Individual Variation in CD4 Cell Count Trajectory among Human Immunodeficiency Virus-infected Men and Women on Long-term Highly Active Antiretroviral Therapy: An Application using a Bayesian Random Change-Point Model

Haitao Chu1, Stephen J. Gange1, Traci E. Yamashita1, Donald R. Hoover2, Joan S. Chmiel3, Joseph B. Margolick4 and Lisa P. Jacobson1

1 Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
2 Institute for Health, Health Care Policy, and Aging Research, Rutgers University, New Brunswick, NJ
3 Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL
4 Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD

Correspondence to Dr. Haitao Chu, Department of Epidemiology, Room E7636, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205 (e-mail: hchu{at}jhsph.edu).

Received for publication January 26, 2005. Accepted for publication May 11, 2005.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
The authors evaluated population- and individual-level CD4-positive T-lymphocyte (CD4 cell) count trajectories over a 7-year period (July 1995–March 2004) following initiation of highly active antiretroviral therapy (HAART) in the Multicenter AIDS Cohort Study and the Women's Interagency HIV Study. The study population included 404 human immunodeficiency virus (HIV)-infected men and 609 HIV-infected women who 1) had a CD4 cell count measurement available from their last pre-HAART study visit, 2) provided at least four post-HAART CD4 cell count measurements, and 3) reported HAART usage for at least 80% of the post-HAART visits. The CD4 cell count trajectory was analyzed by means of a Bayesian random change-point model. The results indicated that CD4 cell count trajectories for long-term frequent HAART users can be well modeled with change points at both the population and individual levels. At the population level, regardless of CD4 cell count before HAART initiation, the gains in CD4 cell count ended approximately 2 years after HAART initiation in both men and women. At the individual level, 35% of men in the Multicenter AIDS Cohort Study versus 25% of women in the Women's Interagency HIV Study had a statistically significant change in CD4 cell count trajectory within 7 years after HAART initiation.

antiretroviral therapy, highly active; Bayes theorem; CD4 lymphocyte count; change point; HIV infections; models, statistical


Abbreviations: AIDS, acquired immunodeficiency syndrome; HAART, highly active antiretroviral therapy; HIV, human immunodeficiency virus; MACS, Multicenter AIDS Cohort Study; WIHS, Women's Interagency HIV Study


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Highly active antiretroviral therapy (HAART) has been shown by both observational studies and clinical trials to dramatically extend the time to development of acquired immunodeficiency syndrome (AIDS) and the time to death in human immunodeficiency virus (HIV)-infected persons (1Go–4Go). The primary mechanism of action is suppression of plasma HIV RNA levels, which in turn allows an increase in CD4-positive T-lymphocyte (CD4 cell) count and function (5Go, 6Go). Although several studies have quantified the gains in CD4 cell counts over the first 2 years after HAART initiation (7Go, 8Go) and from year 3 to year 4 after HAART initiation (9Go–12Go) at the population level, the long-term (i.e., >2 years) CD4 cell count response to HAART remains largely uncharacterized at the individual level. In the Multicenter AIDS Cohort Study (MACS), Tarwater et al. (10Go) reported that, regardless of CD4 cell counts at HAART initiation, there was a significant increase in CD4 cell count during the first 2 years after HAART initiation, followed by stabilization at 2–3.5 years. This suggests the existence of a change point (i.e., a change in CD4 cell count trajectory) around 2 years after HAART initiation, when CD4 cell counts reach a plateau. However, little is known about the long-term CD4 cell count trajectory with HAART usage at the individual level.

