The effect of posture on airflow distribution, airway geometry and air velocity in healthy subjects.

BACKGROUND: Gravity, and thus body position, can affect the regional distribution of lung ventilation and blood flow. Therefore, body positioning is a potential tool to improve regional ventilation, thereby possibly enhancing the effect of respiratory physiotherapy interventions. In this proof-of-concept study, functional respiratory imaging (FRI) was used to objectively assess effects of body position on regional airflow distribution in the lungs. METHODS: Five healthy volunteers were recruited. The participants were asked during FRI first to lie in supine position, afterwards in standardized right lateral position. RESULTS: In right lateral position there was significantly more regional ventilation also described as Imaging Airflow Distribution in the right lung than in the left lung (P < 0.001). Air velocity was significantly higher in the left lung (P < 0.05). In right lateral position there was significantly more airflow distribution in the right lung than in the left lung (P < 0.001). Significant changes were observed in airway geometry resulting in a decrease in imaged airway volume (P = 0.024) and a higher imaged airway resistance (P = 0.029) in the dependent lung. In general, the effect of right lateral position caused a significant increase in regional ventilation (P < 0.001) in the dependent lung when compared with the supine position. CONCLUSIONS: Changing body position leads to significant changes in regional lung ventilation, objectively assessed by FRI The volume based on the imaging parameters in the dependent lung is smaller in the lateral position than in the supine position. In right lateral decubitus position, airflow distribution is greater in dependent lung compared to the nondependent lung. TRIAL REGISTRATION: The trial has been submitted to www. CLINICALTRIALS: gov with identification number NCT01893697 on 07/02/2013.

CT-derived measurements of pulmonary blood volume in small vessels and the need for supplemental oxygen in COVID-19 patients.

Throughout the COVID-19 pandemic, a portion of those affected have evolved toward acute hypoxic respiratory failure. Initially, this was hypothesized to result from acute lung injury leading to acute respiratory distress syndrome (ARDS). In previous research, a novel quantitative CT post-processing technique was described to quantify the volume of blood contained within pulmonary blood vessels of a given size. We hypothesized that patients with lower BV5 blood flow would have higher supplemental oxygen needs and less favorable arterial blood gas profiles. From the initial data analysis, 111 hospitalized COVID-19 patients were retrospectively selected based on the availability of CT scans of the lungs with a slice thickness of 1.5 mm or less, as well as PCR-confirmed SARS-CoV2 infection. Three-dimensional (3-D) reconstructions of the lungs and pulmonary vasculature were created. Further analysis was performed on 50 patients. Patients were divided into groups based on their need for oxygen at the time of CT scan acquisition. Eighteen out of 50 patients needed >2 L/min supplemental oxygen and this group demonstrated a significantly lower median percentage of total blood flow in the BV5 vessels compared with the 32 patients who needed <2 L/min supplemental oxygen (41.61% vs. 46.89%, P = 0.023). Both groups had significantly less blood as a proportion in BV5 vessels compared with healthy volunteers. These data are consistent with the hypothesis that reduced blood volume within small (BV5) pulmonary vessels is associated with higher needs for supplemental oxygen and more severe gas exchange anomalies in COVID-19 infections.NEW & NOTEWORTHY This research provides, by using new imaging analysis on CT imaging, an insight into the pathophysiology of patients with COVID-19 infection. By visualizing and quantifying the blood in small vessels in the lung, we can link these results to the clinical need for oxygen in patients with COVID-19 infection.

