Inflammatory and Hypercoagulable Biomarkers and Clinical Outcomes in COVID-19 Patients

Hiroki Kitakata1, Shun Kohsaka1, Shunsuke Kuroda2

  • 1Department of Cardiology, Keio University School of Medicine, Tokyo 160-8582, Japan.

Insights

Systemic inflammation and hypercoagulopathy in COVID-19 patients with cardiovascular disease (CVD) can be predicted by C-reactive protein (CRP) and D-dimer levels. Elevated CRP and D-dimer at admission significantly increase in-hospital mortality risk, aiding rapid risk assessment.

Area of Science:

  • Cardiology
  • Infectious Diseases
  • Clinical Biomarkers

Background:

  • Systemic inflammation and hypercoagulopathy are key in COVID-19, especially in patients with cardiovascular disease (CVD).
  • The predictive value of combined inflammatory and coagulation biomarkers at admission for COVID-19 patient outcomes is not well-established.

Purpose of the Study:

  • To investigate the association between admission levels of C-reactive protein (CRP) and D-dimer and in-hospital outcomes in COVID-19 patients with pre-existing CVD.
  • To assess if a cumulative evaluation of CRP and D-dimer can predict in-hospital mortality.

Main Methods:

  • A Japanese nationwide retrospective observational study (CLAVIS-COVID registry) enrolled hospitalized COVID-19 patients with CVD.
  • Patients were stratified by tertiles of admission CRP and D-dimer levels.
  • Multivariable Cox proportional hazard models were used to analyze in-hospital mortality.

Main Results:

  • In 461 patients, in-hospital mortality was 16.5%. Mortality rates increased with higher CRP and D-dimer tertiles (p < 0.001 and p = 0.001, respectively).
  • Patients in the lowest CRP/D-dimer tertiles had very low mortality (1.4%).
  • Elevated CRP and D-dimer combined significantly predicted in-hospital mortality (HR, 2.97; 95% CI, 1.57-5.60).

Conclusions:

  • Combined assessment of admission CRP and D-dimer levels can effectively stratify risk in hospitalized COVID-19 patients with CVD.
  • This biomarker combination offers a framework for rapid risk estimation of in-hospital outcomes.
  • Early identification of high-risk patients can potentially guide timely interventions.

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