Gamma-glutamyl-transferase may predict COVID-19 outcomes in hospitalised patients

Benan Kasapoglu1, Ahmet Yozgat2, Alpaslan Tanoglu3

  • 1Department of Gastroenterology, Faculty of Medicine, Lokman Hekim University, Ankara, Turkey.

Insights

Liver function tests, including creatinine and gamma-glutamyl transferase (GGT), along with D-dimer levels upon hospital admission, can predict intensive care unit (ICU) admission and mortality in COVID-19 patients. These readily available and inexpensive tests aid in early risk stratification.

Area of Science:

  • * Internal Medicine
  • * Infectious Diseases
  • * Clinical Chemistry

Background:

  • * COVID-19, a global pandemic, presents a wide spectrum of clinical severity.
  • * Predictive markers for severe outcomes in hospitalized COVID-19 patients are crucial for resource allocation and patient management.

Purpose of the Study:

  • * To determine the predictive value of liver function tests (LFTs) on hospital admission for COVID-19 patient outcomes.
  • * To identify laboratory markers that predict the need for intensive care unit (ICU) admission and mortality.

Main Methods:

  • * A multicentric retrospective study involving 269 hospitalized adult COVID-19 patients.
  • * Data collected included demographics, medical history, and laboratory findings at admission.
  • * Patients were categorized based on ICU admission during hospitalization.

Main Results:

  • * Older age was associated with both ICU admission and mortality (P < .001).
  • * Elevated serum D-dimer, creatinine, and gamma-glutamyl transferase (GGT) levels at admission independently predicted ICU hospitalization.
  • * These same elevated markers also predicted mortality in COVID-19 patients.

Conclusions:

  • * Admission laboratory markers, including creatinine, GGT, and D-dimer, are significant predictors of ICU need and mortality in COVID-19.
  • * The accessibility and low cost of these tests facilitate their use in developing predictive formulas.
  • * Early identification of high-risk patients using these parameters can optimize intensive care resource utilization.
Abstract

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