Prediction of lethal outcomes in COVID-19 cases based on the results chest computed tomography

Morozov, S. P.; Gombolevskiy, Victor A.; Chernina, Valeria; Blokhin, Ivan; Мокиенко, О. А.; Vladzimirskiy, A. V.; Белевский, А. С.; Protsenko, D.N.; Лысенко, М. А.; Зайратьянц, О. В.; Nikonov, E. L.

doi:10.21292/2075-1230-2020-98-6-7-14

Cited by 20 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Only 10% of the patients with moderate COVID-19 had 75% lung damage, 27% had about 25%, and in 63% lung damage exceeded 25%. Lesion volume in both lungs was scored on a semi-quantitative scale according the to the Russian national guidelines ( 45 , 46 ) from CT-0 to CT-4 with a 25% step (CT-0: 0%, CT-1: 25%, CT-2: 50%, CT-3: 75%, CT-4: 100%). Half of the severely affected patients were admitted to the ICU for 9 ± 3.6 days.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Predicts Severity of COVID-19 Based on Correlation of Exaggerated Monocyte Activation, Excessive Organ Damage and Hyperinflammatory Syndrome: A Prospective Clinical Study

et al. 2021

View full text Add to dashboard Cite

BackgroundPrediction of the severity of COVID-19 at its onset is important for providing adequate and timely management to reduce mortality.ObjectiveTo study the prognostic value of damage parameters and cytokines as predictors of severity of COVID-19 using an extensive immunologic profiling and unbiased artificial intelligence methods.MethodsSixty hospitalized COVID-19 patients (30 moderate and 30 severe) and 17 healthy controls were included in the study. The damage indicators high mobility group box 1 (HMGB1), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), alanine aminotransferase (ALT), extensive biochemical analyses, a panel of 47 cytokines and chemokines were analyzed at weeks 1, 2 and 7 along with clinical complaints and CT scans of the lungs. Unbiased artificial intelligence (AI) methods (logistic regression and Support Vector Machine and Random Forest algorithms) were applied to investigate the contribution of each parameter to prediction of the severity of the disease.ResultsOn admission, the severely ill patients had significantly higher levels of LDH, IL-6, monokine induced by gamma interferon (MIG), D-dimer, fibrinogen, glucose than the patients with moderate disease. The levels of macrophage derived cytokine (MDC) were lower in severely ill patients. Based on artificial intelligence analysis, eight parameters (creatinine, glucose, monocyte number, fibrinogen, MDC, MIG, C-reactive protein (CRP) and IL-6 have been identified that could predict with an accuracy of 83−87% whether the patient will develop severe disease.ConclusionThis study identifies the prognostic factors and provides a methodology for making prediction for COVID-19 patients based on widely accepted biomarkers that can be measured in most conventional clinical laboratories worldwide.

show abstract

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Predicts Severity of COVID-19 Based on Correlation of Exaggerated Monocyte Activation, Excessive Organ Damage and Hyperinflammatory Syndrome: A Prospective Clinical Study

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Detailed information about both patient and control cohorts can be found in ( 24 ). Patient lung damage was quantified by a semi-quantitative scale for the evaluation of lesion volume in lungs based on a chest CT scan, applied according to the national guidelines ( 25 , 26 ) as follows: chest CT grade 0 (no low abnormalities on the chest CT), chest CT grade 1 (25% of the lungs were affected), chest CT grade 2 (50% of the lungs were affected), chest CT grade 3 (75% of the lungs were affected), chest CT grade 4 (100% of the lungs were affected).…”

Section: Methodsmentioning

confidence: 99%

Severity of SARS-CoV-2 infection is associated with high numbers of alveolar mast cells and their degranulation

