2. Academic Validation
  3. Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning

Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning

  • Comput Biol Med. 2022 Jul:146:105659. doi: 10.1016/j.compbiomed.2022.105659.
Alexandre de Fátima Cobre 1,  Monica Surek 2,  Dile Pontarolo Stremel 3,  Mariana Millan Fachi 4,  Helena Hiemisch Lobo Borba 5,  Fernanda Stumpf Tonin 6,  Roberto Pontarolo 7
Affiliations

Affiliations

  • 1 Pharmaceutical Sciences Postgraduate Program, Universidade Federal Do Paraná, Curitiba, Brazil. Electronic address: [email protected].
  • 2 Pharmaceutical Sciences Postgraduate Program, Universidade Federal Do Paraná, Curitiba, Brazil. Electronic address: [email protected].
  • 3 Department of Forest Engineering and Technology, Universidade Federal Do Paraná, Curitiba, Brazil. Electronic address: [email protected].
  • 4 Pharmaceutical Sciences Postgraduate Program, Universidade Federal Do Paraná, Curitiba, Brazil. Electronic address: [email protected].
  • 5 Department of Pharmacy, Universidade Federal Do Paraná, Curitiba, Brazil. Electronic address: [email protected].
  • 6 Pharmaceutical Sciences Postgraduate Program, Universidade Federal Do Paraná, Curitiba, Brazil; H&TRC- Health & Technology Research Center, ESTeSL, Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, Lisbon, Portugal. Electronic address: [email protected].
  • 7 Department of Pharmacy, Universidade Federal Do Paraná, Curitiba, Brazil. Electronic address: [email protected].
PMID: 35751188 DOI: 10.1016/j.compbiomed.2022.105659
Abstract

Objective: To implement and evaluate machine learning (ML) algorithms for the prediction of COVID-19 diagnosis, severity, and fatality and to assess biomarkers potentially associated with these outcomes.

Material and methods: Serum (n = 96) and plasma (n = 96) samples from patients with COVID-19 (acute, severe and fatal illness) from two independent hospitals in China were analyzed by LC-MS. Samples from healthy volunteers and from patients with pneumonia caused by Other viruses (i.e. negative RT-PCR for COVID-19) were used as controls. Seven different ML-based models were built: PLS-DA, ANNDA, XGBoostDA, SIMCA, SVM, LREG and KNN.

Results: The PLS-DA model presented the best performance for both datasets, with accuracy rates to predict the diagnosis, severity and fatality of COVID-19 of 93%, 94% and 97%, respectively. Low levels of the metabolites ribothymidine, 4-hydroxyphenylacetoylcarnitine and uridine were associated with COVID-19 positivity, whereas high levels of N-acetyl-glucosamine-1-phosphate, cysteinylglycine, methyl isobutyrate, l-ornithine and 5,6-dihydro-5-methyluracil were significantly related to greater severity and fatality from COVID-19.

Conclusion: The PLS-DA model can help to predict SARS-CoV-2 diagnosis, severity and fatality in daily practice. Some biomarkers typically increased in COVID-19 patients' serum or plasma (i.e. ribothymidine, N-acetyl-glucosamine-1-phosphate, l-ornithine, 5,6-dihydro-5-methyluracil) should be further evaluated as prognostic indicators of the disease.

Keywords

Biomarker; COVID 19; Diagnosis; Fatality; Machine learning; Severity.

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