Association of abnormal pulmonary vasculature on CT scan for COVID-19 infection with decreased diffusion capacity in follow up: A retrospective cohort study.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) is a respiratory viral illness causing pneumonia and systemic disease. Abnormalities in pulmonary function tests (PFT) after COVID-19 infection have been described. The determinants of these abnormalities are unclear. We hypothesized that inflammatory biomarkers and CT scan parameters at the time of infection would be associated with abnormal gas transfer at short term follow-up. METHODS: We retrospectively studied subjects who were hospitalized for COVID-19 pneumonia and discharged. Serum inflammatory biomarkers, CT scan and clinical characteristics were assessed. CT images were evaluated by Functional Respiratory Imaging with automated tissue segmentation algorithms of the lungs and pulmonary vasculature. Volumes of the pulmonary vessels that were ≤5mm (BV5), 5-10mm (BV5_10), and ≥10mm (BV10) in cross sectional area were analyzed. Also the amount of opacification on CT (ground glass opacities). PFT were performed 2-3 months after discharge. The diffusion capacity of carbon monoxide (DLCO) was obtained. We divided subjects into those with a DLCO <80% predicted (Low DLCO) and those with a DLCO ≥80% predicted (Normal DLCO). RESULTS: 38 subjects were included in our cohort. 31 out of 38 (81.6%) subjects had a DLCO<80% predicted. The groups were similar in terms of demographics, body mass index, comorbidities, and smoking status. Hemoglobin, inflammatory biomarkers, spirometry and lung volumes were similar between groups. CT opacification and BV5 were not different between groups, but both Low and Normal DLCO groups had lower BV5 measures compared to healthy controls. BV5_10 and BV10 measures were higher in the Low DLCO group compared to the normal DLCO group. Both BV5_10 and BV10 in the Low DLCO group were greater compared to healthy controls. BV5_10 was independently associated with DLCO<80% in multivariable logistic regression (OR 1.29, 95% CI 1.01, 1.64). BV10 negatively correlated with DLCO% predicted (r = -0.343, p = 0.035). CONCLUSIONS: Abnormalities in pulmonary vascular volumes at the time of hospitalization are independently associated with a low DLCO at follow-up. There was no relationship between inflammatory biomarkers during hospitalization and DLCO. Pulmonary vascular abnormalities during hospitalization for COVID-19 may serve as a biomarker for abnormal gas transfer after COVID-19 pneumonia.

Functional respiratory imaging in relation to classical outcome measures in cystic fibrosis: a cross-sectional study.

BACKGROUND: Functional Respiratory Imaging (FRI) combines HRCT scans with computational fluid dynamics to provide objective and quantitative information about lung structure and function. FRI has proven its value in pulmonary diseases such as COPD and asthma, but limited studies have focused on cystic fibrosis (CF). This study aims to investigate the relation of multiple FRI parameters to validated imaging parameters and classical respiratory outcomes in a CF population. METHODS: CF patients aged > 5 years scheduled for a chest CT were recruited in a cross-sectional study. FRI outcomes included regional airway volume, airway wall volume, airway resistance, lobar volume, air trapping and pulmonary blood distribution. Besides FRI, CT scans were independently evaluated by 2 readers using the CF-CT score. Spirometry and the 6-Minute Walk Test (6MWT) were also performed. Statistical tests included linear mixed-effects models, repeated measures correlations, Pearson and Spearman correlations. RESULTS: 39 CT scans of 24 (17M/7F) subjects were analyzed. Patients were 24 ± 9 years old and had a ppFEV1 of 71 ± 25% at the time of the first CT. All FRI parameters showed significant low-to-moderate correlations with the total CF-CT score, except for lobar volume. When considering the relation between FRI parameters and similar CF-CT subscores, significant correlations were found between parameters related to airway volume, air trapping and airway wall thickening. Air trapping, lobar volume after normal expiration and pulmonary blood distribution showed significant associations with all spirometric parameters and oxygen saturation at the end of 6MWT. In addition, air trapping was the only parameter related to the distance covered during 6MWT. A subgroup analysis showed considerably higher correlations in patients with mild lung disease (ppFEV1 ≥ 70%) compared to patients with moderate to severe lung disease (ppFEV1 < 70%) when comparing FRI to CF-CT scores. CONCLUSIONS: Multiple structural characteristics determined by FRI were associated with abnormalities determined by CF-CT score. Air trapping and pulmonary blood distribution appeared to be the most clinically relevant FRI parameters for CF patients due to their associations with classical outcome measures. The FRI methodology could particularly be of interest for patients with mild lung disease, although this should be confirmed in future research.

Altered pulmonary blood volume distribution as a biomarker for predicting outcomes in COVID-19 disease.

