Predictors of lethal outcomes in severe cases of a new coronavirus infection COVID-19

ABSTRACT

Background. The spread of the new coronavirus infection COVID-19 has already become one of the main problems of national healthcare systems around the world. Until now, it has not been possible to find drugs with sufficient etiotropic activity for COVID-19, and therefore, it is important to determine new points of application for pathogenetic therapy in relation to this pathology. Aim. To identify the predictors of an unfavorable outcome of a severe course of COVID-19 infection to determine the prognosis of the clinical course and optimize treatment tactics using succinates. Material and methods. A retrospective observational study of 46 cases of treatment with a severe form of the disease on the basis of a monohospital for the treatment of patients with a new coronavirus infection was conducted. All patients had comobrid pathology (median Charlson index - 3 points). The most common ones were encephalopathy of mixed genesis, diabetes mellitus, coronary heart disease, arterial hypertension, alimentary-constitutional obesity. We assessed the relationship between indicators of initial status and mortality in patients, and indicators with a statistically significant relationship were selected as predictors. Statistical processing of the results was carried out in the IBM SPSS v. 23.0, ROC analysis was used to find the relationship between quantitative predictors and lethal outcome. Results. Among the treated parameters, the most significant influence on the risk of death was found in arterial anion gap (odds ratio 28.78;p <0.017) and fibrinogen level (odds ratio 22.20;p <0.01). To a lesser extent, the level of urea, aspartate aminotransferase, and the Charlson comorbidity index had an effect on the prognosis of a lethal outcome. The identified predictors of an unfavorable outcome of a severe course of COVID-19 infection can be used to predict the clinical course and build treatment tactics based on the received information. Conclusion. Predictors of poor outcomes in severe COVID-19 infections include arterial anion gap and fibrinogen levels, and to a lesser extent, urea levels, aspartate aminotransferase levels, and the Charlson comorbidity index. © Eco-Vector, 2023. All rights reserved.