title: CD4+ cell dynamics in untreated HIV-1 infection: overall rates, and effects of age, viral load, sex and calendar time.
abstract: BACKGROUND
  CD4 cell count is a key measure of HIV disease progression, and the basis of successive international guidelines for treatment initiation. CD4 cell dynamics are used in mathematical and econometric models for evaluating public health need and interventions. Here, we estimate rates of CD4 decline, stratified by relevant covariates, in a form that is clinically transparent and can be directly used in such models.
  
  
  METHODS
  We analyse the AIDS Therapy Evaluation in the Netherlands cohort, including individuals with date of seroconversion estimated to be within 1 year and with intensive clinical follow-up prior to treatment initiation. Owing to the fact that CD4 cell counts are intrinsically noisy, we separate the analysis into long-term trends of smoothed CD4 cell counts and an observation model relating actual CD4 measurements to the underlying smoothed counts. We use a monotonic spline smoothing model to describe the decline of smoothed CD4 cell counts through categories CD4 above 500, 350-500, 200-350 and 200 cells/μl or less. We estimate the proportion of individuals starting in each category after seroconversion and the average time spent in each category. We examine individual-level cofactors which influence these parameters.
  
  
  RESULTS
  Among untreated individuals, the time spent in each compartment was 3.32, 2.70, 5.50 and 5.06 years. Only 76% started in the CD4 cell count above 500 cells/μl compartment after seroconversion. Set-point viral load (SPVL) was an important factor: individuals with at least 5 log10 copies/ml took 5.37 years to reach CD4 cell count less than 200 cells/μl compared with 15.76 years for SPVL less than 4 log10 copies/ml.
  
  
  CONCLUSIONS
  Many individuals already have CD4 cell count below 500 cells/μl after seroconversion. SPVL strongly influences the rate of CD4 decline. Treatment guidelines should consider measuring SPVL, whereas mathematical models should incorporate SPVL stratification.
authors.affiliation: aDepartment of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK bStichting HIV Monitoring, Amsterdam, The Netherlands.
authors.forename: Anne
authors.initials: A
authors.lastname: Cori
authors.forename: Michael
authors.initials: M
authors.lastname: Pickles
authors.forename: Ard
authors.initials: A
authors.lastname: van Sighem
authors.forename: Luuk
authors.initials: L
authors.lastname: Gras
authors.forename: Daniela
authors.initials: D
authors.lastname: Bezemer
authors.forename: Peter
authors.initials: P
authors.lastname: Reiss
authors.forename: Christophe
authors.initials: C
authors.lastname: Fraser
mesh_terms: D000328:Adult; D000367:Age Factors; D018791:CD4 Lymphocyte Count; D015496:CD4-Positive T-Lymphocytes / Q000276:immunology*; D015331:Cohort Studies; D005260:Female; D015658:HIV Infections / Q000276:immunology* / Q000821:virology*; D015497:HIV-1 / Q000302:isolation & purification*; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D009426:Netherlands; D012737:Sex Factors; D013997:Time Factors; D019562:Viral Load