The forced oscillation technique is a sensitive method for detecting obstructive airway disease in patients with primary Sjögren’s syndrome

Vascular involvement in chronic thromboembolic pulmonary hypertension is associated with spirometry obstructive impairment

BACKGROUND

Chronic thromboembolic pulmonary hypertension (CTEPH) is a type of pulmonary hypertension caused by persistent thromboembolism of the pulmonary arteries. In clinical practice, CTEPH patients often show obstructive ventilatory impairment, even in the absence of a smoking history. Recent reports imply a tendency for CTEPH patients to have a lower FEV_1.0; however, the mechanism underlying obstructive impairment remains unknown.

METHODS

We retrospectively analyzed CTEPH patients who underwent a pulmonary function test and respiratory impedance test to evaluate their exertional dyspnea during admission for right heart catheterization from January 2000 to December 2019. We excluded patients with a smoking history to rule out the effect of smoking on obstructive impairment.

RESULTS

A total of 135 CTEPH patients were analyzed. The median FEV_1.0/FVC was 76.0%, %FEV _1.0 had a negative correlation with the mean pulmonary artery pressure and pulmonary vascular resistance and the CT Angiogram (CTA) obstruction score. A multivariate regression analysis revealed that the CTA obstruction score was an independent factor of a lower %FEV_1.0. In the 54 patients who underwent pulmonary endarterectomy, %FEV_1.0 was improved in some cases and was not in some. Mean PAP largely decreased after PEA in the better %FEV_1.0 improved cases, suggesting that vascular involvement in CTEPH could be associated with spirometry obstructive impairment.

CONCLUSION

%FEV_1.0 had a significant correlation with the CTA obstruction score. Obstructive impairment might have an etiological relationship with vascular involvement. Further investigations could shed new light on the etiology of CTEPH.

Oscillation Mechanics, Integer and Fractional Respiratory Modeling in COPD: Effect of Obstruction Severity

Purpose

This research examines the emerging role of respiratory oscillometry associated with integer (InOr) and fractional order (FrOr) respiratory models in the context of groups of patients with increasing severity. The contributions to our understanding of the respiratory abnormalities along the course of increasing COPD severity and the diagnostic use of this method were also evaluated.

Patients and Methods

Forty-five individuals with no history of smoking or pulmonary diseases (control group) and 141 individuals with diagnoses of COPD were studied, being classified into 45 mild, 42 moderate, 36 severe and 18 very severe cases.

Results

This study has shown initially that the course of increasing COPD severity was adequately described by the model parameters. This resulted in significant and consistent correlations among these parameters and spirometric indexes. Additionally, this evaluation enhanced our understanding of the respiratory abnormalities in different COPD stages. The diagnostic accuracy analyses provided evidence that hysteresivity, obtained from FrOr modeling, allowed a highly accurate identification in patients with mild changes [area under the receiver operator characteristic curve (AUC)= 0.902]. Similar analyses in groups of moderate and severe patients showed that peripheral resistance, derived from InOr modeling, provided the most accurate parameter (AUC=0.898 and 0.998, respectively), while in very severe patients, traditional, InOr and FrOr parameters were able to reach high diagnostic accuracy (AUC>0.9).

Conclusion

InOr and FrOr modeling improved our knowledge of the respiratory abnormalities along the course of increasing COPD severity. In addition, the present study provides evidence that these models may contribute in the diagnosis of COPD. Respiratory oscillometry exams require only tidal breathing and are easy to perform. Taken together, these practical considerations and the results of the present study suggest that respiratory oscillometry associated with InOr and FrOr models may help to improve lung function tests in COPD.

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.

Mixed Airway and Pulmonary Parenchymal Disease in Patients With Primary Sjögren Syndrome: A 6-year Follow-up

OBJECTIVE

To assess pulmonary function and chronic obstructive pulmonary disease (COPD) development over time in patients with primary Sjögren syndrome (pSS), as well as the association between pulmonary function, radiographic findings, respiratory symptoms, and clinical features of pSS, taking cigarette consumption into account.

METHODS

Forty patients with pSS (mean age 66 yrs; range 42-81 yrs; 39 women), previously participating in a cross-sectional study on pulmonary involvement in pSS, were reassessed by pulmonary function tests after a mean follow-up time of 6 years. At follow-up, patients were also assessed by high-resolution computed tomography of the chest, as well as for pSS disease activity, respiratory symptoms, and cigarette consumption.

