We observed that for the transitions between stages 1 and 2 and between stages 2 and 3 (CD4 cell counts below 500 cells/mm3), the acquisition of a higher CD4 cell count occurred more rapidly than the corresponding loss in every virologic group (P < 0.0001). For instance, in the “durable virologic suppression” group (group 1), the mean transition time from stage 3 to stage 2 was twice as long as the reverse transition from stage 2 to stage 3 (12.6 and 6.7 months, respectively; P < 0.0001). Starting from stage 3 (CD4 cell counts below 500 cells/mm3), the trend gets reversed in most of the virologic groups, and we observed acquisition times higher than loss times between stages 3 and 4 and stages 4 and 5.
Overall, the mean transition times from stage i to stage i + 1 (increase in CD4+ cell count) were shorter in group 1 than in group 2 and also shorter in group 3 than in group 4. In the same way, the reverse process (decrease in CD4 cell count) was slower in group 1 than in group 2 and also slower in group 3 than in group 4.
Figure 3 represents the estimated mean time course of CD4 cell count increase in each group. The time course of CD4 cell counts increased continuously in each group. These increases were larger in groups 1, 2, and 3 than in group 4, showing that the smaller the virologic rebound, the stronger was the CD4 cell increment (P < 0.0001). For instance, patients in group 2 had a larger increase in CD4 cell counts than patients in group 3 (P < 0.0001).
In groups 1, 2, and 3, the increases in the CD4 cell count between baseline and month 2 were 22, 18, and 19 cells/mm3, respectively, compared with only 5 cells/mm3 in group 4. The CD4 cell count increments between months 2 and 6 in groups 1, 2, 3, and 4 were 26, 20, 11, and 2 cells/mm3, respectively. The rate of increase fell after month 6, and the slope of the increase differed from one virologic group to another. For instance, the CD4 cell count increment between months 6 and 12 was 14 cells/mm3 in group 1, 7 cells/mm3 in groups 2 and 3, and 1 cell/mm3 in group 4. Finally, the increment between month 12 and month 24 was even smaller (5, 1, and 3 cells/mm3 in groups 1, 2, and 3, respectively, and 0 cells/mm3 in group 4).
Thus, the CD4 cell count increased on HAART, regardless of the level of virologic rebound, albeit more rapidly in patients with durable virologic suppression or low-level rebound than in patients with intermediate- or high-level rebound. Finally, the CD4 cell count stabilized after an initial increase, and the higher the level of virologic rebound, the earlier this stabilization occurred.
This study focused on the influence of virologic rebounds on the time course of the CD4 cell count in patients on HAART. Overall, CD4 cell counts tended to increase continuously for 18 months after an initial fall in viral load to below 500 copies/mL. This increase was observed regardless of the degree of virologic rebound, although it was minimal in patients in whom viral load rebounded to values above 10,000 copies/mL.
Because of the chosen exclusion criteria, data on patients who were lost to follow-up, patients who died, and patients who may not have attended outpatient visits because of slow disease progression were not analyzed, and this may have led us to underestimate or overestimate the mean CD4 cell count increment. Another limitation of this study is the lack of information on treatment interruption. Nevertheless, lengthy treatment interruptions were likely to be uncommon during the study period (up to the end of 1999). In addition, short treatment interruptions due to adverse events were unlikely to affect our results, because we studied the influence of plasma viral load variations during the year following the first measured value below 500 copies/mL.
Despite these limitations, this assessment of immunologic outcome, being based on a large cohort and relatively lengthy follow-up, is likely to be more reliable than studies based on clinical trials involving smaller numbers of patients followed up for a maximum of 1 to 2 years after treatment initiation.
The Markov models are often used to model a longitudinal process, particularly to model the CD4 cell count dynamics. These models, contrary to regression models, do not require any preliminary parametric assumptions such as a preset time point for the change in the slope of CD4 cell count for linear models. These multistate models also proved useful in the context of a database with censored data.