We undertook this initiative to characterize long-term patterns of individual CD4 cell count changes after HAART initiation using data from two prospective cohort studies—the MACS and the Women's Interagency HIV Study (WIHS)—and to identify, if any, points at which changes in CD4 cell count trajectories become apparent (i.e., the change point of CD4 cell count slopes). To determine the role of CD4 cell count at HAART initiation in the subsequent trajectories of CD4 cell count, we stratified our analysis according to CD4 cell count within 6 months prior to HAART initiation, with cutoff values at 100, 200, 350, and 500 cells/mm3. We adapted a hierarchical Bayesian change-point model (13Go, 14Go) that allows for individual heterogeneity in the average time of trajectory change from HAART initiation, the average CD4 cell count at the change point (i.e., the posterior intercept of CD4 cell counts at change points), and the average trajectories (i.e., posterior slopes of change) before and after the change points. This hierarchical change-point model "borrows strength" from the data of similar persons, thereby increasing statistical power to find differences between the CD4 slopes before and after the identified change points.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Population and study design
The MACS was initiated in 1983 to study the natural history of HIV type 1 infection among homosexual and bisexual men in the United States. The study design has been previously described (15Go). A total of 2,788 infected participants were either HIV-positive at enrollment (79 percent) or were documented to have acquired HIV infection during follow-up (21 percent). The WIHS is a multicenter prospective cohort study of the natural history of HIV type 1 infection in women, initiated in 1993. Methods and baseline cohort characteristics have been described previously (16Go). A total of 2,074 infected participants were either HIV-positive at enrollment (99 percent) or were documented to have acquired HIV infection during follow-up (1 percent). The MACS and WIHS protocols were approved by the institutional review boards of each of the participating centers, and informed consent was obtained from every participant.

In both the MACS and the WIHS, participants returned every 6 months for clinical visits at which detailed questionnaires and physical examinations were administered and biologic specimens were collected and stored. Use of antiretroviral medications in the preceding 6 months was reported at each semiannual visit, and these data were summarized to determine whether participants reported HAART usage in the preceding 6 months. HAART was defined according to the US Department of Health and Human Services/Kaiser Panel guidelines (7Go, 17Go). The date of HAART initiation was considered the midpoint between the last visit without reported HAART use (last no-HAART) and the first visit at which HAART use was reported (first HAART). Levels of CD4 cells were quantified using standardized flow cytometry (18Go). AIDS was defined as the presence of any clinical condition consistent with the 1993 Centers for Disease Control and Prevention case definition (19Go), but the definition did not include the criterion of only having a CD4 cell count below 200 cells/ml.

We used MACS and WIHS data collected up to March 31, 2004 (end of semiannual visit 39 of the MACS and visit 18 of the WIHS). Furthermore, we restricted the analysis to participants who: 1) initiated HAART on or after July 1, 1995, with less than or equal to 1 year between their last no-HAART visit and their first HAART visit; 2) had a CD4 cell count measurement available from their last pre-HAART visit and at least four post-HAART CD4 cell count measurements; and 3) reported HAART usage for at least 80 percent of the post-HAART visits. Of the HIV-positive participants who contributed data after July 1, 1995, 758 of 1,175 men and 1,282 of 1,942 women reported use of HAART. Of these participants, 404 MACS men (contributing 4,714 person-visits) and 609 WIHS women (contributing 6,843 person-visits) met all eligibility criteria. CD4 cell counts from the visit prior to HAART initiation and all subsequent visits were used in the analysis.

Statistical method: a hierarchical Bayesian change-point model
Although square-root transformation (13Go, 20Go) and fourth-root transformation (21Go) have been used to stabilize variances when longitudinal CD4 cell counts are modeled, we chose logarithmic transformation because this is directly interpretable as the relative (ratio) change over time. Nonparametric smoothed LOWESS curves were fitted to longitudinal measurements of CD4 cell counts on the log10 scale in preliminary analyses to establish population-level trends. We fitted a hierarchical Bayesian change-point model, which took into consideration the correlation among multiple measurements per person, to obtain a more comprehensive picture of the CD4 changes at the individual level.

Let the random variable Yit denote the log10-transformed CD4 cell counts for the ith person measured at time tij, i = 1, ..., N, and j = 1, ..., ni, where ti1 is the time of the last pre-HAART visit, ti2 is the time of the first post-HAART visit, N represents the number of persons, and ni represents the total number of CD4 measurements collected for the ith person. The first stage of the hierarchical Bayesian change-point model assumes that Yij satisfies the linear change-point regression model,

(1)
where CPi denotes the change point for the ith person; (tij – CPi) equals tij – CPi if tij < CPi (i.e., time prior to the change point) and equals zero if tij ≥ CPi; (tij CPi)+ equals zero if tij ≤ CPi and equals tij – CPi if tij > CPi (i.e., time after the change point); and {varepsilon}ij represents the random error in the model prediction for the ith person at the jth time point.

