Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis

PurposeTo assess the impact of lung segmentation accuracy in an automatic pipeline for quantitative analysis of CT images.MethodsFour different platforms for automatic lung segmentation based on convolutional neural network (CNN), region-growing technique and atlas-based algorithm were considered. The platforms were tested using CT images of 55 COVID-19 patients with severe lung impairment. Four radiologists assessed the segmentations using a 5-point qualitative score (QS). For each CT series, a manually revised reference segmentation (RS) was obtained. Histogram-based quantitative metrics (QM) were calculated from CT histogram using lung segmentationsfrom all platforms and RS. Dice index (DI) and differences of QMs (ΔQMs) were calculated between RS and other segmentations.ResultsHighest QS and lower ΔQMs values were associated to the CNN algorithm. However, only 45% CNN segmentations were judged to need no or only minimal corrections, and in only 17 cases (31%), automatic segmentations provided RS without manual corrections. Median values of the DI for the four algorithms ranged from 0.993 to 0.904. Significant differences for all QMs calculated between automatic segmentations and RS were found both when data were pooled together and stratified according to QS, indicating a relationship between qualitative and quantitative measurements. The most unstable QM was the histogram 90th percentile, with median ΔQMs values ranging from 10HU and 158HU between different algorithms.ConclusionsNone of tested algorithms provided fully reliable segmentation. Segmentation accuracy impacts differently on different quantitative metrics, and each of them should be individually evaluated according to the purpose of subsequent analyses.     

Predictive factors of mortality in patients treated with tocilizumab for acute respiratory distress syndrome related to coronavirus disease 2019 (COVID-19)

COVID-19 patients (n = 34) suffering from ARDS were treated with tocilizumab (TCZ). Outcome was classified in two groups: “Death” and “Recovery”. Predictive factors of mortality were studied. Mean age was 75.3, mean oxygen (O₂) requirements 10.4 l/min. At baseline, all patients had multiple biological abnormalities (lymphopenia, increased CRP, ferritin, fibrinogen, D-dimer and liver enzymes). 24 patients (70.5%) recovered after TCZ therapy and 10 died (29.5%). Deceased subjects differed from patients in whom treatment was effective with regard to more pronounced lymphopenia (0.6 vs 1.0 G/l; p = 0.037), lower platelet number (156 vs 314 G/l; p = 0.0001), lower fibrinogen serum level (0.6 vs 1.0 G/l; p = 0.03), higher aspartate-amino-transferase (108 vs 57 UI/l; p = 0.05) and greater O₂ requirements (11 vs 8 l/min; p = 0.003).     

Pathogenesis and treatment of cytokine storm in COVID-19

COVID-19 is a viral infection caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that killed a large number of patients around the world. A hyperinflammatory state resulting in a cytokine storm and adult respiratory distress syndrome seems to be the major cause of the death. Many mechanisms have been suggested in the pathogenesis of COVID-19 associated cytokine storm (COVID-CS). Insufficient viral clearance and persistence of a strong cytokine response despite inadequate antiviral immunity seem to be the main mechanisms underlying the pathogenesis. The diagnosis of COVID-19 is based on relatively constant clinical symptoms, clinical findings, laboratory tests, and imaging techniques, while the diagnosis of COVID-CS is a rather dynamic process, based on evolving or newly emerging findings during the clinical course. Management of COVID-19 consists of using antiviral agents to inhibit SARS-CoV-2 replication and treating potential complications including the cytokine storm together with general supportive measures. COVID-CS may be treated using appropriate immunosuppressive and immunomodulatory drugs that reduce the level of inappropriate systemic inflammation, which has the potential to cause organ damage. Currently corticosteroids, IL-6 blockers, or IL-1 blockers are most widely used for treating COVID-CS.     

Bacterial and fungal co-infections among COVID-19 patients in intensive care unit

This study aimed to investigate the frequency and characteristics of respiratory co-infections in COVID-19 patients in the intensive care unit (ICU). In this retrospective observational study, pathogens responsible for potential co-infections were detected by the bacterial culture, real-time polymerase chain reaction (RT-PCR), or serological fungal antigen tests. Demographic and clinical characteristics, as well as microbial results, were analyzed. Bacterial culture identified 56 (58.3%) positive samples for respiratory pathogens, with the most common bacteria being Burkholderia cepacia (18, 18.8%). RT-PCR detected 38 (76.0%) and 58 (87.9%) positive results in the severe and critical groups, respectively. Most common pathogens detected were Stenotrophomonas maltophilia (28.0%) and Pseudomonas aeruginosa (28.0%) in the severe group and S. maltophilia (45.5%) in the critical group. P. aeruginosa was detected more during the early stage after ICU admission. Acinetobacter baumannii and Staphylococcus aureus were more frequently identified during late ICU admission. Fungal serum antigens were more frequently positive in the critical group than in the severe group, and the positive rate of fungal serum antigens frequency increased with prolonged ICU stay. A high frequency of respiratory co-infections presented in ICU COVID-19 patients. Careful examinations and necessary tests should be performed to exclude these co-infections.     

