Evaluation of seven SARS-CoV-2 rapid IgM/IgG tests

José Luis Pelegrino Martínez de la Cotera, Licel de los Ángeles Rodriguez Lay, Yahisel Tejero Suárez, Odalys Valdés Ramírez, Sonia Resik Aguirre, Odalys Calderón Fuentes, Yaumara Ugarte Pérez, Juan Carlos de León González, Darling Danay Morales Verdecia, María Guadalupe Guzmán Tirado

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Introduction: In late 2019, a new coronavirus was detected in China causing an acute respiratory illness known as COVID-2019.

Objective. Evaluate seven commercial systems for the rapid detection of antibodies to determine their sensitivity, specificity and robustness in our conditions to be used by the National Health System.

Methods: Seven systems were evaluated for the detection of IgM/IgG antibodies. Evaluation panel with samples from negative individuals, sera from other pathologies prior to the pandemic and from positive patients with the disease were conformed.

Results: General sensitivity figures range between 25 and 88%, with the Realy Tech and Deep Blue systems showied the best results. The specificity for both was 100%. The IgM positive rate according to Realy Tech or Deep Blue increased to 94.1 or 81.8% in the late stage of the disease.

Conclusions: Realy Tech and Deep Blue systems detected IgM/IgG in serum and in whole blood with adequate sensitivity and specificity. Cross-reactivity does not seem to be a problem. Serology in the case of COVID-19 cannot be used as a diagnostic but it allows epidemiological surveillance to know the immune status of populations. It’s essential to analyze the immune response against the infection to carry out epidemiological characterization and potentially inform individual risk of future disease and the study of potential vaccines.

Palabras clave

COVID‐19; lateral flow immunoassay; point‐of‐care testing; rapid IgM/IgG; SARS-CoV-2

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Copyright (c) 2022 José Luis Pelegrino Martínez, Licel de los Angeles Rodriguez Lay, Yahisel Tejero Suarez, Odalys Valdes, Sonia Resik Aguirre, Odalys Calderon Fuentes, Yaumara Ugarte Perez, Juan Carlos de Leon González, Darling Danay Morales Verdecia, María Guadalupe Guzmán Tirado

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Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial 4.0 Internacional.