The parameters ßi0 can be interpreted as the estimated log10 CD4 cell counts at CPi, and and represent the relative (proportional) rate of change in CD4 cell counts before and after CPi for the ith person, respectively. The structure of the random error {varepsilon}ij is assumed to be independently and identically distributed as Gaussian N(0, {sigma}2). For illustration, figure 1 depicts a hypothetical person's expected log10 CD4 cell count trajectory over time following HAART initiation. The change point for the ith person, CPi, is unknown. The first segment, ßi0 + ßi1(tij – CPi), shows a rapid increase in the log10 CD4 cell count trajectory immediately following HAART initiation. The second segment, ßi0 + ßi2(tij CPi)+, shows a plateau in the log10 CD4 cell count after some unknown time since HAART initiation.



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FIGURE 1. A hypothetical person's expected log10 CD4-positive T-lymphocyte (CD4 cell) counts over time since initiation of highly active antiretroviral therapy (HAART), with unknown change point CPi and a plateau in CD4 cell count following change. The first segment, ßi0 + ßi1(tij – CPi), shows a rapid increase in log10 CD4 cell count immediately after HAART initiation, where (tij – CPi) equals tij – CPi if tij < CPi and equals zero if tij ≥ CPi. The second segment, ßi0 + ßi2(tij – CPi)+, shows a plateau in log10 CD4 cell count after some unknown time since HAART initiation, where (tij – CPi)+ equals zero if tij < CPi and equals (tij – CPi) if tij ≥ CPi.

 
In a second stage, we model the individual intercepts (ßi0), the slopes before and after CPii1 and ßi2), and the change-point times (CPi) using multivariate linear regression to explain the observed variability between the subjects with respect to their pre-HAART CD4 cell count, as follows:




(2)
where I(·)'s are indicator variables denoting the pre-HAART CD4 cell count categories for each subject. For example, the indicator variable indicates whether the pre-HAART CD4 cell count was less than or equal to 100 cells/mm3. The twenty {alpha} parameters in expression 2 denote the population effects in four sets of five each for baseline CD4 cell count values, and the four bi's denote individual-level effects. Specifically, for each of the five pre-HAART CD4 categories, the first equation in expression 2 models the individual intercepts (ßi0) at the individual change point CPi and thus provides the expected population average of the log10 CD4 cell count ({alpha}0's). The second and third equations model the individual slopes (ßi1 and ßi2) before and after the change point CPi and thus provide the expected population average annual changes in the log10 CD4 cell count ({alpha}1's and {alpha}2's). The fourth equation models the individual change point CPi and provides the expected population average change point times. Thus, represents the expected population average log10 CD4 cell count at the average change point for the population with a pre-HAART CD4 cell count less than or equal to 100 cells/mm3.

As is customary, we assume independence and diffuseness for the prior specification (22Go–24Go), taking (k = 1, 2, ..., 5 and j = 0, 1, 2, 3) independently distributed as with To further stabilize variance, the random effects bij (i = 1, 2, ... N and j = 0, 1, 2) are assumed to be independently distributed as with a hyperprior ~ {gamma}(10–3, 10–3) for each pre-HAART CD4 cell count stratum (k = 1, 2, ..., 5). Analogous to the study by Gange et al. (25Go, 26Go), two CD4 cell counts were required before and after the unknown change point CPi to ensure that the slopes before and after the change point were evaluated using at least two observations. To take this into consideration, the prior distribution of the random effect bi3 is specified in such a way that CPi is uniformly distributed within the time interval of ti2 and for the ith person. The prior distribution of the precision—that is, the inverse of the variance—for the homogeneous random error {varepsilon}ij, {sigma}–2, is assumed to be {gamma}(10–3, 10–3).

