A quantitative analysis of extension and distribution of lung injury in COVID-19: a prospective study based on chest computed tomography

Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan

BACKGROUND

Automated analysis of lung computed tomography (CT) scans may help characterize subphenotypes of acute respiratory illness. We integrated lung CT features measured via deep learning with clinical and laboratory data in spontaneously breathing subjects to enhance the identification of COVID-19 subphenotypes.

METHODS

This is a multicenter observational cohort study in spontaneously breathing patients with COVID-19 respiratory failure exposed to early lung CT within 7 days of admission. We explored lung CT images using deep learning approaches to quantitative and qualitative analyses; latent class analysis (LCA) by using clinical, laboratory and lung CT variables; regional differences between subphenotypes following 3D spatial trajectories.

RESULTS

Complete datasets were available in 559 patients. LCA identified two subphenotypes (subphenotype 1 and 2). As compared with subphenotype 2 (n = 403), subphenotype 1 patients (n = 156) were older, had higher inflammatory biomarkers, and were more hypoxemic. Lungs in subphenotype 1 had a higher density gravitational gradient with a greater proportion of consolidated lungs as compared with subphenotype 2. In contrast, subphenotype 2 had a higher density submantellar-hilar gradient with a greater proportion of ground glass opacities as compared with subphenotype 1. Subphenotype 1 showed higher prevalence of comorbidities associated with endothelial dysfunction and higher 90-day mortality than subphenotype 2, even after adjustment for clinically meaningful variables.

CONCLUSIONS

Integrating lung-CT data in a LCA allowed us to identify two subphenotypes of COVID-19, with different clinical trajectories. These exploratory findings suggest a role of automated imaging characterization guided by machine learning in subphenotyping patients with respiratory failure.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020.

Automatically transferring supervised targets method for segmenting lung lesion regions with CT imaging

BACKGROUND

To present an approach that autonomously identifies and selects a self-selective optimal target for the purpose of enhancing learning efficiency to segment infected regions of the lung from chest computed tomography images. We designed a semi-supervised dual-branch framework for training, where the training set consisted of limited expert-annotated data and a large amount of coarsely annotated data that was automatically segmented based on Hu values, which were used to train both strong and weak branches. In addition, we employed the Lovasz scoring method to automatically switch the supervision target in the weak branch and select the optimal target as the supervision object for training. This method can use noisy labels for rapid localization during the early stages of training, and gradually use more accurate targets for supervised training as the training progresses. This approach can utilize a large number of samples that do not require manual annotation, and with the iterations of training, the supervised targets containing noise become closer and closer to the fine-annotated data, which significantly improves the accuracy of the final model.

RESULTS

The proposed dual-branch deep learning network based on semi-supervision together with cost-effective samples achieved 83.56 ± 12.10 and 82.67 ± 8.04 on our internal and external test benchmarks measured by the mean Dice similarity coefficient (DSC). Through experimental comparison, the DSC value of the proposed algorithm was improved by 13.54% and 2.02% on the internal benchmark and 13.37% and 2.13% on the external benchmark compared with U-Net without extra sample assistance and the mean-teacher frontier algorithm, respectively.

CONCLUSION

The cost-effective pseudolabeled samples assisted the training of DL models and achieved much better results compared with traditional DL models with manually labeled samples only. Furthermore, our method also achieved the best performance compared with other up-to-date dual branch structures.

Differences in clinical characteristics and quantitative lung CT features between vaccinated and not vaccinated hospitalized COVID-19 patients in Italy

BACKGROUND

To evaluate the differences in the clinical characteristics and severity of lung impairment, assessed by quantitative lung CT scan, between vaccinated and non-vaccinated hospitalized patients with COVID-19; and to identify the variables with best prognostic prediction according to SARS-CoV-2 vaccination status. We recorded clinical, laboratory and quantitative lung CT scan data in 684 consecutive patients [580 (84.8%) vaccinated, and 104 (15.2%) non-vaccinated], admitted between January and December 2021.

