Risk Factors of Severe Disease and Methods for Clinical Outcome Prediction in Patients with COVID-19 (Review)
Large population studies using statistical analysis and mathematical computer modeling could be an effective tool in studying COVID-19. The use of prognostic scales developed using correlation of changes in clinical and laboratory parameters and morphological data, can help in early prediction of di...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russia
2022-02-01
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| Series: | Общая реаниматология |
| Subjects: | |
| Online Access: | https://www.reanimatology.com/rmt/article/view/2177 |
| Summary: | Large population studies using statistical analysis and mathematical computer modeling could be an effective tool in studying COVID-19. The use of prognostic scales developed using correlation of changes in clinical and laboratory parameters and morphological data, can help in early prediction of disease progression and identification of patients with high risk of unfavorable outcome.Aim of the review. To assess the risk factors for severe course and unfavorable outcome of COVID-19 and to evaluate the existing tools for predicting the course and outcome of the novel coronavirus infection. PubMed, Medline, and Google Scholar were searched for the relevant sources. This review contains information on existing tools for assessing the prognosis and outcome of the disease, along with the brief data on the etiology, pathogenesis of the novel coronavirus infection and the known epidemiological, clinical and laboratory factors affecting its course.Conclusion. It is essential to develop predictive models tailored to specific settings and capable of continuous monitoring of the situation and making the necessary adjustments. The discovery of new and more sensitive early markers and developing marker-based predictive assessment tools could significantly impact improving the outcomes of COVID-19. |
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| ISSN: | 1813-9779 2411-7110 |