L Ratiani^1,2, T Sanikidze², G Ormotsadze^2,3, E Vashakidze^1,2, V Shoshiashvili^1,2*, E Pachkoria^1,2, M Rurua^1,2, E Khuchua^1,2, L Gabunia^1,2, K Machavariani^1,2 and T Megrelishvili^1,2

¹First University Clinic of Tbilisi State Medical University, Georgia
²Tbilisi State Medical University, Georgia
³I. Beritashvili Center of Experimental Biomedicine, Georgia

*Corresponding Author: V Shoshiashvili, First University Clinic of Tbilisi State Medical University, Georgia.

Received: July 16, 2021; Published: August 28, 2021

Abstract

  The number of patients with COVID-19 is currently rapidly increasing globally. Most of the patients with COVID-19 have a favorable prognosis, but some have rapidly progressed to severe and critical conditions with respiratory distress syndrome, coagulation dysfunction, multiple organ failure, etc.

The purpose of our study was to identify early clinical-laboratory indicators of possible complications in COVID-19 patients.

  The information of a total of 106 patients with confirmed COVID-19 (the positive result of the nucleic acid test by real-time fluorescence RT-PCR), who were admitted to First University Clinic of Tbilisi State Medical University (Georgia) (1.04.20-1.5.20), was collected. The clinical-laboratory data (saturation index, pH, hemoglobin, leukocytes (lymphocytes and neutrophils) and thrombocytes content, prothrombin, D-Dimer, CRP, LDH, AST, ALT, bilirubin, urea level in the blood, and CT-rate) were collected at the time of patient admission to the hospital.

  In accordance with the severity of the course of the disease during hospitalization, all patients were divided into two groups - without complications and with severe complications, when respiratory and other organs failure occur, requiring observation and treatment in intensive care units (ICU).

  Exploratory and confirmatory (K-mean Clustering, ANOVA, Xi2), data analysis methods were used to reveal the statistical reliability of the difference between the mean values of the parameters. To identify the optimal complex predictors of the risk of complications, Binary Logistic Regression analysis and Receiver operating characteristic (ROC) curve analysis were performed.

  According to the results of the study, it was found that the prognostic criterion of further complications in patients with COVID-19, developed on the basis of a linear function of parameter values (saturation index, neutrophil count, and CT-Rate) at the moment of hospitalization is characterized by high accuracy. Also, the ways for further refining the complex prognostic criterion were identified. In particular, the use of generalized nonlinear models with random effects in regression will allow to 1) increase the number of clinical- laboratory characteristics, 2) consider the effects of interaction between them, 3) to assess the level of uncertainty and its sources.

  The studied cohort was quite limited (106 patients) and given the importance of comorbidities in the development of complications of COVID-19, it is unlikely to be representative of the general population. It is important to increase the studied cohort's capacity, which will allow us to take into account the effects of comorbidity, the degree of representativeness, and perform a comparative analysis of various therapeutic approaches, generalize the results to other populations.

Keywords: COVID -19; Clinical-laboratory; Indicators; Complication; Predictor

References

1. Machase E. “China coronavirus: mild but infectious cases may make it hard to control outbreaks. Report warns”. BMJ 368 (2020): m325.
2. W Han., et al. “The course of clinical diagnosis and treatment of a case infected with coronavirus disease 2019”. Journal of Medical Virology5 (2020): 461-463.
3. N Tang., et al. “Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia”. Journal of Thrombosis and Haemostasis4 (2020): 844-847.
4. Kandel N., et al. “Health security capacities in the context of COVID-19 outbreak: an analysis of International Health Regulations annual report data from 182 countries”. Lancet 10229 (2020): 1047-1053.
5. Shim E., et al. “Transmission potential and severity of COVID-19 in South Korea”. International Journal of Infectious Diseases 93 (2020): 339-344.
6. Cowling BJ., et al. “Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study”. Lancet Public Health5 (2020): e279-288.
7. Xu Z., et al. “Pathological findings of COVID-19 associated with acute respiratory distress syndrome”. Lancet Respiratory Medicine4 (2020): 420-422.
8. General office of the national health commission of China. “Covid-19 diagnostic and therapeutic regimen (trial 7th, edition)”. Journal of Cardiopulmonary Rehabilitation and Prevention 2 (2020): 103-107.
9. Lee KS. “Pneumonia associated with 2019 novel coronavirus: can computer tomographic findings help predict the prognosis of the disease?” Korean Journal of Radiology3 (2020): 257-258.
10. Jayawant N Mandrekar. “Receiver Operating Characteristic Curve in Diagnostic Test Assessment”. Thoracolumbar Oncology 5 (2010): 1315-1316.
11. Tomasz J Guzik., et al. “COVID-19 and the cardiovascular system: implications for risk assessment, diagnosis, and treatment options”. Cardiovascular Research10 (2020): 1666-1687.
12. Arun Samidurai and Anindita Das. “Cardiovascular Complications Associated with COVID-19 and Potential Therapeutic Strategies”. International Journal of Molecular Sciences 21 (2020): 6790.
13. You Shang., et al. “Management of critically ill patients with COVID-19 in ICU: statement from front-line intensive care experts in Wuhan, China”. Annals of Intensive Care 10 (2020): 73.

Citation

Citation: V Shoshiashvili., et al. “The Informative Value of Several Clinical-laboratory Characteristics at the Time of Hospitalization in Terms of Prognosis of Disease Complication with COVID-19 Patients”.Acta Scientific Medical Sciences 5.9 (2021): 143-149.

Copyright

Copyright: © 2021 V Shoshiashvili., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.