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Elevated Anti-SARS-CoV-2 Antibodies and IL-6, IL-8, MIP-1β, Early Predictors of Severe COVID-19.

ABSTRACT: Viral and host immune kinetics during acute COVID-19 and after remission of acute symptoms need better characterization. SARS-CoV-2 RNA, anti-SARS-CoV-2 IgA, IgM, and IgG antibodies, and proinflammatory cytokines were measured in sequential samples from hospitalized COVID-19 patients during acute infection and six months following diagnosis. Twenty four laboratory confirmed COVID-19 patients with mild/moderate and severe COVID-19 were included. Most were males (83%) with a median age of 61 years. Twenty one percent were admitted to the intensive care unit (ICU) and eight of them (33.3%) met the criteria for severe COVID-19 disease. A delay in SARS-CoV-2 levels' decline during the first six days of follow up, and viral load persistence until month 3 were related to severe COVID-19, but not viral load levels at the diagnosis. Higher levels of anti-SARS-CoV-2 IgA, IgM, IgG and the cytokines IL-6, IL-8 and MIP-1β at the diagnosis time were related to the severe COVID-19 outcome. Higher levels of MIP-1β, IL-1β, MIP-1α and IFN-γ were observed at month 1 and 3 during mild/moderate disease, compared to severe COVID-19. IgG persisted at low levels after six months of diagnosis. In conclusion, higher concentrations of IgA, IgM, and IgG, and IL-6, IL-8 and MIP-1β are identified as early predictors of COVID-19 severity, whereas no significant association is found between baseline SARS-COV-2 viral load and COVID-19 severity.

SUBMITTER: Codina H

PROVIDER: S-EPMC8624589 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and Objectives: The severe forms of SARS-CoV-2 pneumonia are associated with acute hypoxic respiratory failure and high mortality rates, raising significant challenges for the medical community. The objective of this paper is to present the importance of early quantitative evaluation of radiological changes in SARS-CoV-2 pneumonia, including an alternative way to evaluate lung involvement using normal density clusters. Based on these elements we have developed a more accurate new predictive score which includes quantitative radiological parameters. The current evolution models used in the evaluation of severe cases of COVID-19 only include qualitative or semi-quantitative evaluations of pulmonary lesions which lead to a less accurate prognosis and assessment of pulmonary involvement. Materials and Methods: We performed a retrospective observational cohort study that included 100 adult patients admitted with confirmed severe COVID-19. The patients were divided into two groups: group A (76 survivors) and group B (24 non-survivors). All patients were evaluated by CT scan upon admission in to the hospital. Results: We found a low percentage of normal lung densities, PaO2/FiO2 ratio, lymphocytes, platelets, hemoglobin and serum albumin associated with higher mortality; a high percentage of interstitial lesions, oxygen flow, FiO2, Neutrophils/lymphocytes ratio, lactate dehydrogenase, creatine kinase MB, myoglobin, and serum creatinine were also associated with higher mortality. The most accurate regression model included the predictors of age, lymphocytes, PaO2/FiO2 ratio, percent of lung involvement, lactate dehydrogenase, serum albumin, D-dimers, oxygen flow, and myoglobin. Based on these parameters we developed a new score (COV-Score). Conclusions: Quantitative assessment of lung lesions improves the prediction algorithms compared to the semi-quantitative parameters. The cluster evaluation algorithm increases the non-survivor and overall prediction accuracy.COV-Score represents a viable alternative to current prediction scores, demonstrating improved sensitivity and specificity in predicting mortality at the time of admission.

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Elevated Anti-SARS-CoV-2 Antibodies and IL-6, IL-8, MIP-1β, Early Predictors of Severe COVID-19.

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