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


Journal

Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250

Informations de publication

Date de publication:
07 2022
Historique:
received: 20 02 2022
revised: 11 05 2022
accepted: 18 05 2022
entrez: 25 6 2022
pubmed: 26 6 2022
medline: 29 6 2022
Statut: ppublish

Résumé

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. 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. 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. 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.

Identifiants

pubmed: 35751188
pii: S0010-4825(22)00451-6
doi: 10.1016/j.compbiomed.2022.105659
pmc: PMC9123826
pii:
doi:

Substances chimiques

Biomarkers 0
Phosphates 0
Ornithine E524N2IXA3
Glucosamine N08U5BOQ1K
Thymine QR26YLT7LT

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

105659

Informations de copyright

Copyright © 2022 Elsevier Ltd. All rights reserved.

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Auteurs

Alexandre de Fátima Cobre (A)

Pharmaceutical Sciences Postgraduate Program, Universidade Federal Do Paraná, Curitiba, Brazil. Electronic address: alexandrecobre@gmail.com.

Monica Surek (M)

Pharmaceutical Sciences Postgraduate Program, Universidade Federal Do Paraná, Curitiba, Brazil. Electronic address: monicasurek13@gmail.com.

Dile Pontarolo Stremel (DP)

Department of Forest Engineering and Technology, Universidade Federal Do Paraná, Curitiba, Brazil. Electronic address: dile.stremel@gmail.com.

Mariana Millan Fachi (MM)

Pharmaceutical Sciences Postgraduate Program, Universidade Federal Do Paraná, Curitiba, Brazil. Electronic address: marianamfachi@gmail.com.

Helena Hiemisch Lobo Borba (HH)

Department of Pharmacy, Universidade Federal Do Paraná, Curitiba, Brazil. Electronic address: helena.hlb@gmail.com.

Fernanda Stumpf Tonin (FS)

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: stumpf.tonin@ufpr.br.

Roberto Pontarolo (R)

Department of Pharmacy, Universidade Federal Do Paraná, Curitiba, Brazil. Electronic address: pontarolo@ufpr.br.

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