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Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
Published on: February 16, 2022
Do the Kinetics of Antibody Responses Predict Clinical Outcome in Hospitalized Patients With Moderate-to-Severe
George Pavlidis1, Christos F Kampolis2, Garyfallia Perlepe3
1Department of Emergency Medicine, Hippokration General Hospital, Athens, Greece; geo_pavlidis@yahoo.gr.
Background/Aim:
The relationship between the kinetics of antibody responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the severity of Coronavirus Disease 2019 (COVID-19) is poorly understood. The aim of the present study was to investigate whether serum SARS-CoV-2 antibody kinetics serve as an early predictor of clinical deterioration or recovery in hospitalized patients with COVID-19.
Patients And Methods:
In this prospective observational study, 102 consecutive patients (median age: 60 years, 58% males) with symptomatic COVID-19 infection diagnosed by real-time polymerase chain reaction assay, hospitalized in two tertiary hospitals, were included. Rapid test for qualitative detection of immunoglobulin M (IgM) and immunoglobulin G (IgG) SARS-CoV-2 antibodies was performed at pre-defined time intervals during hospitalization (days: 0, 3, 7, 10, 14, 21 and 28).
Results:
During a 3-month follow-up period after COVID-19 disease onset, a total of 87 patients were discharged, 12 patients were intubated and entered the Intensive Care Unit, and three patients died. The median time for seroconversion was 10 days for IgM and 12 days for IgG post onset of symptoms. Univariate logistic regression analysis found no associations between IgM or IgG positivity and clinical outcomes or complications during hospitalization for COVID-19 infection. Diabetes and dyslipidemia were the only clinical risk factors predictive of COVID-19-related complications during hospitalization.
Conclusion:
SARS-CoV-2 antibody responses do not predict clinical outcome in hospitalized patients with moderate-to-severe COVID-19 infection.
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