Prediction of COVID-19 mortality using machine learning strategies and a large-scale panel of plasma inflammatory
Luiz Filipe Bastos Mendes1, Henrique Ritter Dal-Pizzol1, Gabriele Prestes2
1Departamento de Bioquímica, Centro de Estudos em Estresse Oxidativo, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
Objective:
To apply machine learning algorithms to generate models capable of predicting mortality in COVID-19 patients, using a large platform of plasma inflammatory mediators.
Desing:
Prospective, descriptive, cohort study.
Setting:
6 intensive care units in 2 hospitals in Southern Brazil.
Patients:
Patients aged > 18 years who were diagnosed with COVID-19 through reverse transcriptase reaction or rapid antigen test.
Interventions:
None.
Main Variables Of Interest:
Demographic and clinical variables, 65 inflammatory biomarkers, mortality.
Results:
Combinations of two or three proteins yield higher predictive value when compared to individual proteins or the full set of the 65 proteins. A proliferation-inducing ligand (APRIL) and cluster of differentiation 40 ligand (CD40L) consistently emerge among the highest-ranking combinations, suggesting a potential synergistic effect in predicting clinical outcomes. The network structure suggested a dysregulated immune response in non-survivors characterized by the failure of regulatory cytokines to temper an overwhelming inflammatory reaction.
Conclusion:
Our results highlight the value of feature selection and careful consideration of biomarker combinations to improve prediction accuracy in COVID-19 patients.
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