Effects of inspiratory flow on lung stress, pendelluft, and ventilation heterogeneity in ARDS: A physiological study

Background: High inspiratory flow might damage the lungs by mechanisms not fully understood yet. We hypothesized that increasing inspiratory flow would increase lung stress, ventilation heterogeneity, and pendelluft in ARDS patients undergoing volume-controlled ventilation with constant tidal volume and that higher PEEP levels would reduce this phenomenon. Methods: Ten ARDS patients were studied during protective volume-controlled ventilation. Three inspiratory flows (400, 800, and 1200 ml/s) and two PEEP levels (5 and 15 cmH2O) were applied in random order to each patient. Airway and esophageal pressures were recorded, end-inspiratory and end-expiratory holds were performed, and ventilation distribution was measured with electrical impedance tomography. Peak and plateau airway and transpulmonary pressures were recorded, together with the airway and transpulmonary pressure corresponding to the first point of zero end-inspiratory flow (P1). Ventilation heterogeneity was measured by the EIT-based global inhomogeneity (GI) index. Pendelluft was measured as the absolute difference between pixel-level inflation measured at plateau pressure minus P1. Results: Plateau airway and transpulmonary pressure was not affected by inspiratory flow, while P1 increased at increasing inspiratory flow. The difference between P1 and plateau pressure was higher at higher flows at both PEEP levels (p < 0.001). While higher PEEP reduced heterogeneity of ventilation, higher inspiratory flow increased GI (p = 0.05), irrespective of the PEEP level. Finally, gas volume undergoing pendelluft was larger at higher inspiratory flow (p < 0.001), while PEEP had no effect. Conclusions: The present exploratory analysis suggests that higher inspiratory flow increases additional inspiratory pressure, heterogeneity of ventilation, and pendelluft while PEEP has negligible effects on these flow-dependent phenomena. The clinical significance of these findings needs to be further clarified

Lung response to prone positioning in mechanically-ventilated patients with COVID-19

Background: Prone positioning improves survival in moderate-to-severe acute respiratory distress syndrome (ARDS) unrelated to the novel coronavirus disease (COVID-19). This benefit is probably mediated by a decrease in alveolar collapse and hyperinflation and a more homogeneous distribution of lung aeration, with fewer harms from mechanical ventilation. In this preliminary physiological study we aimed to verify whether prone positioning causes analogue changes in lung aeration in COVID-19. A positive result would support prone positioning even in this other population. Methods: Fifteen mechanically-ventilated patients with COVID-19 underwent a lung computed tomography in the supine and prone position with a constant positive end-expiratory pressure (PEEP) within three days of endotracheal intubation. Using quantitative analysis, we measured the volume of the non-aerated, poorly-aerated, well-aerated, and over-aerated compartments and the gas-to-tissue ratio of the ten vertical levels of the lung. In addition, we expressed the heterogeneity of lung aeration with the standardized median absolute deviation of the ten vertical gas-to-tissue ratios, with lower values indicating less heterogeneity. Results: By the time of the study, PEEP was 12 (10–14) cmH2O and the PaO2:FiO2 107 (84–173) mmHg in the supine position. With prone positioning, the volume of the non-aerated compartment decreased by 82 (26–147) ml, of the poorly-aerated compartment increased by 82 (53–174) ml, of the normally-aerated compartment did not significantly change, and of the over-aerated compartment decreased by 28 (11–186) ml. In eight (53%) patients, the volume of the over-aerated compartment decreased more than the volume of the non-aerated compartment. The gas-to-tissue ratio of the ten vertical levels of the lung decreased by 0.34 (0.25–0.49) ml/g per level in the supine position and by 0.03 (− 0.11 to 0.14) ml/g in the prone position (p < 0.001). The standardized median absolute deviation of the gas-to-tissue ratios of those ten levels decreased in all patients, from 0.55 (0.50–0.71) to 0.20 (0.14–0.27) (p < 0.001). Conclusions: In fifteen patients with COVID-19, prone positioning decreased alveolar collapse, hyperinflation, and homogenized lung aeration. A similar response has been observed in other ARDS, where prone positioning improves outcome. Therefore, our data provide a pathophysiological rationale to support prone positioning even in COVID-19

Noninvasive assessment of airflows by electrical impedance tomography in intubated hypoxemic patients : an exploratory study

