[Informativeness estimation for the main clinical and laboratory parameters in patients with severe COVID-19].
Ter Arkh
; 94(11): 1225-1233, 2022 Dec 26.
Article
in Ru
| MEDLINE
| ID: mdl-37167158
ABSTRACT
AIM:
To conduct a retrospective assessment of the clinical and laboratory data of patients with severe forms of COVID-19 hospitalized in the intensive care and intensive care unit, in order to assess the contribution of various indicators to the likelihood of death. MATERIALS ANDMETHODS:
A retrospective assessment of data on 224 patients with severe COVID-19 admitted to the intensive care unit was carried out. The analysis included the data of biochemical, clinical blood tests, coagulograms, indicators of the inflammatory response. When transferring to the intensive care units (ICU), the indicators of the formalized SOFA and APACHE scales were recorded. Anthropometric and demographic data were downloaded separately.RESULTS:
Analysis of obtained data, showed that only one demographic feature (age) and a fairly large number of laboratory parameters can serve as possible markers of an unfavorable prognosis. We identified 12 laboratory features the best in terms of prediction procalcitonin, lymphocytes (absolute value), sodium (ABS), creatinine, lactate (ABS), D-dimer, oxygenation index, direct bilirubin, urea, hemoglobin, C-reactive protein, age, LDH. The combination of these features allows to provide the quality of the forecast at the level of AUC=0.85, while the known scales provided less efficiency (APACHE AUC=0.78, SOFA AUC=0.74).CONCLUSION:
Forecasting the outcome of the course of COVID-19 in patients in ICU is relevant not only from the position of adequate distribution of treatment measures, but also from the point of view of understanding the pathogenetic mechanisms of the development of the disease.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Sepsis
/
COVID-19
Type of study:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
Ru
Journal:
Ter Arkh
Year:
2022
Document type:
Article