1
|
Villagrana-Bañuelos KE, Maeda-Gutiérrez V, Alcalá-Rmz V, Oropeza-Valdez JJ, Herrera-Van Oostdam AS, Castañeda-Delgado JE, López JA, Borrego Moreno JC, Galván-Tejada CE, Galván-Tejeda JI, Gamboa-Rosales H, Luna-García H, Celaya-Padilla JM, López-Hernández Y. COVID-19 Outcome Prediction by Integrating Clinical and Metabolic Data using Machine Learning Algorithms. Rev Invest Clin 2022; 74:314-327. [PMID: 36546894 DOI: 10.24875/ric.22000182] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. Objectives To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university. Methods A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19. Results The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables. Conclusions ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.
Collapse
Affiliation(s)
| | | | | | - Juan J Oropeza-Valdez
- Metabolomics and Proteomics Laboratory, Universidad Autónoma de Zacatecas (UAZ), Zacatecas, Zac., Mexico
| | - Ana S Herrera-Van Oostdam
- Doctorate Program, Ciencias Biomédicas Básicas, Centro de Investigación en Ciencias de la Salud y Biomedicina, Universidad Autónoma de San Luis Potosí, SLP, Mexico
| | - Julio E Castañeda-Delgado
- Consejo Nacional de Ciencia y Tecnología (CONACyT), Instituto Mexicano de Seguridad Social, Zacatecas, Zac., Mexico
| | - Jesús Adrián López
- MicroRNAs Laboratory, Biological Sciences Academic Unit, UAZ, Zacatecas, Zac., Mexico
| | - Juan C Borrego Moreno
- Department of Epidemiology, Hospital General de Zona 1 Emilio Varela Luján, Instituto Mexicano del Seguro Social, Zacatecas, Zac., Mexico
| | | | | | | | | | | | | |
Collapse
|