Pulmonary Emphysema Regional Distribution and Extent Assessed by Chest Computed Tomography Is Associated With Pulmonary Function Impairment in Patients With COPD.

Objective: This study aimed to evaluate how emphysema extent and its regional distribution quantified by chest CT are associated with clinical and functional severity in patients with chronic obstructive pulmonary disease (COPD). Methods/Design: Patients with a post-bronchodilator forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) < 0.70, without any other obstructive airway disease, who presented radiological evidence of emphysema on visual CT inspection were retrospectively enrolled. A Quantitative Lung Imaging (QUALI) system automatically quantified the volume of pulmonary emphysema and adjusted this volume to the measured (EmphCTLV) or predicted total lung volume (TLV) (EmphPLV) and assessed its regional distribution based on an artificial neural network (ANN) trained for this purpose. Additionally, the percentage of lung volume occupied by low-attenuation areas (LAA) was computed by dividing the total volume of regions with attenuation lower or equal to -950 Hounsfield units (HU) by the predicted [LAA (%PLV)] or measured CT lung volume [LAA (%CTLV)]. The LAA was then compared with the QUALI emphysema estimations. The association between emphysema extension and its regional distribution with pulmonary function impairment was then assessed. Results: In this study, 86 patients fulfilled the inclusion criteria. Both EmphCTLV and EmphPLV were significantly lower than the LAA indices independently of emphysema severity. CT-derived TLV significantly increased with emphysema severity (from 6,143 ± 1,295 up to 7,659 ± 1,264 ml from mild to very severe emphysema, p < 0.005) and thus, both EmphCTLV and LAA significantly underestimated emphysema extent when compared with those values adjusted to the predicted lung volume. All CT-derived emphysema indices presented moderate to strong correlations with residual volume (RV) (with correlations ranging from 0.61 to 0.66), total lung capacity (TLC) (from 0.51 to 0.59), and FEV1 (~0.6) and diffusing capacity for carbon monoxide DLCO (~0.6). The values of FEV1 and DLCO were significantly lower, and RV (p < 0.001) and TLC (p < 0.001) were significantly higher with the increasing emphysema extent and when emphysematous areas homogeneously affected the lungs. Conclusions: Emphysema volume must be referred to the predicted and not to the measured lung volume when assessing the CT-derived emphysema extension. Pulmonary function impairment was greater in patients with higher emphysema volumes and with a more homogeneous emphysema distribution. Further studies are still necessary to assess the significance of CTpLV in the clinical and research fields.

Automatic Quantification of Interstitial Lung Disease From Chest Computed Tomography in Systemic Sclerosis.

Background: Interstitial lung disease (ILD) is a common complication in patients with systemic sclerosis (SSc), and its diagnosis contributes to early treatment decisions. Purposes: To quantify ILD associated with SSc (SSc-ILD) from chest CT images using an automatic quantification method based on the computation of the weight of interstitial lung opacities. Methods: Ninety-four patients with SSc underwent CT, forced vital capacity (FVC), and carbon monoxide diffusion capacity (DLCO) tests. Seventy-three healthy individuals without radiological evidence of lung disease served as controls. After lung and airway segmentation, the ratio between the weight of interstitial opacities [densities between -500 and +50 Hounsfield units (HU)] and the total lung weight (densities between -1,000 and +50 HU) was used as an ILD indicator (ILD[%] = 100 × [LW(-500 to +50HU)/LW(-1, 000 to +50HU)]). The cutoff of normality between controls and SSc was determined with a receiver operator characteristic curve. The severity of pulmonary involvement in SSc patients was also assessed by calculating Z scores of ILD relative to the average interstitial opacities in controls. Accordingly, SSc-ILD was classified as SSc Limited-ILD (Z score < 3) and SSc Extensive-ILD (Z score ≥ 3 or FVC < 70%). Results: Seventy-eight (83%) SSc patients were classified as presenting SSc-ILD (optimal ILD threshold of 23.4%, 0.83 sensitivity, 0.92 specificity, and 0.94 area under the receiver operator characteristic curve, 95% CI from 0.89 to 0.96, 0.93 positive predictive value, and 0.81 negative predictive value, p < 0.001) and exhibited radiological attenuations compatible with interstitial pneumonia dispersed in the lung parenchyma. Thirty-six (38%) patients were classified as SSc Extensive-ILD (ILD threshold ≥ 29.6% equivalent to a Z score ≥ 3) and 42 (45%) as SSc Limited-ILD. Eighteen (50%) patients with SSc Extensive-ILD presented FVC < 70%, being only five patients classified exclusively based on FVC. SSc Extensive-ILD also presented lower DLCO (57.9 ± 17.9% vs. 73.7 ± 19.8%; p < 0.001) and total lung volume (2,916 ± 674 vs. 4,286 ± 1,136, p < 0.001) compared with SSc Limited-ILD. Conclusion: The proposed method seems to provide an alternative to identify and quantify the extension of ILD in patients with SSc, mitigating the subjectivity of semiquantitative analyzes based on visual scores.

COVID-19 Chest Computed Tomography to Stratify Severity and Disease Extension by Artificial Neural Network Computer-Aided Diagnosis.

