Prediction of COVID-19 mortality using machine learning strategies and a large-scale panel of plasma inflammatory proteins: A cohort study.

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.