed by using the Cyranose 320 electronic nose. Group data were evaluated by Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA) and Support Vector Machine (SVM) and the test's diagnostic power by means of ROC (Receiver Operating Characteristic) curves.

RESULTS

The results indicated that the breath-print of patients with COPD is different from the one of healthy subjects explaining a variability of 93.8% with a correct prediction of 97.8% and correct classification of 100%,also positive and negative predictive value of 96.5 and 100% respectively. Furthermore, the breath-print of exhaled breath from patients with COPD-S and COPD-HAP does not present any difference.

CONCLUSIONS

The breath-print of exhaled breath from patients with COPD-S and COPD-HAP does not present any difference, which demonstrates that the breath-print is related to the disease and not to causality. With these results, the analysis of the breath-print of COPD is proposed as an alternative for a screening method in future clinical applications.

COPD beyond proximal bronchial obstruction: phenotyping and related tools at the bedside

Chronic obstructive pulmonary disease (COPD) is characterised by nonreversible proximal bronchial obstruction leading to major respiratory disability. However, patient phenotypes better capture the heterogeneously reported complaints and symptoms of COPD. Recent studies provided evidence that classical bronchial obstruction does not properly reflect respiratory disability, and symptoms now form the new paradigm for assessment of disease severity and guidance of therapeutic strategies. The aim of this review was to explore pathways addressing COPD pathogenesis beyond proximal bronchial obstruction and to highlight innovative and promising tools for phenotyping and bedside assessment. Distal small airways imaging allows quantitative characterisation of emphysema and functional air trapping. Micro-computed tomography and parametric response mapping suggest small airways disease precedes emphysema destruction. Small airways can be assessed functionally using nitrogen washout, probing ventilation at conductive or acinar levels, and forced oscillation technique. These tests may better correlate with respiratory symptoms and may well capture bronchodilation effects beyond proximal obstruction.Knowledge of inflammation-based processes has not provided well-identified targets so far, and eosinophils probably play a minor role. Adaptative immunity or specific small airways secretory protein may provide new therapeutic targets. Pulmonary vasculature is involved in emphysema through capillary loss, microvascular lesions or hypoxia-induced remodelling, thereby impacting respiratory disability.

The importance of pathophysiology to the understanding of functional limitations in the bioarchaeology of care approach

This article presents a partial bioarchaeology of care case study of a mummified adult female with chronic obstructive pulmonary disease (COPD) from late historic period United States. It examines likely clinical and functional impacts of disease and corresponding need for provision of care, stopping short of Stage 4 Interpretation/analysis. The case study illustrates and argues for the importance of an interdisciplinary research team for achieving a comprehensive understanding of disability and care. The article highlights the necessity of knowledge of pathophysiology for identifying the potential interventions (supports) dictated by the associated functional limitations. inter alia, this case is a powerful illustration of the way analysis of preserved soft tissue can provide insights into disease and likely care that are inaccessible using skeletal analysis alone. The article represents an interesting contribution to the theory and methodology of both the bioarchaeology of care approach and mummy studies.

Forced oscillation technique for early detection of the effects of smoking and COPD: contribution of fractional-order modeling

Purpose

The aim of the present study was to evaluate the performance of the forced oscillation technique (FOT) for the early diagnosis of the effects of smoking and COPD. The contributions of the integer-order (InOr) and fractional-order (FrOr) models were also evaluated.

Patients and methods

In total, 120 subjects were analyzed: 40 controls, 40 smokers (20.3±9.3 pack-years) and 40 patients with mild COPD.

Results

Initially, it was observed that traditional FOT parameters and the InOr and FrOr models provided a consistent description of the COPD pathophysiology. Mild COPD introduced significant increases in the FrOr inertance, damping factor and hysteresivity (P<0.0001). These parameters were significantly correlated with the spirometric parameters of central and small airway obstruction (P<0.0001). The diagnostic accuracy analyses indicated that FOT parameters and InOr modeling may adequately identify these changes (area under the receiver operating characteristic curve – AUC >0.8). The use of FrOr modeling significantly improved this process (P<0.05), allowing the early diagnosis of smokers and patients with mild COPD with high accuracy (AUC >0.9).

Conclusion

FrOr modeling improves our knowledge of modifications that occur in the early stages of COPD. Additionally, the findings of the present study provide evidence that these models may play an important role in the early diagnosis of COPD, which is crucial for improving the clinical management of the disease.

Airway dynamics in COPD patients by within-breath impedance tracking: effects of continuous positive airway pressure

Tracking of the within-breath changes of respiratory mechanics using the forced oscillation technique may provide outcomes that characterise the dynamic behaviour of the airways during normal breathing.

We measured respiratory resistance (Rrs) and reactance (Xrs) at 8 Hz in 55 chronic obstructive pulmonary disease (COPD) patients and 20 healthy controls, and evaluated Rrs and Xrs as functions of gas flow (V′) and volume (V) during normal breathing cycles. In 12 COPD patients, additional measurements were made at continuous positive airway pressure (CPAP) levels of 4, 8, 14 and 20 hPa.

The Rrs and Xrsversus V′ and V relationships displayed a variety of loop patterns, allowing characterisation of physiological and pathological processes. The main outcomes emerging from the within-breath analysis were the Xrsversus V loop area (AXV) quantifying expiratory flow limitation, and the tidal change in Xrs during inspiration (ΔXI) reflecting alteration in lung inhomogeneity in COPD. With increasing CPAP, AXV and ΔXI approached the normal ranges, although with a large variability between individuals, whereas mean Rrs remained unchanged.

Within-breath tracking of Rrs and Xrs allows an improved assessment of expiratory flow limitation and functional inhomogeneity in COPD; thereby it may help identify the physiological phenotypes of COPD and determine the optimal level of respiratory support.

Current methods to diagnose small airway disease in patients with COPD

The small airways are characterized by an internal diameter < 2 mm and absence of cartilage. Approximately 10-25% of total airway resistance in healthy lungs is due to the small airways, with their contribution to total airway resistance increasing substantially in chronic obstructive pulmonary disease (COPD). As the small airways are located in the lung periphery, they are not easily evaluable, which can potentially interfere with the diagnosis (especially at early stages), monitoring, detection of responses to clinical interventions, and prognostic evaluation in COPD. Here, we will discuss the currently available methods in clinical practice to evaluate small airway disease in COPD, focusing on the concept, advantages, and disadvantages of each method.