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.

Introdution: COVID-19 is associated with an increased risk of thrombotic events. However, the contribution of platelet reactivity (PR) to the aetiology of the increased thrombotic risk associated with COVID-19 remains unclear. Our aim was to evaluate PR in stable patients diagnosed with COVID-19 and hospitalized with respiratory symptoms (mainly dyspnoea and dry cough), in comparison with a control group comprised of non-hospitalized healthy controls. Methods: Observational, case control study that included patients with confirmed COVID-19 (COVID-19 group, n = 60) and healthy individuals matched by age and sex (control group, n = 60). Multiplate electrode aggregometry (MEA) tests were used to assess PR with adenosine diphosphate (MEA-ADP, low PR defined as\53 AUC), arachidonic acid (MEA-ASPI, low PR\86 AUC) and thrombin receptoractivating peptide 6 (MEA-TRAP, low PR\97 AUC) in both groups. Results: The rates of low PR with MEA-ADP were 27.5% in the COVID-19 group and 21.7% in the control group (OR = 1.60, p = 0.20); with MEA-ASPI, the rates were, respectively, 37.5% and 22.5% (OR = 3.67, p\0.001); and with MEA-TRAP, the incidences were 48.5% and 18.8%, respectively (OR = 9.58, p\0.001). Levels of D-dimer, fibrinogen, and plasminogen activator inhibitor 1 (PAI-1) were higher in the COVID-19 group in comparison with the control group (all p\0.05). Thromboelastometry was utilized in a subgroup of patients and showed a hypercoagulable state in the COVID-19 group.

No abstract

SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS–PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.

COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play 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 instrumental analysis using 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 cohort of 728 patients (369 confirmed COVID-19 and 359 controls) was enrolled from three Brazilian epicentres (Sao Paulo capital, Sao Paulo countryside and Manaus) in the months of April, May, June and July 2020. We were able to elect and identify 21 molecules that are related to the disease's pathophysiology and 26 features to patient's health-related outcomes. With specificity >97% and sensitivity >83% from blinded data, this screening approach is understood as a tool with great potential for real-world application.