One advantage of Bayesian hierarchical modeling is its ability to estimate multidimensional parameters using Markov chain Monte Carlo methods. When the conditional distribution of each variable given the others is relatively simple to specify, the Gibbs sampling algorithm can be applied to even very complex joint distributions (13Go, 14Go, 22Go, 27Go). We used the Gibbs sampling algorithm built into the noncommercial statistical software WinBUGS (28Go) to obtain the posterior samples. The Markov chain analyses were run two times with different starting values, with a single chain monitored during each run, and the Brooks, Gelman, and Rubin statistics (27Go, 29Go) were used to diagnose convergence of the Markov chains. The results were based on samples of 20,000 values from the posterior densities following a burn-in of 10,000 iterations.

To summarize the trajectory for each individual, we further dichotomized participants by their long-term CD4 response to HAART using the triplet posterior means (i.e., and )—that is, the posterior expectation of parameters, which is analogous to the point estimates in the frequentist paradigm. "Sustained CD4 response" to HAART was defined as:

All other responses were classified as "no sustained response." We used 350 cells/mm3 as the cutpoint of the posterior mean of the change point as a criterion for defining "sustained response," since postponing HAART treatment until the CD4 cell count is less than 350 cells/mm3 is recommended in the treatment guidelines (17Go).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Table 1 presents descriptive statistics for the study population at HAART initiation according to pre-HAART CD4 cell count and cohort. Among the 404 men, the median number of CD4 cell count measurements was 13, with a median of 6.12 post-HAART follow-up years; 344 (85 percent) were Caucasian, 36 (9 percent) were African-American, and 21 (5 percent) were Latino. Among the 609 women, the median number of CD4 cell count measurements was 12, with a median of 5.70 post-HAART follow-up years; 126 (21 percent) were Caucasian, 316 (52 percent) were African-American, and 151 (25 percent) were Latina. The proportions of participants on antiretroviral therapy before HAART initiation, the calendar years in which HAART was initiated, and all-cause mortality during follow-up for the five CD4 subgroups were similar in the two cohorts. Compared with women in the WIHS, men in the MACS were approximately 4–5 years older, had 20–30 more CD4 cells/mm3, had higher HIV RNA levels, and had a lower proportion with an opportunistic infection or malignancy diagnostic of AIDS (19Go) at the visit within 6 months before HAART initiation.


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TABLE 1. Characteristics of the study population at initiation of highly active antiretroviral therapy (HAART), according to pre-HAART CD4-positive T-lymphocyte (CD4 cell) count and cohort, Multicenter AIDS Cohort Study and Women's Interagency HIV Study, July 1995–March 2004

 
Figure 2 shows the longitudinal measurements of CD4 cell counts on the log10 scale for the 404 men in the MACS (upper panels) and the 609 women in the WIHS (lower panels), stratified by the five categories of CD4 cell count measured at the pre-HAART visit, defined by cutoff values at 100, 200, 350, and 500 cells/mm3. The nonparametric smoothed LOWESS curves for persons with a pre-HAART CD4 cell count less than or equal to 200 cells/mm3 suggest that after a significant rapid increase during approximately the first 1.5 years, CD4 cell counts subsequently stabilize or only gradually increase on the population level.



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FIGURE 2. Longitudinal patterns of CD4-positive T-lymphocyte (CD4 cell) counts on the log10 scale for 404 men in the Multicenter AIDS Cohort Study (MACS) (upper panels) and 609 women in the Women's Interagency HIV Study (WIHS) (lower panels), stratified by CD4 cell count prior to initiation of highly active antiretroviral therapy (HAART), with cutoff values at 100, 200, 350, and 500 cells/mm3, July 1995–March 2004. The solid lines denote LOWESS smoothing curves summarizing the trends.

 
Table 2 shows the fold change in CD4 cell count before and after change points and the levels (intercepts) of CD4 cell count at change points for each category of pre- HAART CD4 cell count at the population level for both the MACS and the WIHS. The results confirm that the post-HAART CD4 cell count has trajectory change on the population level (i.e., the log10 CD4 changes before change points are statistically higher than those after change points for each category in both studies, except for the MACS subgroup of pre-HAART CD4 cell count greater than 500 cells/mm3).