RESULTS

Vaccinated patients were significantly older 78 [69-84] vs 67 [53-79] years and with more comorbidities. Vaccinated and non-vaccinated patients had similar PaO2/FiO2 (300 [252-342] vs 307 [247-357] mmHg; respiratory rate 22 [8-26] vs 19 [18-26] bpm); total lung weight (918 [780-1069] vs 954 [802-1149] g), lung gas volume (2579 [1801-3628] vs 2370 [1675-3289] mL) and non-aerated tissue fraction (10 [7.3-16.0] vs 8.5 [6.0-14.1] %). The overall crude hospital mortality was similar between the vaccinated and non-vaccinated group (23.1% vs 21.2%). However, Cox regression analysis, adjusted for age, ethnicity, age unadjusted Charlson Comorbidity Index and calendar month of admission, showed a 40% reduction in hospital mortality in the vaccinated patients (HRadj = 0.60, 95%CI 0.38-0.95).

CONCLUSIONS

Hospitalized vaccinated patients with COVID-19, although older and with more comorbidities, presented a similar impairment in gas exchange and lung CT scan compared to non-vaccinated patients, but were at a lower risk of mortality.

In Vivo Visualization and Quantification of Neutrophil Elastase in Lungs of COVID-19 Patients: A First-in-Humans PET Study with 11C-NES

Coronavirus disease 2019 (COVID-19) can cause life-threatening lung inflammation that is thought to be mediated by neutrophils. The aim of the present work was to evaluate a novel PET tracer for neutrophil elastase (NE). Methods: In this first-in-humans study, 4 patients with hypoxia due to COVID-19 and 2 healthy controls were investigated with PET using ¹¹C-NES and ¹⁵O-water for visualization and quantification of NE and perfusion in the lungs, respectively. Results: ¹¹C-NES accumulated selectively in lung areas with COVID-19 opacities on CT scans, suggesting high levels of NE there. In the same areas, perfusion was severely reduced in comparison to healthy lung tissue as measured with ¹⁵O-water. Conclusion: The data suggest that NE is associated with severe lung inflammation in COVID-19 patients and that inhibition of NE could potentially reduce the acute inflammatory process and improve the condition.

Chest dual-energy CT to assess the effects of steroids on lung function in severe COVID-19 patients

BACKGROUND

Steroids have been shown to reduce inflammation, hypoxic pulmonary vasoconstriction (HPV) and lung edema. Based on evidence from clinical trials, steroids are widely used in severe COVID-19. However, the effects of steroids on pulmonary gas volume and blood volume in this group of patients are unexplored.

OBJECTIVE

Profiting by dual-energy computed tomography (DECT), we investigated the relationship between the use of steroids in COVID-19 and distribution of blood volume as an index of impaired HPV. We also investigated whether the use of steroids influences lung weight, as index of lung edema, and how it affects gas distribution.

METHODS

Severe COVID-19 patients included in a single-center prospective observational study at the intensive care unit at Uppsala University Hospital who had undergone DECT were enrolled in the current study. Patients' cohort was divided into two groups depending on the administration of steroids. From each patient's DECT, 20 gas volume maps and the corresponding 20 blood volume maps, evenly distributed along the cranial-caudal axis, were analyzed. As a proxy for HPV, pulmonary blood volume distribution was analyzed in both the whole lung and the hypoinflated areas. Total lung weight, index of lung edema, was estimated.

RESULTS

Sixty patients were analyzed, whereof 43 received steroids. Patients not exposed to steroids showed a more extensive non-perfused area (19% vs 13%, p < 0.01) and less homogeneous pulmonary blood volume of hypoinflated areas (kurtosis: 1.91 vs 2.69, p < 0.01), suggesting a preserved HPV compared to patients treated with steroids. Moreover, patients exposed to steroids showed a significantly lower lung weight (953 gr vs 1140 gr, p = 0.01). A reduction in alveolar-arterial difference of oxygen followed the treatment with steroids (322 ± 106 mmHg at admission vs 267 ± 99 mmHg at DECT, p = 0.04).

CONCLUSIONS

The use of steroids might cause impaired HPV and might reduce lung edema in severe COVID-19. This is consistent with previous findings in other diseases. Moreover, a reduced lung weight, as index of decreased lung edema, and a more homogeneous distribution of gas within the lung were shown in patients treated with steroids.

TRIAL REGISTRATION

Clinical Trials ID: NCT04316884, Registered March 13, 2020.

