Relationships between the pulmonary densitometry values obtained by CT and the forced oscillation technique parameters in patients with silicosis

Respiratory oscillometry and functional analyses in patients with idiopathic scoliosis

Scoliosis is a condition that affects the spine and causes chest rotation and trunk distortion. Individuals with severe deformities may experience dyspnea on exertion and develop respiratory failure. Respiratory oscillometry is a simple and non-invasive method that provides detailed information on lung mechanics. This work aims to investigate the potential of oscillometry in the evaluation of respiratory mechanics in patients with scoliosis and its association with physical performance. We analyzed 32 volunteers in the control group and 32 in the scoliosis group. The volunteers underwent traditional pulmonary function tests, oscillometry, and the 6-minute walk test (6MWT). Oscillometric analysis showed increased values of resistance at 4 Hz (R4, P<0.01), 12 Hz (R12, P<0.0001), and 20 Hz (R20, P<0.01). Similar analysis showed reductions in dynamic compliance (Cdyn, P<0.001) and ventilation homogeneity, as evaluated by resonance frequency (fr, P<0.001) and reactance area (Ax, P<0.001). Respiratory work, described by the impedance modulus, also showed increased values (Z4, P<0.01). Functional capacity was reduced in the group with scoliosis (P<0.001). A significant direct correlation was found between Cobb angle and R12, AX, and Z4 (P=0.0237, P=0.0338, and P=0.0147, respectively), and an inverse correlation was found between Cdyn and Cobb angle (P=0.0190). These results provided new information on respiratory mechanics in scoliosis and are consistent with the involved pathophysiology, suggesting that oscillometry may improve lung function tests for patients with scoliosis.

Oscillometry of the respiratory system in Parkinson's disease: physiological changes and diagnostic use

BACKGROUND

Lung function analysis in Parkinson's disease (PD) is often difficult due to the demand for adequate forced expiratory maneuvers. Respiratory oscillometry exams require onlyquiet tidal breathing and provide a detailed analysis of respiratory mechanics. We hypothesized that oscillometry would simplify the diagnosis of respiratory abnormalitiesin PD and improve our knowledge about the pathophysiological changes in these patients.

MATERIALS AND METHODS

This observational study includes 20 controls and 47 individuals with PD divided into three groups (Hoehn and Yahr Scale 1-1.5; H&Y scale 2-3 and PD smokers).The diagnostic accuracy was evaluated by investigating the area under the receiver operating characteristic curve (AUC).

RESULTS

Initial stages are related to increased peripheral resistance (Rp; p = 0.001). In more advanced stages, a restrictive pattern is added, reflected by reductions in dynamic compliance (p < 0.05) and increase in resonance frequency (Fr; p < 0.001). Smoking PD patients presented increased Rp (p < 0.001) and Fr (p < 0.01). PD does not introduce changes in the central airways. Oscillometric changes were correlated with respiratory muscle weakness (R = 0.37, p = 0.02). Rp showed adequate accuracy in the detection of early respiratory abnormalities (AUC = 0.858), while in more advanced stages, Fr showed high diagnostic accuracy (AUC = 0.948). The best parameter to identify changes in smoking patients was Rp (AUC = 0.896).

CONCLUSION

The initial stages of PD are related to a reduction in ventilation homogeneity associated with changes in peripheral airways. More advanced stages also include a restrictive ventilatory pattern. These changes were correlated with respiratory muscle weakness and were observed in mild and moderate stages of PD in smokers and non-smokers. Oscillometry may adequately identify respiratory changes in the early stages of PD and obtain high diagnostic accuracy in more advanced stages of the disease.

Clinical significance and applications of oscillometry

Recently, “Technical standards for respiratory oscillometry” was published, which reviewed the physiological basis of oscillometric measures and detailed the technical factors related to equipment and test performance, quality assurance and reporting of results. Here we present a review of the clinical significance and applications of oscillometry. We briefly review the physiological principles of oscillometry and the basics of oscillometry interpretation, and then describe what is currently known about oscillometry in its role as a sensitive measure of airway resistance, bronchodilator responsiveness and bronchial challenge testing, and response to medical therapy, particularly in asthma and COPD. The technique may have unique advantages in situations where spirometry and other lung function tests are not suitable, such as in infants, neuromuscular disease, sleep apnoea and critical care. Other potential applications include detection of bronchiolitis obliterans, vocal cord dysfunction and the effects of environmental exposures. However, despite great promise as a useful clinical tool, we identify a number of areas in which more evidence of clinical utility is needed before oscillometry becomes routinely used for diagnosing or monitoring respiratory disease.