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TABLE 2. Estimates of population parameters (posterior median with equal-tail 95% credible interval) among users of highly active antiretroviral therapy (HAART), according to pre-HAART CD4-positive T-lymphocyte (CD4 cell) count, Multicenter AIDS Cohort Study and Women's Interagency HIV Study, July 1995–March 2004

 
The rates of increase prior to change points and the times of change points were associated with pre-HAART CD4 cell counts, such that persons with lower CD4 cell counts had a more rapid early rise and change points occurred earlier in comparison with persons with higher pre-HAART CD4 cell counts. For persons with a pre-HAART CD4 cell count less than or equal to 100 cells/mm3, after approximately 1 year and 3 months of rapid increase (a 4.89-fold increase per year for men and a 4.35-fold increase per year for women), median CD4 cell counts were 212 cells/mm3 for men and 160 cells/mm3 for women, respectively. After that time, the median CD4 cell count continued to increase at a rate of 0.05-fold per year for men and started to decrease at a rate of 0.17-fold per year for women.

For those categories with pre-HAART CD4 cell counts above 100 cells/mm3, the median fold increase per year ranged from 0.49 to 0.04 for men and from 0.21 to 0.04 for women before the change point, which is approximately 2.5 years after HAART initiation. The population average slopes post-change-points for those categories were very small and not statistically significant, suggesting that CD4 cell counts tended to stabilize on the population level after 2.5 years following HAART initiation.

Figure 3 presents graphs of the fitted CD4 cell counts using the posterior mean of each parameter on the log10 scale for the 404 men in the MACS (upper panels) and the 609 women in the WIHS (lower panels), stratified by the five categories of CD4 cell count at the pre-HAART visit. It shows the very high between-person variability, especially for persons with pre-HAART CD4 cell counts less than or equal to 200 cells/mm3. It is noteworthy that some persons appear to have dramatic declines in CD4 cell count after the change point, especially women with pre-HAART CD4 cell counts less than or equal to 100 cells/mm3, which may due to the difference in adherence. Table 3 shows the proportions of participants with a significant decline after change points for each category; this was defined as the upper bound of the equal-tail 95 percent posterior credible interval of ßi2 being less than zero. On the average, 5 percent of men in the MACS and 12 percent of women in the WIHS had a significant decline as defined above after change points.



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FIGURE 3. The predicted trajectory of CD4-positive T-lymphocyte (CD4 cell) counts on the log10 scale for 404 men in the Multicenter AIDS Cohort Study (MACS) (upper panels) and 609 women in the Women's Interagency HIV Study (WIHS) (lower panels) using the posterior mean of each parameter, stratified by CD4 cell count prior to initiation of highly active antiretroviral therapy (HAART), with cutoff values at 100, 200, 350, and 500 cells/mm3, July 1995–March 2004.

 

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TABLE 3. Presence or absence of a significant decline* in CD4-positive T-lymphocyte (CD4 cell) count among users of highly active antiretroviral therapy (HAART) after change points, according to pre-HAART CD4 cell count, Multicenter AIDS Cohort Study and Women's Interagency HIV Study, July 1995–March 2004

 
Table 4 presents the proportions of participants with significant change points for each category, which is defined as the equal-tail 95 percent posterior credible interval of ßi2 ßi1 not containing zero. The results provide evidence of change points at the individual level, especially for persons with pre-HAART CD4 cell counts less than or equal to 100 cells/mm3. For those with pre-HAART CD4 cell counts less than or equal to 100 cells/mm3, 91 percent of the men and 69 percent of the women had a statistically significant alteration of the rate of change in CD4 cell count.