INTRODUCTION: Evidence suggests that vascular inflammation and thrombosis may be important drivers of poor clinical outcomes in patients with COVID-19. We hypothesised that a significant decrease in the percentage of blood volume in vessels with a cross-sectional area between 1.25 and 5 mm2 relative to the total pulmonary blood volume (BV5%) on chest computed tomography (CT) in COVID-19 patients is predictive of adverse clinical outcomes. METHODS: We performed a retrospective analysis of chest CT scans from 10 hospitals across two US states in 313 COVID-19-positive and 195 COVID-19-negative patients seeking acute medical care. RESULTS: BV5% was predictive of outcomes in COVID-19 patients in a multivariate model, with a BV5% threshold below 25% associated with OR 5.58 for mortality, OR 3.20 for intubation and OR 2.54 for the composite of mortality or intubation. A model using age and BV5% had an area under the receiver operating characteristic curve of 0.85 to predict the composite of mortality or intubation in COVID-19 patients. BV5% was not predictive of clinical outcomes in patients without COVID-19. CONCLUSIONS: The data suggest BV5% as a novel biomarker for predicting adverse outcomes in patients with COVID-19 seeking acute medical care.

Interstitial lung disease in systemic sclerosis quantification of disease classification and progression with high-resolution computed tomography: An observational study.

Introduction: Systemic sclerosis-associated interstitial lung disease accounts for up to 20% of mortality in these patients and has a highly variable prognosis. Functional respiratory imaging, a quantitative computed tomography imaging technique which allows mapping of regional information, can provide a detailed view of lung structures. It thereby shows potential to better characterize this disease. Purpose: To evaluate the use of functional respiratory imaging quantitative computed tomography in systemic sclerosis-associated interstitial lung disease staging, as well as the relationship between short-term changes in pulmonary function tests and functional respiratory imaging quantitative computed tomography with respect to disease severity. Materials and methods: An observational cohort of 35 patients with systemic sclerosis was retrospectively studied by comparing serial pulmonary function tests and in- and expiratory high-resolution computed tomography over 1.5-year interval. After classification into moderate to severe lung disease and limited lung disease (using a hybrid method integrating quantitative computed tomography and pulmonary function tests), post hoc analysis was performed using mixed-effects models and estimated marginal means in terms of functional respiratory imaging parameters. Results: At follow-up, relative mean forced vital capacity percentage change was not significantly different in the limited (6.37%; N = 13; p = 0.053) and moderate to severe disease (-3.54%; N = 16; p = 0.102) groups, respectively. Specific airway resistance decreased from baseline for both groups. (Least square mean changes -25.11% predicted (p = 0.006) and -14.02% predicted (p = 0.001) for limited and moderate to severe diseases.) In contrast to limited disease from baseline, specific airway radius increased in moderate to severe disease by 8.57% predicted (p = 0.011) with decline of lower lobe volumes of 2.97% predicted (p = 0.031). Conclusion: Functional respiratory imaging is able to differentiate moderate to severe disease versus limited disease and to detect disease progression in systemic sclerosis.

Functional respiratory imaging identifies redistribution of pulmonary blood flow in patients with COVID-19.

An increasing observation is that some patients with COVID-19 have normal lung compliance but significant hypoxaemia different from typical acute respiratory distress syndrome (ARDS). We hypothesised that changes in pulmonary blood distribution may be partially responsible and used functional respiratory imaging on CT scans to calculate pulmonary blood volume. We found that patients with COVID-19 had significantly reduced blood volume in the smaller calibre blood vessels (here defined as <5 mm2 cross-sectional area) compared with matched ARDS patients and healthy controls. This suggests that using high levels of PEEP may not alone be enough to oxygenate these patients and that additional management strategies may be needed.

Assessment of Small Pulmonary Blood Vessels in COVID-19 Patients Using HRCT.