Biological effects of COVID-19 on lung cancer: Can we drive our decisions

COVID-19 infection caused by SARS-CoV-2 is considered catastrophic because it affects multiple organs, particularly those of the respiratory tract. Although the consequences of this infection are not fully clear, it causes damage to the lungs, the cardiovascular and nervous systems, and other organs, subsequently inducing organ failure. In particular, the effects of SARS-CoV-2-induced inflammation on cancer cells and the tumor microenvironment need to be investigated. COVID-19 may alter the tumor microenvironment, promoting cancer cell proliferation and dormant cancer cell (DCC) reawakening. DCCs reawakened upon infection with SARS-CoV-2 can populate the premetastatic niche in the lungs and other organs, leading to tumor dissemination. DCC reawakening and consequent neutrophil and monocyte/macrophage activation with an uncontrolled cascade of pro-inflammatory cytokines are the most severe clinical effects of COVID-19. Moreover, neutrophil extracellular traps have been demonstrated to activate the dissemination of premetastatic cells into the lungs. Further studies are warranted to better define the roles of COVID-19 in inflammation as well as in tumor development and tumor cell metastasis; the results of these studies will aid in the development of further targeted therapies, both for cancer prevention and the treatment of patients with COVID-19.

COVID-19 Acute Respiratory Distress Syndrome: One Pathogen, Multiple Phenotypes

Acute respiratory distress syndrome (ARDS) is a heterogeneous syndrome arising from multiple causes with a range of clinical severity. In recent years, the potential for prognostic and predictive enrichment of clinical trials has been increased with identification of more biologically homogeneous subgroups or phenotypes within ARDS. COVID-19 ARDS also exhibits significant clinical heterogeneity despite a single causative agent. In this review the authors summarize the existing literature on COVID-19 ARDS phenotypes, including physiologic, clinical, and biological subgroups as well as the implications for improving both prognostication and precision therapy.

Lung aeration, ventilation, and perfusion imaging

PURPOSE OF REVIEW

Lung imaging is a cornerstone of the management of patients admitted to the intensive care unit (ICU), providing anatomical and functional information on the respiratory system function. The aim of this review is to provide an overview of mechanisms and applications of conventional and emerging lung imaging techniques in critically ill patients.

RECENT FINDINGS

Chest radiographs provide information on lung structure and have several limitations in the ICU setting; however, scoring systems can be used to stratify patient severity and predict clinical outcomes. Computed tomography (CT) is the gold standard for assessment of lung aeration but requires moving the patients to the CT facility. Dual-energy CT has been recently applied to simultaneous study of lung aeration and perfusion in patients with respiratory failure. Lung ultrasound has an established role in the routine bedside assessment of ICU patients, but has poor spatial resolution and largely relies on the analysis of artifacts. Electrical impedance tomography is an emerging technique capable of depicting ventilation and perfusion at the bedside and at the regional level.

SUMMARY

Clinicians should be confident with the technical aspects, indications, and limitations of each lung imaging technique to improve patient care.

Mechanistic Insights Into the Immune Pathophysiology of COVID-19; An In-Depth Review

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), which causes coronavirus-19 (COVID-19), has caused significant morbidity and mortality globally. In addition to the respiratory manifestations seen in severe cases, multi-organ pathologies also occur, making management a much-debated issue. In addition, the emergence of new variants can potentially render vaccines with a relatively limited utility. Many investigators have attempted to elucidate the precise pathophysiological mechanisms causing COVID-19 respiratory and systemic disease. Spillover of lung-derived cytokines causing a cytokine storm is considered the cause of systemic disease. However, recent studies have provided contradictory evidence, whereby the extent of cytokine storm is insufficient to cause severe illness. These issues are highly relevant, as management approaches considering COVID-19 a classic form of acute respiratory distress syndrome with a cytokine storm could translate to unfounded clinical decisions, detrimental to patient trajectory. Additionally, the precise immune cell signatures that characterize disease of varying severity remain contentious. We provide an up-to-date review on the immune dysregulation caused by COVID-19 and highlight pertinent discussions in the scientific community. The response from the scientific community has been unprecedented regarding the development of highly effective vaccines and cutting-edge research on novel therapies. We hope that this review furthers the conversations held by scientists and informs the aims of future research projects, which will potentially further our understanding of COVID-19 and its immune pathogenesis.