RESULTS

Patients with pSS showed significantly decreased percentages of predicted total lung capacity (TLC), residual volume (RV), RV/TLC ratio, and diffusing capacity of the lungs for carbon monoxide, as well as an increase in predicted forced expiratory volume in 1 second/vital capacity (FEV1/VC) ratio from baseline to follow-up. The proportion of COPD in patients with pSS did not change significantly from baseline to follow-up (38% vs 40%, respectively). Radiographic signs of bronchial involvement and interstitial lung disease were each found in 38% of the patients.

CONCLUSION

Both airway and pulmonary parenchymal disease were commonly found in patients with pSS, with a coexistence of both an obstructive and restrictive pulmonary function pattern, where the latter tended to deteriorate over time. COPD was a common finding. Airway and pulmonary involvement may be underdiagnosed in pSS, which is why special attention to clinical assessment of pulmonary involvement in patients with pSS is mandated.

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.

High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements

BACKGROUND AND OBJECTIVES

The main pathologic feature of asthma is episodic airway obstruction. This is usually detected by spirometry and body plethysmography. These tests, however, require a high degree of collaboration and maximal effort on the part of the patient. There is agreement in the literature that there is a demand of research into new technologies to improve non-invasive testing of lung function. The purpose of this study was to develop automatic classifiers to simplify the clinical use and to increase the accuracy of the forced oscillation technique (FOT) in the diagnosis of airway obstruction in patients with asthma.

METHODS

The data consisted of FOT parameters obtained from 75 volunteers (39 with obstruction and 36 without). Different supervised machine learning (ML) techniques were investigated, including k-nearest neighbors (KNN), random forest (RF), AdaBoost with decision trees (ADAB) and feature-based dissimilarity space classifier (FDSC).

RESULTS

The first part of this study showed that the best FOT parameter was the resonance frequency (AUC = 0.81), which indicates moderate accuracy (0.70-0.90). In the second part of this study, the use of the cited ML techniques was investigated. All the classifiers improved the diagnostic accuracy. Notably, ADAB and KNN were very close to achieving high accuracy (AUC = 0.88 and 0.89, respectively). Experiments including the cross products of the FOT parameters showed that all the classifiers improved the diagnosis accuracy and KNN was able to reach a higher accuracy range (AUC = 0.91).

CONCLUSIONS

Machine learning classifiers can help in the diagnosis of airway obstruction in asthma patients, and they can assist clinicians in airway obstruction identification.

Early Diagnosis of Respiratory Abnormalities in Asbestos-Exposed Workers by the Forced Oscillation Technique

BACKGROUND

The current reference test for the detection of respiratory abnormalities in asbestos-exposed workers is spirometry. However, spirometry has several shortcomings that greatly affect the efficacy of current asbestos control programs. The forced oscillation technique (FOT) represents the current state-of-the-art technique in the assessment of lung function. This method provides a detailed analysis of respiratory resistance and reactance at different oscillatory frequencies during tidal breathing. Here, we evaluate the FOT as an alternative method to standard spirometry for the early detection and quantification of respiratory abnormalities in asbestos-exposed workers.

METHODOLOGY/PRINCIPAL FINDINGS

Seventy-two subjects were analyzed. The control group was composed of 33 subjects with a normal spirometric exam who had no history of smoking or pulmonary disease. Thirty-nine subjects exposed to asbestos were also studied, including 32 volunteers in radiological category 0/0 and 7 volunteers with radiological categories of 0/1 or 1/1. FOT data were interpreted using classical parameters as well as integer (InOr) and fractional-order (FrOr) modeling. The diagnostic accuracy was evaluated by investigating the area under the receiver operating characteristic curve (AUC). Exposed workers presented increased obstruction (resistance p<0.001) and a reduced compliance (p<0.001), with a predominance of obstructive changes. The FOT parameter changes were correlated with the standard pulmonary function analysis methods (R = -0.52, p<0.001). Early respiratory abnormalities were identified with a high diagnostic accuracy (AUC = 0.987) using parameters obtained from the FrOr modeling. This accuracy was significantly better than those obtained with classical (p<0.001) and InOr (p<0.001) model parameters.

CONCLUSIONS

The FOT improved our knowledge about the biomechanical abnormalities in workers exposed to asbestos. Additionally, a high diagnostic accuracy in the diagnosis of early respiratory abnormalities in asbestos-exposed workers was obtained. This makes the FOT particularly useful as a screening tool in the context of asbestos control and elimination. Moreover, it can facilitate epidemiological research and the longitudinal follow-up of asbestos exposure and asbestos-related diseases.