This paper provides a current review of the interpretation, clinical significance and application of oscillometry in respiratory medicine, with special emphasis on limitations of evidence and suggestions for future research.https://bit.ly/3GQPViA

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.

Association of respiratory integer and fractional-order models with structural abnormalities in silicosis

BACKGROUND AND OBJECTIVE

Integer and fractional-order models have emerged as powerful methods for obtaining information regarding the anatomical or pathophysiological changes that occur during respiratory diseases. However, the precise interpretation of the model parameters in light of the lung structural changes is not known. This study analyzed the associations of the integer and fractional-order models with structural changes obtained using multidetector computed tomography densitometry (MDCT) and pulmonary function analysis.

METHODS

Integer and fractional-order models were adjusted to data obtained using the forced oscillation technique (FOT). The results obtained in controls (n = 20) were compared with those obtained in patients with silicosis (n = 32), who were submitted to spirometry, body plethysmograph, FOT, diffusing capacity of the lungs for carbon monoxide (DLCO), and MDCT. The diagnostic accuracy was also investigated using ROC analysis.

RESULTS

The observed changes in the integer and fractional-order models were consistent with the pathophysiology of silicosis. The integer-order model showed association only between inertance and the non-aerated compartment (R = -0.69). This parameter also presented the highest associations with spirometry (R = 0.81), plethysmography (-0.61) and pulmonary diffusion (R = 0.53). Considering the fractional-order model, the increase in the poorly aerated and non-aerated regions presented direct correlations with the fractional inertance (R = 0.48), respiratory damping (R = 0.37) and hysteresivity (R = 0.54) and inverse associations with its fractional exponent (R = -0.62) and elastance (-0.35). Significant associations were also observed with spirometry (R = 0.63), plethysmography (0.37) and pulmonary diffusion (R = 0.51). Receiver operator characteristic analysis showed a higher accuracy in the FrOr model (0.908) than the eRIC model (0.789).

CONCLUSIONS

Our study has shown clear associations of the integer and fractional-order parameters with anatomical changes obtained via MDCT and pulmonary function measurements. These findings help to elucidate the physiological interpretation of the integer and fractional-order parameters and provide evidence that these parameters are reflective of the abnormal changes in silicosis. We also observed that the fractional-order model showed smaller curve-fitting errors, which resulted in a higher diagnostic accuracy than that of the eRIC model. Taken together, these results provide strong motivation for further studies exploring the clinical and scientific use of these models in respiratory medicine.

Pulmonary Densitovolumetry Using Computed Tomography in Patients with Nontuberculous Mycobacteria: Correlation with Pulmonary Function Tests

Background

Since nontuberculous mycobacterial pulmonary disease (NTM-PD) is a condition with increasing morbidity, a more detailed knowledge of radiological aspects and pulmonary function plays a relevant role in the diagnosis and appropriate therapeutic management of these patients.

Objectives

The purpose of this study was to evaluate changes in lung parenchyma through computed tomography (CT) densitometry and, secondarily, to analyze its correlation with pulmonary function testing (PFT) in patients with NTM-PD.

Methods

This is a cross-sectional study in which 31 patients with NTM-PD and 27 controls matched by sex, age, and body mass index underwent CT pulmonary densitovolumetry and pulmonary function tests including spirometry and body plethysmograph.

Results

Based on the total lung volume (TLV) and total lung mass (TLM) measurements, the cumulative mass ratios were calculated for 3% (M3), 15% (M15), 85% (M85), and 97% (M97) of the TLV. We also calculated the complement, which is represented by TLM (100%) minus the mass of 15% (C85) or 3% (C97) of the TLV. Patients with NTM-PD presented lower values of M3 and M15 than controls, with greater significant differences in the apical third and middle third measurements. Compared to controls, patients with NTM-PD showed higher values of C85 and C97, although significant differences were observed only in the basal third measurements. There were negative correlations of total lung capacity with M3 and M15 in the middle third and apical third measurements. There were positive correlations of residual volume and airway resistance with M3 at the apical third measurement.

Conclusions

Patients with NTM-PD show reduced lung mass and increased lung mass in the apical and basal regions of the lungs, respectively. Furthermore, there is a relationship between lung mass measurements and pulmonary function parameters.