Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning

Abstract

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.

Figure 1

Study design flowchart. Abbreviations: Hosp, hospitalization; IMV, invasive mechanical ventilation.

Figure 2

End to end process for putative biomarkers determination and diagnosis test generation. (a) MS data acquisition and preparation; (b) Sequential steps of ML data analysis and metabolomics biomarkers validation.

Figure 3

Recursive fitting of mass spectra data followed by model optimization processes allowed the determination of putative biomarkers ranked by ΔJ importance and group contribution. Abbreviations: CE, cholesteryl ester; DG diacylglycerol; DHEA, dehydroepiandrosterone; DeoxyGU, deoxyguanosine; LysoPC, lysophosphatidylcholine; PC, phosphatidylcholine; PE, phosphatidyethanolamine; PG, phosphatidylglycerol; PS, phosphatidylserine; SM, sphingomyelin; TG, triacylglycerol; UNK, unknown.

Figure 4

Proposed role of identified biomarkers in COVID-19 pathophysiology. Abbreviations: ARDS, acute respiratory distress syndrome; COX-2, cyclooxygenase-2, deoxyGU, deoxyguanosine; LPCAT1, lysophosphatidylcholine acyltransferase 1; LysoPC, lysophosphatidylcholine; PC, phosphatidylcholine; PLA₂, phospholipase A₂.