CD4 Discordance as a Predictor of Liver Fibrosis in People Coinfected with Human Immune Deficiency Virus/Hepatitis C

Ahmed Cordie1,2, Ahmed M Kamel3, Rahma Mohamed1,2

  • 1Department of Endemic Medicine and Hepatology, Faculty of Medicine, Cairo University, Cairo, Egypt.

Insights

CD4 discordance, a measure of immune status discrepancy, is significantly linked to liver fibrosis in patients with concurrent hepatitis C virus (HCV) and human immune deficiency virus (HIV) infection. Predictive models using CD4 values aid in assessing liver fibrosis risk.

Area of Science:

  • Hepatology
  • Infectious Diseases
  • Immunology

Background:

  • Liver fibrosis diagnosis is complex in patients coinfected with hepatitis C virus (HCV) and human immune deficiency virus (HIV).
  • CD4 discordance, the discrepancy between absolute CD4 count and CD4 percentage, may influence liver fibrosis progression in this population.

Purpose of the Study:

  • To investigate the association between CD4 discordance and liver fibrosis in HIV/HCV-coinfected adults.
  • To evaluate the predictive performance of different models for identifying significant liver fibrosis.

Main Methods:

  • Cross-sectional study of 198 adults with HIV/HCV coinfection.
  • Noninvasive liver fibrosis assessment using transient elastography.
  • CD4 discordance defined by absolute CD4 count and CD4 percentage discrepancies; analyzed using logistic regression and ROC curves.

Main Results:

  • High CD4 discordance was significantly correlated with liver fibrosis (p < .001).
  • Prevalence of significant fibrosis was higher in individuals with high CD4 discordance (65.5%) compared to low (14.5%) or concordant (13.5%) values.
  • High CD4 discordance strongly predicted significant fibrosis (OR = 11.48, p < .001).

Conclusions:

  • CD4 discordance is a valuable predictive factor for significant liver fibrosis in HIV/HCV coinfection.
  • Models combining CD4 count and percentage offer improved diagnostic utility for liver fibrosis.
  • Further research is needed to refine predictive models for clinical application in managing liver fibrosis.