Our study was based on a Markov assumption that the rate of progression from one CD4 cell count stage to the next is independent of the rate of progression through previous stages: the time-homogeneous assumption. This simplification is required to obtain tractable forms of the transition probabilities and, thus, the likelihood function.
The stages of our Markov model were defined by categorizing continuous changes in the CD4 cell count, which, as in all models based on categorization of continuous variables, inevitably entails a certain loss of information. We chose to model CD4 cell count kinetics on the basis of the following clinically relevant categories: <200, 200 through 349, 350 through 499, 500 through 649, and >650 cells/mm3.
We initially used a Markov model with 6 stages: stages 1 and 2 represented CD4 cell counts between 0 and 100 cells/mm3 and between 100 and 200 cells/mm3, respectively, and stages 3 through 6 represented the same count intervals as those used in the final model. The information available for estimating the transition intensities, particularly between stages 1 and 2, appeared to be insufficient in the 6-stage model, however. Stages 1 and 2 were thus combined into a single stage (<200 cells/mm3), yielding the final 5-stage model.
Our results show that it seems easier to reconstitute an immunologic response with a CD4 cell count below the 500 cells/mm3 threshold value. This is in line with the fact that the slope of CD4 cell count increase tends to become smaller over time. 21
Our results also suggest that the CD4 cell count continues to increase on HAART despite virologic rebound but that the rate of increase varies significantly with the degree of virologic rebound. No fall in mean CD4 cell count was observed in these virologic rebound groups, even among patients in whom viral load rebounded to more than 10,000 copies/mL.
Our results agree with those of Le Moing et al 22 for patients with low-level virologic rebound (<5000 copies/mL) but not for patients with high-level rebound (>10,000 copies/mL). Indeed, these authors found that the CD4 cell count continued to increase during the second year of follow-up when the plasma HIV-1 RNA level at the time of rebound was below 5000 copies/mL, whereas it stabilized when the plasma HIV-1 RNA level was between 5000 and 10,000 copies/mL and fell significantly when virologic rebound exceeded 10,000 copies/mL. This discrepancy could be related to treatment modification after rebound, viral resistance, viral fitness, or differences in the modeling approach.
Other studies showed an increase in CD4 cell count among patients who had detectable plasma HIV-1 RNA while on HAART. Deeks et al 9 suggested that patients who remained on even partially effective therapy had more durable CD4 cell count responses than patients who discontinued therapy and that this effect was independent of the level of viral replication. Kaufmann et al 23 reported that some patients adhering to HAART may show an increase in their CD4 cell count despite persistent viremia, suggesting that virologic and immunologic responses can diverge. Deeks et al 9 reported that among patients who failed to achieve durable viral suppression (<500 copies/mL), there was a median interval of 3 years between the onset of virologic failure and the return of the absolute CD4 cell count to the pretreatment level.
Our results do not necessarily hold for NNRTI-containing HAART. Indeed, virologic rebounds among NNRTI-treated patients quickly involve acquisition of mutations responsible for a complete resistance of the virus to NNRTIs. 19 Moreover, viral mutations and subsequent viral fitness are different when rebound of viral replication occurred on NNRTI therapy compared with PI therapy.
We found that the rate of increase in the CD4 cell count tended to be inversely proportional to the level of virologic rebound. Taken together, our findings suggest that in terms of further CD4 count increase, a threshold of 5000 copies/mL is a practical value with which to define virologic failure and to consider a treatment switch. The risk of viral mutation is unknown in patients with this level of viral load on HAART, however. In patients with low or moderate virologic rebound while on a PI regimen or HAART who have difficulties with treatment observance or have previously been exposed to multiple antiretroviral regimens, delaying the treatment switch may spare some therapeutic options for future use without markedly compromising the CD4 cell count.
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Keywords:Copyright © 2003 Wolters Kluwer Health, Inc. All rights reserved.
CD4 lymphocytes; virologic rebound; highly active antiretroviral therapy (HAART); Markov model; Bayesian inference