Dataset Information

Specific Features of the Coagulopathy Signature in Severe COVID-19 Pneumonia.

ABSTRACT: Rationale: COVID-19 displays distinct characteristics that suggest a unique pathogenesis. The objective of this study was to compare biomarkers of coagulopathy and outcomes in COVID-19 and non-COVID-19 patients with severe pneumonia. Methods: Thirty-six non-COVID-19 and 27 COVID-19 non-immunocompromised patients with severe pneumonia were prospectively enrolled, most requiring intensive care. Clinical and biological characteristics (including plasma biomarkers of coagulopathy) were compared. Results: At similar baseline severity, COVID-19 patients required mechanical ventilation (MV) for significantly longer than non-COVID-19 patients (p = 0.0049) and more frequently developed venous thrombotic complications (p = 0.031). COVID-19 patients had significantly higher plasma concentrations of soluble VCAM1 (sVCAM1) (5,739 ± 3,293 vs. 3,700 ± 2,124 ng/ml; p = 0.009), but lower levels of D-dimers, vWF-A2, sICAM1, sTREM1, VEGF, and P-selectin, compared to non-COVID-19 patients. Principal component analysis identified two main patterns, with a clear distinction between non-COVID-19 and COVID-19 patients. Multivariable regression analysis confirmed that sVCAM1 rising levels were independently associated with a longer duration of MV. Finally, we identified close correlations between sVCAM1 and some features of COVID-19 immune dysregulation (ie. CXCL10, GM-CSF, and IL-10). Conclusion: We identified specific features of the coagulopathy signature in severe COVID-19 patients, with higher plasma sVCAM1 levels, that were independently associated with the longer duration of mechanical ventilation. Clinical Trial Registration:ClinicalTrials.gov, identifier: NCT03505281.

SUBMITTER: Blot M

PROVIDER: S-EPMC8371474 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:OBJECTIVE:The aim of this study was to investigate the clinical and computed tomography (CT) features associated with severe and critical coronavirus disease 2019 (COVID-19) pneumonia. MATERIALS AND METHODS:Eighty-three patients with COVID-19 pneumonia including 25 severe/critical cases and 58 ordinary cases were enrolled. The chest CT images and clinical data of them were reviewed and compared. The risk factors associated with disease severity were analyzed. RESULTS:Compared with the ordinary patients, the severe/critical patients had older ages, higher incidence of comorbidities, cough, expectoration, chest pain, and dyspnea. The incidences of consolidation, linear opacities, crazy-paving pattern, and bronchial wall thickening in severe/critical patients were significantly higher than those of the ordinary patients. Besides, severe/critical patients showed higher incidences of lymph node enlargement, pericardial effusion, and pleural effusion than the ordinary patients. The CT scores of severe/critical patients were significantly higher than those of the ordinary patients (P < 0.001). Receiver operating characteristic curve showed that the sensitivity and specificity of CT score were 80.0% and 82.8%, respectively, for the discrimination of the 2 types. The clinical factors of age older than 50 years, comorbidities, dyspnea, chest pain, cough, expectoration, decreased lymphocytes, and increased inflammation indicators were risk factors for severe/critical COVID-19 pneumonia. Computed tomography findings of consolidation, linear opacities, crazy-paving pattern, bronchial wall thickening, high CT scores, and extrapulmonary lesions were features of severe/critical COVID-19 pneumonia. CONCLUSIONS:There are significant differences in clinical symptoms, laboratory examinations, and CT manifestations between the ordinary patients and the severe/critical patients. Many factors are related to the severity of the disease, which can help clinicians to judge the severity of the patient and evaluate the prognosis.

Dataset Information

Specific Features of the Coagulopathy Signature in Severe COVID-19 Pneumonia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets