CD4+ cell dynamics in untreated HIV-1 infection: overall rates, and effects of age, viral load, sex and calendar time

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 200cells/l 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 500cells/l compartment after seroconversion. Set-point viral load (SPVL) was an important factor: individuals with at least 5 log(10)copies/ml took 5.37 years to reach CD4(+) cell count less than 200cells/l compared with 15.76 years for SPVL less than 4 log(10)copies/ml. Conclusion:Many individuals already have CD4(+) cell count below 500cells/l after seroconversion. SPVL strongly influences the rate of CD4(+) decline. Treatment guidelines should consider measuring SPVL, whereas mathematical models should incorporate SPVL stratification. Copyright (C) 2015 Wolters Kluwer Health, Inc. All rights reserved.