posted on 2021-01-20, 21:29authored byJeany Delafiori, Luiz Cláudio Navarro, Rinaldo Focaccia Siciliano, Gisely Cardoso de Melo, Estela Natacha
Brandt Busanello, José Carlos Nicolau, Geovana Manzan Sales, Arthur Noin de Oliveira, Fernando Fonseca Almeida Val, Diogo Noin de Oliveira, Adriana Eguti, Luiz Augusto dos Santos, Talia Falcão Dalçóquio, Adriadne Justi Bertolin, Rebeca Linhares Abreu-Netto, Rocio Salsoso, Djane Baía-da-Silva, Fabiana G Marcondes-Braga, Vanderson Souza Sampaio, Carla Cristina Judice, Fabio Trindade
Maranhão Costa, Nelson Durán, Mauricio Wesley Perroud, Ester Cerdeira Sabino, Marcus Vinicius
Guimarães Lacerda, Leonardo Oliveira Reis, Wagner José Fávaro, Wuelton Marcelo Monteiro, Anderson Rezende Rocha, Rodrigo Ramos Catharino
COVID-19
is still placing a heavy health and financial burden worldwide.
Impairment in patient screening and risk management plays a fundamental
role on how governments and authorities are directing resources, planning
reopening, as well as sanitary countermeasures, especially in regions
where poverty is a major component in the equation. An efficient diagnostic
method must be highly accurate, while having a cost-effective profile.
We combined a machine learning-based algorithm with mass spectrometry
to create an expeditious platform that discriminate COVID-19 in plasma
samples within minutes, while also providing tools for risk assessment,
to assist healthcare professionals in patient management and decision-making.
A cross-sectional study enrolled 815 patients (442 COVID-19, 350 controls
and 23 COVID-19 suspicious) from three Brazilian epicenters from April
to July 2020. We were able to elect and identify 19 molecules related
to the disease’s pathophysiology and several discriminating
features to patient’s health-related outcomes. The method applied
for COVID-19 diagnosis showed specificity >96% and sensitivity
>83%,
and specificity >80% and sensitivity >85% during risk assessment,
both from blinded data. Our method introduced a new approach for COVID-19
screening, providing the indirect detection of infection through metabolites
and contextualizing the findings with the disease’s pathophysiology.
The pairwise analysis of biomarkers brought robustness to the model
developed using machine learning algorithms, transforming this screening
approach in a tool with great potential for real-world application.