Background: Noninvasive monitoring of maximal inspiratory and expiratory flows (MIF and MEF, respectively) by electrical impedance tomography (EIT) might enable early recognition of changes in the mechanical properties of the respiratory system due to new conditions or in response to treatments. We aimed to validate EIT-based measures of MIF and MEF against spirometry in intubated hypoxemic patients during controlled ventilation and spontaneous breathing. Moreover, regional distribution of maximal airflows might interact with lung pathology and increase the risk of additional ventilation injury. Thus, we also aimed to describe the effects of mechanical ventilation settings on regional MIF and MEF. Methods: We performed a new analysis of data from two prospective, randomized, crossover studies. We included intubated patients admitted to the intensive care unit with acute hypoxemic respiratory failure (AHRF) and acute respiratory distress syndrome (ARDS) undergoing pressure support ventilation (PSV, n = 10) and volume-controlled ventilation (VCV, n = 20). We measured MIF and MEF by spirometry and EIT during six different combinations of ventilation settings: higher vs. lower support during PSV and higher vs. lower positive end-expiratory pressure (PEEP) during both PSV and VCV. Regional airflows were assessed by EIT in dependent and non-dependent lung regions, too. Results: MIF and MEF measured by EIT were tightly correlated with those measured by spirometry during all conditions (range of R2 0.629\u20130.776 and R2 0.606\u20130.772, respectively, p < 0.05 for all), with clinically acceptable limits of agreement. Higher PEEP significantly improved homogeneity in the regional distribution of MIF and MEF during volume-controlled ventilation, by increasing airflows in the dependent lung regions and lowering them in the non-dependent ones. Conclusions: EIT provides accurate noninvasive monitoring of MIF and MEF. The present study also generates the hypothesis that EIT could guide PSV and PEEP settings aimed to increase homogeneity of distending and deflating regional airflows

Fusing multi-season UAS images with convolutional neural networks to map tree species in Amazonian forests.

Remote sensing images obtained by unoccupied aircraft systems (UAS) across different seasons enabled capturing of species-specific phenological patterns of tropical trees. The application of UAS multi-season images to classify tropical tree species is still poorly understood. In this study, we used RGB images from different seasons obtained by a low-cost UAS and convolutional neural networks (CNNs) to map tree species in an Amazonian forest. Individual tree crowns (ITC) were outlined in the UAS images and identified to the species level using forest inventory data. The CNN model was trained with images obtained in February, May, August, and November. The classification accuracy in the rainy season (November and February) was higher than in the dry season (May and August). Fusing images from multiple seasons improved the average accuracy of tree species classification by up to 21.1 percentage points, reaching 90.5%. The CNN model can learn species-specific phenological characteristics that impact the classification accuracy, such as leaf fall in the dry season, which highlights its potential to discriminate species in various conditions. We produced high-quality individual tree crown maps of the species using a post-processing procedure. The combination of multi-season UAS images and CNNs has the potential to map tree species in the Amazon, providing valuable insights for forest management and conservation initiatives

Awareness of cognitive decline trajectories in asymptomatic individuals at risk for AD

Background: Lack of awareness of cognitive decline (ACD) is common in late-stage Alzheimer’s disease (AD). Recent studies showed that ACD can also be reduced in the early stages. Methods: We described different trends of evolution of ACD over 3 years in a cohort of memory-complainers and their association to amyloid burden and brain metabolism. We studied the impact of ACD at baseline on cognitive scores’ evolution and the association between longitudinal changes in ACD and in cognitive score. Results: 76.8% of subjects constantly had an accurate ACD (reference class). 18.95% showed a steadily heightened ACD and were comparable to those with accurate ACD in terms of demographic characteristics and AD biomarkers. 4.25% constantly showed low ACD, had significantly higher amyloid burden than the reference class, and were mostly men. We found no overall effect of baseline ACD on cognitive scores’ evolution and no association between longitudinal changes in ACD and in cognitive scores. Conclusions: ACD begins to decrease during the preclinical phase in a group of individuals, who are of great interest and need to be further characterized. Trial registration: The present study was conducted as part of the INSIGHT-PreAD study. The identification number of INSIGHT-PreAD study (ID-RCB) is 2012-A01731-42

Adaptive regression modeling of biomarkers of potential harm in a population of U.S. adult cigarette smokers and nonsmokers

<p>Abstract</p> <p>Background</p> <p>This article describes the data mining analysis of a clinical exposure study of 3585 adult smokers and 1077 nonsmokers. The analysis focused on developing models for four biomarkers of potential harm (BOPH): white blood cell count (WBC), 24 h urine 8-epi-prostaglandin F_2α(EPI8), 24 h urine 11-dehydro-thromboxane B₂(DEH11), and high-density lipoprotein cholesterol (HDL).</p> <p>Methods</p> <p>Random Forest was used for initial variable selection and Multivariate Adaptive Regression Spline was used for developing the final statistical models</p> <p>Results</p> <p>The analysis resulted in the generation of models that predict each of the BOPH as function of selected variables from the smokers and nonsmokers. The statistically significant variables in the models were: platelet count, hemoglobin, C-reactive protein, triglycerides, race and biomarkers of exposure to cigarette smoke for WBC (R-squared = 0.29); creatinine clearance, liver enzymes, weight, vitamin use and biomarkers of exposure for EPI8 (R-squared = 0.41); creatinine clearance, urine creatinine excretion, liver enzymes, use of Non-steroidal antiinflammatory drugs, vitamins and biomarkers of exposure for DEH11 (R-squared = 0.29); and triglycerides, weight, age, sex, alcohol consumption and biomarkers of exposure for HDL (R-squared = 0.39).</p> <p>Conclusions</p> <p>Levels of WBC, EPI8, DEH11 and HDL were statistically associated with biomarkers of exposure to cigarette smoking and demographics and life style factors. All of the predictors togather explain 29%-41% of the variability in the BOPH.</p