RATIONALE AND OBJECTIVES: Mounting evidence supports the role of pulmonary hemodynamic alternations in the pathogenesis of COVID-19. Previous studies have demonstrated that changes in pulmonary blood volumes measured on computed tomography (CT) are associated with histopathological markers of pulmonary vascular pruning, suggesting that quantitative CT analysis may eventually be useful in the assessment pulmonary vascular dysfunction more broadly. MATERIALS AND METHODS: Building upon previous work, automated quantitative CT measures of small blood vessel volume and pulmonary vascular density were developed. Scans from 103 COVID-19 patients and 107 healthy volunteers were analyzed and their results compared, with comparisons made both on lobar and global levels. RESULTS: Compared to healthy volunteers, COVID-19 patients showed significant reduction in BV5 (pulmonary blood volume contained in blood vessels of <5 mm2) expressed as BV5/(total pulmonary blood volume; p < 0.0001), and significant increases in BV5-10 and BV 10 (pulmonary blood volumes contained in vessels between 5 and 10 mm2 and above 10 mm2, respectively, p < 0.0001). These changes were consistent across lobes. CONCLUSION: COVID-19 patients display striking anomalies in the distribution of blood volume within the pulmonary vascular tree, consistent with increased pulmonary vasculature resistance in the pulmonary vessels below the resolution of CT.

Machine Learning Algorithms Utilizing Functional Respiratory Imaging May Predict COPD Exacerbations.

RATIONALE AND OBJECTIVES: Acute chronic obstructive pulmonary disease exacerbations (AECOPD) have a significant negative impact on the quality of life and accelerate progression of the disease. Functional respiratory imaging (FRI) has the potential to better characterize this disease. The purpose of this study was to identify FRI parameters specific to AECOPD and assess their ability to predict future AECOPD, by use of machine learning algorithms, enabling a better understanding and quantification of disease manifestation and progression. MATERIALS AND METHODS: A multicenter cohort of 62 patients with COPD was analyzed. FRI obtained from baseline high resolution CT data (unenhanced and volume gated), clinical, and pulmonary function test were analyzed and incorporated into machine learning algorithms. RESULTS: A total of 11 baseline FRI parameters could significantly distinguish ( p < 0.05) the development of AECOPD from a stable period. In contrast, no baseline clinical or pulmonary function test parameters allowed significant classification. Furthermore, using Support Vector Machines, an accuracy of 80.65% and positive predictive value of 82.35% could be obtained by combining baseline FRI features such as total specific image-based airway volume and total specific image-based airway resistance, measured at functional residual capacity. Patients who developed an AECOPD, showed significantly smaller airway volumes and (hence) significantly higher airway resistances at baseline. CONCLUSION: This study indicates that FRI is a sensitive tool (PPV 82.35%) for predicting future AECOPD on a patient specific level in contrast to classical clinical parameters.

Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation.

RATIONALE AND OBJECTIVES: Long-term survival after lung transplantation (LTx) is limited by bronchiolitis obliterans syndrome (BOS), defined as a sustained decline in forced expiratory volume in the first second (FEV1) not explained by other causes. We assessed whether machine learning (ML) utilizing quantitative computed tomography (qCT) metrics can predict eventual development of BOS. MATERIALS AND METHODS: Paired inspiratory-expiratory CT scans of 71 patients who underwent LTx were analyzed retrospectively (BOS [n = 41] versus non-BOS [n = 30]), using at least two different time points. The BOS cohort experienced a reduction in FEV1 of >10% compared to baseline FEV1 post LTx. Multifactor analysis correlated declining FEV1 with qCT features linked to acute inflammation or BOS onset. Student t test and ML were applied on baseline qCT features to identify lung transplant patients at baseline that eventually developed BOS. RESULTS: The FEV1 decline in the BOS cohort correlated with an increase in the lung volume (P = .027) and in the central airway volume at functional residual capacity (P = .018), not observed in non-BOS patients, whereas the non-BOS cohort experienced a decrease in the central airway volume at total lung capacity with declining FEV1 (P = .039). Twenty-three baseline qCT parameters could significantly distinguish between non-BOS patients and eventual BOS developers (P < .05), whereas no pulmonary function testing parameters could. Using ML methods (support vector machine), we could identify BOS developers at baseline with an accuracy of 85%, using only three qCT parameters. CONCLUSIONS: ML utilizing qCT could discern distinct mechanisms driving FEV1 decline in BOS and non-BOS LTx patients and predict eventual onset of BOS. This approach may become useful to optimize management of LTx patients.