Clinical and laboratory features of COVID-19: Predictors of severe prognosis

Aliye Bastug1, Hurrem Bodur1, Serpil Erdogan2

  • 1Department of Infectious Diseases and Clinical Microbiology, Health Science University Turkey, Ankara City Hospital, 06800 Ankara, Turkey.

Insights

Laboratory markers like D-dimer, neutrophil-lymphocyte ratio (NLR), and C-reactive protein (CRP) can predict severe COVID-19 outcomes. A decrease in large unstained cells (%LUC) also indicates a worse prognosis in patients with coronavirus disease 2019.

Area of Science:

  • Medical Research
  • Infectious Diseases
  • Hematology

Background:

  • Coronavirus disease 2019 (COVID-19) rapidly spread globally.
  • Understanding clinical and laboratory data is crucial for managing COVID-19 patients.

Purpose of the Study:

  • To identify clinical and laboratory predictors of severe prognosis in hospitalized COVID-19 patients.
  • To compare prognostic factors between critically ill (ICU) and non-critically ill patients.

Main Methods:

  • Retrospective analysis of 191 COVID-19 patients hospitalized between March and April 2020.
  • Comparison of admission variables between patients requiring intensive care unit (ICU) support and those who did not.
  • Logistic regression and ROC analysis to determine the predictive power of various parameters.

Main Results:

  • ICU patients were older with more comorbidities, exhibiting lower lymphocytes, hemoglobin, and albumin.
  • ICU patients showed higher leucocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet-lymphocytes ratio (PLR), urea, creatinine, AST, LDH, and D-dimer.
  • Decreased percentage of large unstained cells (%LUC) and increased D-dimer were strong predictors of severe prognosis, with D-dimer, CRP, and NLR showing the highest AUC in ROC analysis.

Conclusions:

  • Clinical and laboratory parameters at admission are vital for predicting severe COVID-19 prognosis.
  • Decreased %LUC and increased D-dimer, NLR, and CRP are powerful laboratory indicators of severe disease.
  • Identifying these predictors aids in patient follow-up and understanding COVID-19 pathophysiology.
Abstract

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