title: CD4+/CD8+ T cell ratio for diagnosis of HIV-1 infection in infants: Women and Infants Transmission Study.
abstract: OBJECTIVE
  In this study, we tested the hypothesis that the CD4(+)/CD8(+) T cell ratio could predict HIV infection status in HIV-exposed infants.
  
  
  METHODS
  CD4(+)/CD8(+) T cell ratios were determined from data for live-born singleton infants who had been prospectively enrolled in the Women and Infants Transmission Study. Data for 2208 infants with known HIV infection status (179 HIV-infected and 2029 uninfected infants) were analyzed.
  
  
  RESULTS
  Receiver operating characteristic curves indicated that the CD4(+)/CD8(+) T cell ratio performed better than the proportion of CD4(+) T cells for diagnosis of HIV infection as early as 2 months of age, and this relationship was unaffected by adjustment for maternal race/ethnicity, infant birth weight, gestational age, and gender. At 4 months of age, 90% specificity for HIV diagnosis was associated with 60% sensitivity. For ease of use, graphical estimates based on cubic splines for the time-dependent parameters in a Box-Cox transformation (L), the median (M), and the coefficient of variation (S) were used to create LMS centile curves to show the sensitivity and specificity of CD4(+)/CD8(+) T cell ratios in HIV-infected and uninfected infants until 12 months of age. At 6 months of age, a simplified equation that incorporated sequential CD4(+)/CD8(+) T cell ratios and hematocrit values resulted in improved receiver operating characteristic curves, with 94% positive predictive value and 98% negative predictive value. The positive and negative predictive values remained above 90% in simulated infant populations over a wide range of HIV infection prevalence values.
  
  
  CONCLUSIONS
  In the absence of virological diagnosis, a presumptive diagnosis of HIV infection status can be made on the basis of CD4(+)/CD8(+) T cell ratios in HIV-1-exposed infants after 2 months of age; sensitivity and specificity can be improved at 6 months by using a discriminant analysis equation.
authors.affiliation: Department of Microbiology and Immunology, University of Miami, Miller School of Medicine, Miami, Florida 33136, USA. spahwa@med.miami.edu
authors.forename: Savita
authors.initials: S
authors.lastname: Pahwa
authors.forename: Jennifer S
authors.initials: JS
authors.lastname: Read
authors.forename: Wanrong
authors.initials: W
authors.lastname: Yin
authors.forename: Yvonne
authors.initials: Y
authors.lastname: Matthews
authors.forename: William
authors.initials: W
authors.lastname: Shearer
authors.forename: Clemente
authors.initials: C
authors.lastname: Diaz
authors.forename: Kenneth
authors.initials: K
authors.lastname: Rich
authors.forename: Hermann
authors.initials: H
authors.lastname: Mendez
authors.forename: Bruce
authors.initials: B
authors.lastname: Thompson
mesh_terms: D000704:Analysis of Variance; D015415:Biomarkers / Q000097:blood; D018414:CD8-Positive T-Lymphocytes / Q000276:immunology*; D016022:Case-Control Studies; D015331:Cohort Studies; D005260:Female; D005500:Follow-Up Studies; D015658:HIV Infections / Q000175:diagnosis / Q000453:epidemiology / Q000635:transmission*; D006679:HIV Seropositivity; D006801:Humans; D015994:Incidence; D007231:Infant, Newborn; D018445:Infectious Disease Transmission, Vertical / Q000706:statistics & numerical data*; D008297:Male; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D011247:Pregnancy; D011251:Pregnancy Complications, Infectious / Q000175:diagnosis* / Q000453:epidemiology; D011336:Probability; D011446:Prospective Studies; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D017678:Sex Distribution
chemical_list: D015415:Biomarkers