Mesenchymal stem/stromal cells as adjuvant therapy in COVID-19-associated acute lung injury and cytokine storm: Importance of cell identification

Theoretically, mesenchymal stem cells (MSCs) are very promising as adjuvant therapy to alleviate coronavirus disease 2019 (COVID-19)-associated acute lung injury and cytokine storm. Several published studies, which used MSCs to alleviate COVID-19-associated acute lung injury and cytokine storm, reported promising results. However, the evidence came from a case report, case series, and clinical trials with a limited number of participants. Therefore, more studies are needed to get robust proof of MSC beneficial effects.     

Diagnosis and treatment of lung cancer in Denmark during the COVID-19 pandemic

BackgroundWe examined the number of lung cancers diagnosed, the quality of care and the socio-economic and clinical characteristics among patients with lung cancer during the COVID-19 pandemic compared to previous years.MethodsWe included all patients ≥ 18 years old diagnosed with lung cancer from 01 January 2018 to 31 August 2021 as registered in the Danish Lung Cancer Registry. Using a generalised linear model, we estimated prevalence ratios (PR) and 95% confidence intervals (CI) of the associations between the pandemic and socioeconomic and clinical factors, and indicators of quality.ResultsWe included 18,113 patients with lung cancer (82.0% non-small cell lung cancer (NSCLC)), which was similar to the preceding years, although a decline in NSCLC cases occurred during the first lockdown period in 2020. No difference in distribution of income or educational level was observed. No difference was observed in the quality of treatment – as measured by curative intent, proportion of patients resected or who died within 90 days of diagnosis.ConclusionUsing nationwide population-based data, our study reassuringly shows no adverse effects of the COVID-19 pandemic on the diagnosis, socio-economic characteristics nor quality of treatment of lung cancer, as compared to the preceding years.     

Early adoption of non-pharmaceutical interventions and COVID-19 mortality

To contain the spread of the COVID-19 pandemic, many countries around the globe have adopted social distancing measures. Yet, establishing the causal effect of non-pharmaceutical interventions (NPIs) is difficult because they do not occur arbitrarily. We exploit a quasi-random source of variation for identification purposes –namely, regional differences in the placement on the pandemic curve following an unexpected and nationwide lockdown. Our results reveal that regions where the outbreak had just started when the lockdown was implemented had 1.62 fewer daily deaths per 100,000 inhabitants when compared to regions for which the lockdown arrived 10+ days after the pandemic’s outbreak. As a result, a total of 4,642 total deaths (232 deaths/daily) could have been avoided by the end of our period of study –a figure representing 23% of registered deaths in Spain at the time. We rule out differential pre−COVID mortality trends and self-distancing behaviors across the compared regions prior to the swift lockdown, which was also uniformly observed nationwide. In addition, we provide supporting evidence for contagion deceleration as the main mechanism behind the effectiveness of the early adoption of NPIs in lowering the death rate, rather than an increased healthcare capacity.     

Dosimetric and radiation cancer risk evaluation of high resolution thorax CT during COVID-19 outbreak

PurposeThe aim of this work was to evaluate the dosimetric impact of high-resolution thorax CT during COVID-19 outbreak in the University Hospital of Parma. In two months we have performed a huge number of thorax CT scans collecting effective and equivalent organ doses and evaluating also the lifetime attributable risk (LAR) of lung and other major cancers.Materials and MethodFrom February 24th to April 28th, 3224 high-resolution thorax CT were acquired. For all patients we have examined the volumetric computed tomography dose index (CTDIvol), the dose length product (DLP), the size-specific dose estimate (SSDE) and effective dose (E₁₀₃) using a dose tracking software (Radimetrics Bayer HealthCare). From the equivalent dose to organs for each patient, LAR for lung and major cancers were estimated following the method proposed in BEIR VII which considers age and sex differences.ResultsStudy population included 3224 patients, 1843 male and 1381 female, with an average age of 67 years. The average CTDIvol, SSDE and DLP, and E₁₀₃ were 6.8 mGy, 8.7 mGy, 239 mGy·cm and 4.4 mSv respectively. The average LAR of all solid cancers was 2.1 cases per 10,000 patients, while the average LAR of leukemia was 0.2 cases per 10,000 patients. For both male and female the organ with a major cancer risk was lung.ConclusionsDespite the impressive increment in thoracic CT examinations due to COVID-19 outbreak, the high resolution low dose protocol used in our hospital guaranteed low doses and very low risk estimation in terms of LAR.     