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TABLE 4. Presence or absence of a significant change point* in the CD4-positive T-lymphocyte (CD4 cell) count trajectory among users of highly active antiretroviral therapy (HAART), according to pre-HAART CD4 cell count, Multicenter AIDS Cohort Study and Women's Interagency HIV Study, July 1995–March 2004

 
Table 5 shows the proportions of participants with a "sustained" long-term CD4 cell response to HAART according to pre-HAART CD4 cell count. Despite the fact that HAART had dramatically extended the time to AIDS and death in HIV-infected persons, 24 percent of men in the MACS versus 42 percent of women in the WIHS did not have a sustained response to long-term HAART usage. As expected, it was highly correlated with pre-HAART CD4 cell count. For those men with pre-HAART CD4 cell counts less than or equal to 100 cells/mm3 in the MACS, 47 percent had a sustained response, whereas for those with a pre-HAART CD4 cell count greater than 350 cells/mm3, 95 percent had a sustained response. In comparison with men in the MACS, women in the WIHS had a lower rate of sustained response to HAART within each pre-HAART CD4 cell category. For those women with pre-HAART CD4 cell counts less than or equal to 100 cells/mm3, 20 percent had a sustained response, whereas for those with a pre-HAART CD4 cell count greater than 350 cells/mm3, 87 percent had a sustained response.


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TABLE 5. Proportions of participants with a sustained* long-term CD4-positive T-lymphocyte (CD4 cell) count response to highly active antiretroviral therapy (HAART), according to pre-HAART CD4 cell count, Multicenter AIDS Cohort Study and Women's Interagency HIV Study, July 1995–March 2004

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Through the use of longitudinal data on persons frequently exposed to HAART up to 7 years after HAART initiation from two large US cohort studies, this analysis allowed us to model long-term changes in CD4 cell count among HIV-infected men and women on continuous HAART. We statistically identified change points of CD4 cell count trajectories with long-term HAART usage at both the population and individual levels using a Bayesian random-effect change-point model. For persons who are not familiar with Bayesian methods, the posterior means and medians used in this paper can be visualized as the point estimates in the more traditionally used frequentist paradigm, and the 95 percent equal-tail credible interval could be thought as an alternative to the frequentist 95 percent confidence interval.

With our methods for the estimation of trajectory change at the individual level, we could investigate issues that cannot be addressed at the population level, particularly the prevalence of change. It is not necessary that every person exhibit a change point for the phenomenon to exist in the aggregate of HIV-infected persons. In the population averages, such as those presented by Tarwater et al. (10Go), a few persons with strong changes could have accounted for the observed population change after HAART initiation. Our new analysis provides substantial evidence for individual-level change among these long-term frequent HAART users.

Furthermore, the identification of CD4 cell change on an individual level provides an approach for investigating immunologic and virologic characteristics associated with the response to HAART. Using the Bayesian random change-point model described in this article, studies now may be designed to examine the occurrence of landmark events (e.g., viral failure) relative to the timing of post-HAART change. This type of design will be critical to distinguish factors that are consequences of HAART response from those antecedent factors that may help precipitate HAART failure.

The prior distributions of change points were assumed to be uniform within the observed time interval between the second and second-last (penultimate) available points for each person, such that at least two CD4 cell counts were used to estimate the posterior slopes before and after the change point. Caution is necessary when interpreting the posterior parameters of change points, since subjects were followed for different lengths of time. At the individual level, the posteriors are affected by both the individual length of follow-up and the number of CD4 cell count measurements available. However, since the average lengths of follow-up were similar across men and women and across different pre-HAART CD4 cell count strata, the population-level parameters still provide appropriate summaries.

Our study focused on characterizing the trajectories of CD4 cell counts in HIV-infected persons after HAART initiation rather than investigating the predictors of the different patterns among individuals other than pre-HAART CD4 cell count. Potential factors associated with the heterogeneity of patterns may include HIV RNA levels, history of antiretroviral therapy before HAART initiation, age at HAART initiation, and genetic factors. Further studies using a nested case-control design could address these issues by comparing persons with and without changes. An important consideration for this next analysis is that only frequent HAART users (i.e., persons reporting HAART usage at ≥80 percent of post-HAART visits) with at least four CD4 cell measurements after HAART initiation were included in the analyses, eliminating those who had poor long-term response due to the discontinuation of HAART.

