Status of Inflammatory and Coagulation Factors in COVID-19 and Its Relation with the Disease Severity

Atoosa Gharib1, Zahra Nematollahi2, Behrang Kazeminejad1

  • 1Department of Pathology and Laboratory Medicine, Clinical Research Development Center, Shahid Modarres Educational Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Tanaffos
|August 23, 2024
PubMed

Insights

Inflammatory and coagulation factors like fibrinogen, D-dimer, protein C, and protein S predict COVID-19 severity. Lower protein C levels were the strongest indicator for ICU admission, highlighting their prognostic value in severe disease.

Area of Science:

  • Medical Research
  • Infectious Diseases
  • Hematology

Background:

  • Inflammation exacerbates COVID-19, and thromboembolic events can occur post-recovery.
  • Prognostic factors for COVID-19 severity remain debated.
  • This study investigated inflammatory and coagulation markers for predicting disease severity.

Purpose of the Study:

  • To evaluate the prognostic role of specific inflammatory and coagulation factors in predicting COVID-19 severity.
  • To determine if these factors correlate with the need for intensive care unit (ICU) admission.

Main Methods:

  • A cross-sectional study of 46 adult COVID-19 patients.
  • Assessed coagulation profiles (D-dimer, protein S, protein C, fibrinogen) and inflammatory markers (interleukin-6).
  • Measured serum levels using automated analyzers and ELISA techniques.

Main Results:

  • 45.7% of patients required ICU admission.
  • Significant differences in fibrinogen, D-dimer, protein C, and protein S levels were observed between ICU and non-ICU groups.
  • Decreased protein C levels showed the highest predictive value for disease severity and ICU admission.

Conclusions:

  • Fibrinogen, D-dimer, protein C, and protein S levels are confirmed predictors of severe COVID-19.
  • These markers are crucial for identifying patients at risk of requiring ICU admission.
  • Protein C levels are particularly significant in forecasting COVID-19 severity.
Abstract

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