Perspectives on mesenchymal stem/progenitor cells and their derivates as potential therapies for lung damage caused by COVID-19

The new coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which emerged in December 2019 in Wuhan, China, has reached worldwide pandemic proportions, causing coronavirus disease 2019 (COVID-19). The clinical manifestations of COVID-19 vary from an asymptomatic disease course to clinical symptoms of acute respiratory distress syndrome and severe pneumonia. The lungs are the primary organ affected by SARS-CoV-2, with a very slow turnover for renewal. SARS-CoV-2 enters the lungs via angiotensin-converting enzyme 2 receptors and induces an immune response with the accumulation of immunocompetent cells, causing a cytokine storm, which leads to target organ injury and subsequent dysfunction. To date, there is no effective antiviral therapy for COVID-19 patients, and therapeutic strategies are based on experience treating previously recognized coronaviruses. In search of new treatment modalities of COVID-19, cell-based therapy with mesenchymal stem cells (MSCs) and/or their secretome, such as soluble bioactive factors and extracellular vesicles, is considered supportive therapy for critically ill patients. Multipotent MSCs are able to differentiate into different types of cells of mesenchymal origin, including alveolar epithelial cells, lung epithelial cells, and vascular endothelial cells, which are severely damaged in the course of COVID-19 disease. Moreover, MSCs secrete a variety of bioactive factors that can be applied for respiratory tract regeneration in COVID-19 patients thanks to their trophic, anti-inflammatory, immunomodulatory, anti-apoptotic, pro-regenerative, and proangiogenic properties.     

Immediate and antecedent causes of mortality in hospitalised Indian patients with COVID-19

It is of interest to assess the immediate and antecedent causes of mortality amongst adult COVID-19 infected patients with or without comorbidities admitted in an exclusive COVID-19 hospital was conducted the between August 2020 to May 2021. The immediate and antecedent causes were collected from the medical certificate of cause of death (MCCD). Remaining data was extracted from the hospital’s record. ICMR protocol was used to grade severity of illness at admission into mild, moderate and severe categories. Clinical status during hospitalisation and most recent radiographic and laboratory data were used to assess disease progression and outcome. This study includes data from 571 people, who died at our centre between August 2020 and May 2021. Patients registered without any co-morbidity were 146 with mean age of 57.53 years; (33/146) were females and (110/46) males. Hypertension (274, 47.99%) was found in a moderately large number of patients followed by diabetes (225, 39.4%) and anaemia (199, 34.6%). Increase in risk of mortality of COVID-19 was found maximum in patients with acute respiratory distress syndrome (72.33%), followed by secondary infections (6.83%). Mortality recorded in this study was mainly in males of older age (50 years and above) with at least one co-morbidity. Anaemia was also prevalent amongst these patients and considered as an independent factor for mortality. Hence, recording of comorbidities and haemoglobin levels may help as a guideline to develop risk stratification and management of patients with COVID-19 to reduce overall mortality.     

Disparities in COVID-19 mortality by county racial composition and the role of spring social distancing measures

Available COVID-19 data shows higher shares of cases and deaths occur among Black Americans, but reporting of data by race is poor. This paper investigates disparities in county-level mortality rates across counties with higher and lower than national average Black population shares using nonlinear regression decomposition and estimates potential differential impact of social distancing measures. I find counties with Black population shares above the national share have mortality rates 2 to 3 times higher than in other counties. Observable differences in living conditions, health, and work characteristics reduce the disparity to approximately 1.25 to 1.65 overall, and explain 100% of the disparity at 21 days after the first case. Though higher rates of comorbidities in counties with higher Black population shares are an important predictor, living situation factors like single parenthood and population density are just as important. Higher rates of co-residence with grandchildren explain 11% of the 21 day disparity but do not appear important by 42 days, suggesting families may have been better able to protect vulnerable family members later in the epidemic. To analyze differential effects of social distancing measures use two approaches. First, I exploit the timing of interventions relative to the first case among counties that began their epidemic at the same time. Second, I use event study analysis to analyze within-county changes in mortality. Findings for social distancing measures are not always consistent across approaches. Overall, I find no evidence that school closures were less effective in counties with larger Black population shares, and some estimates suggest closures may have disproportionately helped more diverse counties and counties with high rates of grandparent and grandchild co-residence. Conversely, stay at home orders are less clearly associated with mortality in any counties, reaching peak unemployment did not reduce mortality in any models, and some estimates indicate reaching peak unemployment before the first case was associated with higher mortality rates, especially in more diverse counties.     