Caution is necessary when comparing these cohorts, since they differ according to several characteristics besides gender. In the MACS, 85 percent of subjects were Caucasian, as compared with only 21 percent in the WIHS, and the two cohorts have substantially different sociodemographic characteristics. Furthermore, only 18 percent of the subjects in the MACS had clinical AIDS before HAART initiation, as opposed to 45 percent of the subjects in the WIHS. Generalization to the current era, in which subjects are encouraged to go directly from no treatment to HAART, may also be limited, since more than 80 percent of HIV-infected participants in both MACS and WIHS were treated with mono- or combination antiretroviral therapy before HAART initiation. Therefore, these results may only be generalizable to heavily pretreated populations.

In summary, the data suggest that the proportional CD4 cell count change over time can be well modeled with change on both the population and individual levels. On the population level, the slopes before change points are statistically different from those after change points for all five CD4 categories in both studies, except for the MACS subgroup with a pre-HAART CD4 cell count greater than 500 cells/mm3, as presented in table 2. As expected, the differences between before- and after-change-point slopes were correlated with pre-HAART CD4 cell counts. On the individual level, we identified 35 percent of the men in MACS versus 25 percent of the women in WIHS as having a statistically significant change in CD4 cell count trajectories within 7 years on HAART (table 4). The proportions of persons with statistically significant change points were also correlated with pre-HAART CD4 cell count. As pre-HAART CD4 cell count increases, the proportion with statistically significant change points decreases dramatically, mostly because of lower rates of rapid CD4 increase immediately after HAART initiation. On the basis of the triplet estimates of slopes before and after change points and levels at change points, 24 percent of the men in MACS versus 42 percent of the women in WIHS did not have a sustained response to long-term HAART usage, which underscores the need for long-term monitoring and the need to search for additional therapies or different combinations of regimens that will further boost immune reconstitution. On the average, 5 percent of men in the MACS and 12 percent of women in the WIHS had a significant decline after change points. These findings indicate that there is substantial interperson variation in CD4 cell trajectories, and this could have important implications for therapy. Future research into the causes of such dramatic interperson variation may be warranted.


    ACKNOWLEDGMENTS
 
Data presented in this paper were collected by the Multicenter AIDS Cohort Study (MACS) and the Women's Interagency HIV Study (WIHS) Collaborative Study Group. The MACS is funded by the National Institute of Allergy and Infectious Diseases, with additional supplemental funding from the National Cancer Institute, and the National Heart, Lung, and Blood Institute (grants UO1-AI-35042, 5-M01-RR-00052 (General Clinical Research Center), UO1-AI-35043, UO1-AI-37984, UO1-AI-35039, UO1-AI-35040, UO1-AI-37613, and UO1-AI-35041). The WIHS is funded by the National Institute of Allergy and Infectious Diseases, with additional supplemental funding from the National Cancer Institute; the National Institute of Child Health and Human Development; the National Institute on Drug Abuse; the National Institute of Craniofacial and Dental Research; and the National Heart, Lung, and Blood Institute (grants U01-AI-35004, U01-AI-31834, U01-AI-34994, U01-AI-34989, U01-HD-32632, U01-AI-34993, U01-AI-42590, M01-RR00079, and M01-RR00083).

MACS and WIHS study centers (and Principal Investigators) are as follows: MACS—Johns Hopkins Bloomberg School of Public Health (Joseph Margolick); Howard Brown Health Center and Northwestern University Medical School (John Phair); University of California, Los Angeles (Roger Detels); University of Pittsburgh (Charles Rinaldo); and Data Analysis Center (Lisa Jacobson); WIHS—New York City/Bronx Consortium (Kathryn Anastos); Brooklyn, New York (Howard Minkoff); Washington, DC, Metropolitan Consortium (Mary Young); Connie Wofsy Study Consortium of Northern California (Ruth Greenblatt); Los Angeles County/Southern California Consortium (Alexandra Levine); Chicago Consortium (Mardge Cohen); and Data Analysis Center (Stephen J. Gange).

The authors are grateful to Drs. Peter Bacchetti and Alvaro Muñoz for helpful comments and to Lorie Benning for data management and descriptive analyses of the WIHS data.

Conflict of interest: none declared.


    References
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

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