et al. 2022

View full text Add to dashboard Cite

BackgroundThe systemic inflammatory response post-SARS-CoV-2 infection increases pro-inflammatory cytokine production, multi-organ damage, and mortality rates. Mast cells (MC) modulate thrombo-inflammatory disease progression (e.g., deep vein thrombosis) and the inflammatory response post-infection.ObjectiveTo enhance our understanding of the contribution of MC and their proteases in SARS-CoV-2 infection and the pathogenesis of the disease, which might help to identify novel therapeutic targets.MethodsMC proteases chymase (CMA1), carboxypeptidase A3 (CPA3), and tryptase beta 2 (TPSB2), as well as cytokine levels, were measured in the serum of 60 patients with SARS-CoV-2 infection (30 moderate and 30 severe; severity of the disease assessed by chest CT) and 17 healthy controls by ELISA. MC number and degranulation were quantified by immunofluorescent staining for tryptase in lung autopsies of patients deceased from either SARS-CoV-2 infection or unrelated reasons (control). Immortalized human FcεR1+c-Kit+ LUVA MC were infected with SARS-CoV-2, or treated with its viral proteins, to assess direct MC activation by flow cytometry.ResultsThe levels of all three proteases were increased in the serum of patients with COVID-19, and strongly correlated with clinical severity. The density of degranulated MC in COVID-19 lung autopsies was increased compared to control lungs. The total number of released granules and the number of granules per each MC were elevated and positively correlated with von Willebrand factor levels in the lung. SARS-CoV-2 or its viral proteins spike and nucleocapsid did not induce activation or degranulation of LUVA MC in vitro.ConclusionIn this study, we demonstrate that SARS-CoV-2 is strongly associated with activation of MC, which likely occurs indirectly, driven by the inflammatory response. The results suggest that plasma MC protease levels could predict the disease course, and that severe COVID-19 patients might benefit from including MC-stabilizing drugs in the treatment scheme.

show abstract

“…3 а В общей выборке больных изучалось влияние следующих факторов на интервал «дебют заболевания -выявление КТ-4»: пол, возраст, наличие сопутствующих заболеваний. Было выявлены статистически значимое (р=0.033) увеличение сроков у мужчин: медиана составила 11 суток (7-16), у женщин 10 суток (6)(7)(8)(9)(10)(11)(12)(13)(14)(15). При исследовании зависимости интервала от коморбидности больных (за коморбидность принималось наличие 2-х и более значимых хронических заболеваний), достоверных различий не получено (р=0.076).…”

Section: таблица №1unclassified

Dynamics of Ct-Picture Changes in Patients With a Critical Degree of Lung Tissue Damage Caused by the Sars-Cov-2 Virus

Komissarova,

Godzenko,

Rumyantsev

et al. 2022

Rejr

View full text Add to dashboard Cite

t was found that death from coronavirus infection is the more likely, the higher the degree of damage to the lung on the visual scale "CT 0-4". Thus, patients with pulmonary involvement of 75% or more are predictively poor in terms of outcome. Assessment of the influence of the rate of disease progression to CT-4 on mortality and the search for factors affecting this indicator may be of practical importance.The purpose of this study is comparison of the duration of progression of the CT picture to a critical degree in various "waves" of the pandemic, the search for factors influencing the interval of "debut diseases -CT 4", as well as the assessment of the state of the lung tissue after a total process in a long period of time.Materials and methods: a retrospective analysis of 708 patients (226 in the first wave, 482 in the second wave), hospitalized at City Clinical Hospital No. 15 and having a critical degree of lung. 25 discharged patients underwent a control CT scan 6 and 12 months after discharge from the hospital.Results: The study groups were comparable in terms of demographic characteristics and the degree of CT at admission in hospital. When comparing the interval "onset of the disease -detection of CT-4", no significant differences between the "waves" were found (p = 0.372). When assessing the CT-picture in the delayed period, there were no significant differences in the frequency of changes between the groups; an almost complete regression of inflammatory changes was observed. In the study of the entire sample, an increase in the interval "onset of the disease -detection of CT-4" (p = 0.033) was found in men (11 days (7-16)) compared with women for 10 days (6-15), and in discharged patients (12 days (8-18) compared with deaths (10 days (6-15)) (p <0.001). There was a significant reduction in the progression time with increasing age of patients (p = 0.003) Conclusions: Considering the data obtained on the possible influence of the timing of the progression of the CT picture on the mortality of patients, as well as the results of long-term observation, it is advisable to search for modifiable risk factors for the rapid progression of the disease.

show abstract

Prediction of lethal outcomes in COVID-19 cases based on the results chest computed tomography

Cited by 20 publications

References 9 publications

Artificial Intelligence Predicts Severity of COVID-19 Based on Correlation of Exaggerated Monocyte Activation, Excessive Organ Damage and Hyperinflammatory Syndrome: A Prospective Clinical Study

Artificial Intelligence Predicts Severity of COVID-19 Based on Correlation of Exaggerated Monocyte Activation, Excessive Organ Damage and Hyperinflammatory Syndrome: A Prospective Clinical Study

Severity of SARS-CoV-2 infection is associated with high numbers of alveolar mast cells and their degranulation

Dynamics of Ct-Picture Changes in Patients With a Critical Degree of Lung Tissue Damage Caused by the Sars-Cov-2 Virus

Contact Info

Product

Resources

About