A patient-specific approach for quantitative and automatic analysis of computed tomography images in lung disease: Application to COVID-19 patients

PurposeQuantitative metrics in lung computed tomography (CT) images have been widely used, often without a clear connection with physiology. This work proposes a patient-independent model for the estimation of well-aerated volume of lungs in CT images (WAVE).MethodsA Gaussian fit, with mean (Mu.f) and width (Sigma.f) values, was applied to the lower CT histogram data points of the lung to provide the estimation of the well-aerated lung volume (WAVE.f). Independence from CT reconstruction parameters and respiratory cycle was analysed using healthy lung CT images and 4DCT acquisitions. The Gaussian metrics and first order radiomic features calculated for a third cohort of COVID-19 patients were compared with those relative to healthy lungs. Each lung was further segmented in 24 subregions and a new biomarker derived from Gaussian fit parameter Mu.f was proposed to represent the local density changes.ResultsWAVE.f resulted independent from the respiratory motion in 80% of the cases. Differences of 1%, 2% and up to 14% resulted comparing a moderate iterative strength and FBP algorithm, 1 and 3 mm of slice thickness and different reconstruction kernel. Healthy subjects were significantly different from COVID-19 patients for all the metrics calculated. Graphical representation of the local biomarker provides spatial and quantitative information in a single 2D picture.ConclusionsUnlike other metrics based on fixed histogram thresholds, this model is able to consider the inter- and intra-subject variability. In addition, it defines a local biomarker to quantify the severity of the disease, independently of the observer.     

Investigation of inflammation,oxidative stress,and DNA damage in COVID-19 patients

COVID-19 disease, which spreads worldwide, is a disease characterized by widespread inflammation and affects many organs, especially the lungs. The resulting inflammation can lead to reactive oxygen radicals, leading to oxidative DNA damage. The pneumonia severity of 95 hospitalized patients with positive RT-PCR test was determined and divided into three groups: mild, moderate, and severe/critical. Inflammation markers (neutrophil–lymphocyte ratio, serum reactive protein, procalcitonin, etc.) were determined, and IL-10 and IFN-γ measurements were analyzed using the enzyme-linked immunosorbent assay method. In evaluating oxidative damage, total thiol, native thiol, disulfide, and ischemia-modified albumin (IMA) levels were determined by measuring spectrophotometrically. The comet assay method’s percentage of tail DNA obtained was used to determine oxidative DNA damage. As a result, when the mild and severe/critical groups were compared, we found that total thiol, native thiol, and disulfide levels decreased significantly in the severe/critical group due to the increase in inflammation markers and cytokine levels (p < 0.05). We could not detect any significance in IMA levels between the groups (p > 0.05). At the same time, we determined an increase in the tail DNA percent level, that is, DNA damage, due to the increased oxidative effect. As a result, we determined that inflammation and oxidative stress increased in patients with severe pneumonia, and there was DNA damage in these patients.     

Mesenchymal stem cells as living anti-inflammatory therapy for COVID-19 related acute respiratory distress syndrome

Coronavirus disease 2019(COVID-19), a pandemic disease caused by the severe acute respiratory syndrome coronavirus 2(SARS-Co V2), is growing at an exponential rate worldwide. Manifestations of this disease are heterogeneous; however, advanced cases often exhibit various acute respiratory distress syndrome-like symptoms, systemic inflammatory reactions, coagulopathy, and organ involvements. A common theme in advanced COVID-19 is unrestrained immune activation, classically referred to as a cytokine storm, as well as deficiencies in immune regulatory mechanisms such as T regulatory cells. While mesenchymal stem cells(MSCs) themselves are objects of cytokine regulation, they can secrete cytokines to modulate immune cells by inducing antiinflammatory regulatory Treg cells, macrophages and neutrophils; and by reducing the activation of T and B cells, dendritic and nature killer cells. Consequently, they have therapeutic potential for treating severe cases of COVID-19. Here we discuss the unique ability of MSCs, to act as a living antiinflammatory, which can rebalance the cytokine/immune responses to restore equilibrium. We also discuss current MSC trials and present different concepts for optimization of MSC therapy in patients with COVID-19 acute respiratory distress syndrome.     