Purpose: This work aims to develop a computer-aided diagnosis (CAD) to quantify the extent of pulmonary involvement (PI) in COVID-19 as well as the radiological patterns referred to as lung opacities in chest computer tomography (CT). Methods: One hundred thirty subjects with COVID-19 pneumonia who underwent chest CT at hospital admission were retrospectively studied (141 sets of CT scan images). Eighty-eight healthy individuals without radiological evidence of acute lung disease served as controls. Two radiologists selected up to four regions of interest (ROI) per patient (totaling 1,475 ROIs) visually regarded as well-aerated regions (472), ground-glass opacity (GGO, 413), crazy paving and linear opacities (CP/LO, 340), and consolidation (250). After balancing with 250 ROIs for each class, the density quantiles (2.5, 25, 50, 75, and 97.5%) of 1,000 ROIs were used to train (700), validate (150), and test (150 ROIs) an artificial neural network (ANN) classifier (60 neurons in a single-hidden-layer architecture). Pulmonary involvement was defined as the sum of GGO, CP/LO, and consolidation volumes divided by total lung volume (TLV), and the cutoff of normality between controls and COVID-19 patients was determined with a receiver operator characteristic (ROC) curve. The severity of pulmonary involvement in COVID-19 patients was also assessed by calculating Z scores relative to the average volume of parenchymal opacities in controls. Thus, COVID-19 cases were classified as mild (

Driving pressure and long-term outcomes in moderate/severe acute respiratory distress syndrome.

BACKGROUND: Acute respiratory distress syndrome (ARDS) patients may present impaired in lung function and structure after hospital discharge that may be related to mechanical ventilation strategy. The aim of this study was to evaluate the association between functional and structural lung impairment, N-terminal-peptide type III procollagen (NT-PCP-III) and driving pressure during protective mechanical ventilation. It was a secondary analysis of data from randomized controlled trial that included patients with moderate/severe ARDS with at least one follow-up visit performed. We obtained serial measurements of plasma NT-PCP-III levels. Whole-lung computed tomography analysis and pulmonary function test were performed at 1 and 6 months of follow-up. A health-related quality of life survey after 6 months was also performed. RESULTS: Thirty-three patients were enrolled, and 21 patients survived after 6 months. In extubation day an association between driving pressure and NT-PCP-III was observed. At 1 and 6 months forced vital capacity (FVC) was negatively correlated to driving pressure (p < 0.01). At 6 months driving pressure was associated with lower FVC independently on tidal volume, plateau pressure and baseline static respiratory compliance after adjustments (r2 = 0.51, p = 0.02). There was a significant correlation between driving pressure and lung densities and nonaerated/poorly aerated lung volume after 6 months. Driving pressure was also related to general health domain of SF-36 at 6 months. CONCLUSION: Even in patients ventilated with protective tidal volume, higher driving pressure is associated with worse long-term pulmonary function and structure.

Positive end-expiratory pressure at minimal respiratory elastance represents the best compromise between mechanical stress and lung aeration in oleic acid induced lung injury.

INTRODUCTION: Protective ventilatory strategies have been applied to prevent ventilator-induced lung injury in patients with acute lung injury (ALI). However, adjustment of positive end-expiratory pressure (PEEP) to avoid alveolar de-recruitment and hyperinflation remains difficult. An alternative is to set the PEEP based on minimizing respiratory system elastance (Ers) by titrating PEEP. In the present study we evaluate the distribution of lung aeration (assessed using computed tomography scanning) and the behaviour of Ers in a porcine model of ALI, during a descending PEEP titration manoeuvre with a protective low tidal volume. METHODS: PEEP titration (from 26 to 0 cmH2O, with a tidal volume of 6 to 7 ml/kg) was performed, following a recruitment manoeuvre. At each PEEP, helical computed tomography scans of juxta-diaphragmatic parts of the lower lobes were obtained during end-expiratory and end-inspiratory pauses in six piglets with ALI induced by oleic acid. The distribution of the lung compartments (hyperinflated, normally aerated, poorly aerated and non-aerated areas) was determined and the Ers was estimated on a breath-by-breath basis from the equation of motion of the respiratory system using the least-squares method. RESULTS: Progressive reduction in PEEP from 26 cmH2O to the PEEP at which the minimum Ers was observed improved poorly aerated areas, with a proportional reduction in hyperinflated areas. Also, the distribution of normally aerated areas remained steady over this interval, with no changes in non-aerated areas. The PEEP at which minimal Ers occurred corresponded to the greatest amount of normally aerated areas, with lesser hyperinflated, and poorly and non-aerated areas. Levels of PEEP below that at which minimal Ers was observed increased poorly and non-aerated areas, with concomitant reductions in normally inflated and hyperinflated areas. CONCLUSION: The PEEP at which minimal Ers occurred, obtained by descending PEEP titration with a protective low tidal volume, corresponded to the greatest amount of normally aerated areas, with lesser collapsed and hyperinflated areas. The institution of high levels of PEEP reduced poorly aerated areas but enlarged hyperinflated ones. Reduction in PEEP consistently enhanced poorly or non-aerated areas as well as tidal re-aeration. Hence, monitoring respiratory mechanics during a PEEP titration procedure may be a useful adjunct to optimize lung aeration.

Low-frequency heart rate variability is related to the breath-to-breath variability in the respiratory pattern.

Changes in heart rate variability (HRV) at "respiratory" frequencies (0.15-0.5 Hz) may result from changes in respiration rather than autonomic control. We now investigate if the differences in HRV power in the low-frequency (LF) band (0.05-0.15 Hz, HRV(LF)) can also be predicted by respiration variability, quantified by the fraction of tidal volume power in the LF (V(LF,n)). Three experimental protocols were considered: paced breathing, mental effort tasks, and a repeated attentional task. Significant intra- and interindividual correlations were found between changes in HRV(LF) and V(LF,n) despite all subjects having a respiratory frequency above the LF band. Respiratory parameters (respiratory period, tidal volume, and V(LF,n)) could predict up to 79% of HRV(LF) differences in some cases. This suggests that respiratory variability is another mechanism of HRV(LF) generation, which should be always monitored, assessed, and considered in the interpretation of HRV changes.