Cell-Type-Specific Immune Dysregulation in Severely Ill COVID-19 Patients

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Impact of the seventh day nucleated red blood cell count on mortality in COVID-19 intensive care unit patients: A retrospective case-control study

BackgroundCOVID-19 covers a broad clinical spectrum, threatening global health. Although several studies have investigated various prognostic biochemical and hematological parameters, they generally lack specificity and are insufficient for decision-making. Beyond the neonatal period, NRBCs (nucleated red blood cells) in peripheral blood is rare and often associated with malignant neoplasms, bone marrow diseases, and other severe disorders such as sepsis and hypoxia. Therefore, we investigated if NRBCs can predict mortality in hypoxic ICU (Intensive Care Unit) patients of COVID-19.MethodsSeventy-one unvaccinated RT-PCR confirmed COVID-19 ICU patients was divided into those who survived (n=35, mean age=58) and died (n=36, mean age=75). Venous blood samples were collected in K3 EDTA tubes and analyzed on a Sysmex XN-1000 hematology analyzer with semiconductor laser flow cytometry and nucleic acid fluorescence staining method for NRBC analysis. NRBC numbers and percentages of the patients were compared on the first and seventh days of admission to the ICU. Results are reported as a proportion of NRBCs per 100 WBCs NRBCs/100 WBC (NRBC% and as absolute NRBC count (NRBC #, × 109/L).ResultsNRBC 7th-day count and % values were statistically higher in non-survival ones. The sensitivity for 7th day NRBC value <0.01 (negative) was 86.11%, the specificity was 48.57%, for <0.02; 75.00%, and 77.14%, for <0.03; 61.11%, and 94.60%.ConclusionsIn conclusion, our results indicate that NRBC elevation (>0.01) significantly predicts mortality in ICU hospitalized patients due to COVID-19. Worse, a high mortality rate is expected, especially with NRBC values of >0.03.     

Increased sTREM-1 plasma concentrations are associated with poor clinical outcomes in patients with COVID-19

Patients with sepsis display increased concentrations of sTREM-1 (soluble Triggering Receptor Expressed on Myeloid cells 1), and a phase II clinical trial focusing on TREM-1 modulation is ongoing. We investigated whether sTREM-1 circulating concentrations are associated with the outcome of patients with coronavirus disease 2019 (COVID-19) to assess the role of this pathway in COVID-19. This observational study was performed in two independent cohorts of patients with COVID-19. Plasma concentrations of sTREM-1 were assessed after ICU admission (pilot cohort) or after COVID-19 diagnosis (validation cohort). Routine laboratory and clinical parameters were collected from electronic patient files. Results showed sTREM-1 plasma concentrations were significantly elevated in patients with COVID-19 (161 [129–196] pg/ml) compared to healthy controls (104 [75–124] pg/ml; P<0.001). Patients with severe COVID-19 needing ICU admission displayed even higher sTREM-1 concentrations compared to less severely ill COVID-19 patients receiving clinical ward-based care (235 [176–319] pg/ml and 195 [139–283] pg/ml, respectively, P = 0.017). In addition, higher sTREM-1 plasma concentrations were observed in patients who did not survive the infection (326 [207–445] pg/ml) compared to survivors (199 [142–278] pg/ml, P<0.001). Survival analyses indicated that patients with higher sTREM-1 concentrations are at higher risk for death (hazard ratio = 3.3, 95%CI: 1.4–7.8). In conclusion, plasma sTREM-1 concentrations are elevated in patients with COVID-19, relate to disease severity, and discriminate between survivors and non-survivors. This suggests that the TREM-1 pathway is involved in the inflammatory reaction and the disease course of COVID-19, and therefore may be considered as a therapeutic target in severely ill patients with COVID-19.     

Cytokine storm intervention in the early stages of COVID-19 pneumonia

Clinical intervention in patients with corona virus disease 2019 (COVID-19) has demonstrated a strong upregulation of cytokine production in patients who are critically ill with SARS-CoV2-induced pneumonia. In a retrospective study of 41 patients with COVID-19, most patients with SARS-CoV-2 infection developed mild symptoms, whereas some patients later developed aggravated disease symptoms, and eventually passed away because of multiple organ dysfunction syndrome (MODS), as a consequence of a severe cytokine storm. Guidelines for the diagnosis and treatment of SARS-CoV-2 infected pneumonia were first published January 30^th, 2020; these guidelines recommended for the first time that cytokine monitoring should be applied in severely ill patients to reduce pneumonia related mortality. The cytokine storm observed in COVID-19 illness is also an important component of mortality in other viral diseases, including SARS, MERS and influenza. In view of the severe morbidity and mortality of COVID-19 pneumonia, we review the current understanding of treatment of human coronavirus infections from the perspective of a dysregulated cytokine and immune response.     

A linear mixed model to estimate COVID-19-induced excess mortality

The Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID-19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the year-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.