Modeling Changes in CD4-positive T-Lymphocyte Counts after the Start of Highly Active Antiretroviral Therapy and the Relation with Risk of Opportunistic Infections The Aquitaine Cohort, 1996–1997

C. Binquet1, G. Chêne1, H. Jacqmin-Gadda1, V. Journot1, M. Savès1, D. Lacoste2, F. Dabis1 and the Groupe d'Epidémiologie Clinique du SIDA en Aquitaine

1 Institut National de la Santé et de la Recherche Médicale (INSERM), Unité 330, Université Victor Segalen Bordeaux 2, 146 rue Léo-Saignat, 33076 Bordeaux Cedex, France.
2 Centre d'Information et de Soins sur l'Immunodéficience Humaine, Hopital Pellegrin-Tondu, Place Amélie Raba-Léon, 33076 Bordeaux Cedex, France.
Members of the Groupe d'Epidémiologie Clinique du SIDA en Aquitaine are listed in the Acknowledgments.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
After initiation of a treatment for human immunodeficiency virus type 1 infection containing a protease inhibitor, immune restoration associated with increases in CD4-positive (CD4+) T lymphocyte count may be delayed. In a sample of patients who had been prescribed protease inhibitors for the first time, the authors tested to see whether there was a minimal duration of CD4+ cell count increase before the increase had an impact on the occurrence of opportunistic infections. The evolution (difference between time t and baseline) of CD4+ cell count was modeled using a mixed effects linear model. Changes in CD4+ count estimated by this model were then included as time-dependent covariates in a proportional hazards model. Finally, the authors tested for the existence of a CD4+ change x time interaction. The authors used a sample of 553 French patients first prescribed protease inhibitors in 1996 and followed for a median of 16 months. During the first 120 days, there was no association between CD4+ change and the rate of opportunistic infections. After 120 days, each 50-cell/mm3 increase in CD4+ count was associated with a 60% (95% confidence interval: 45, 72) reduction in the incidence of opportunistic infections. These results, based on modeling of CD4+ cell response, at least indirectly reinforce the concept of a delayed but possible immune recovery with the use of protease inhibitors. The findings support the potential for interruption of certain types of prophylaxis against opportunistic infections under reasonable conditions of duration of antiretroviral therapy and sustained CD4+ cell response.

acquired immunodeficiency syndrome; antiviral agents; CD4-positive T-lymphocytes; cell count; immune system; immunity; opportunistic infections; protease inhibitors

Abbreviations: AIC, Akaike Information Criterion; AIDS, acquired immunodeficiency syndrome; CI, confidence interval; HAART, highly active antiretroviral therapy; HIV, human immunodeficiency virus


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The use of highly active antiretroviral therapy (HAART) including a protease inhibitor in the treatment of human immunodeficiency virus (HIV) infection results in reduced viral replication, as measured by lower plasma levels of HIV type 1 (HIV-1) RNA, an increase in CD4-positive (CD4+) T lymphocyte count, and delayed disease progression (1GoGoGo–4Go). Controversy remains as to whether T lymphocytes are functional after use of HAART and what mechanisms might be involved in possible protection against opportunistic infections (5GoGo–7Go). However, the potential for immune reconstitution after severe immunodeficiency is of clinical importance, because it may change the need for prophylaxis against opportunistic infection. The success of a treatment for HIV infection depends not only on its ability to yield an undetectable plasma viral load but also on its ability to restore immune functions. We (8Go) and other investigators (9Go) previously showed that the risk of opportunistic infection decreased substantially when CD4+ cell count increased in response to HAART among patients with advanced HIV-1 infection. However, these observations did not provide details on the temporal sequence of events. If the hypothesis of CD4+ cell repopulation after the first prescription of protease inhibitors, followed by reconstitution of immunity (6Go), is true, we should observe that there is a minimal duration of time before the effect of changes in CD4+ count has an impact on the risk of opportunistic infection. Therefore, we again used the Aquitaine Cohort database to explore this hypothesis.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Study population
In 1987, a prospective study of a hospital-based cohort of HIV-1-infected patients under routine clinical management, the Aquitaine Cohort (10Go), was initiated at Bordeaux University Hospital and four other public hospitals in the Aquitaine region of France by the Groupe d'Epidémiologie Clinique du SIDA en Aquitaine (10Go). All adult inpatients or outpatients of the participating hospital wards who have HIV-1 infection confirmed by Western blot testing, regardless of clinical stage, who have at least one follow-up visit after the first report or a known date of death, and who provide informed consent are eligible for inclusion. Patients eligible for the present study were subjects included in the Aquitaine Cohort who were prescribed at least one available protease inhibitor (saquinavir, indinavir, ritonavir) for the first time between March and December 31, 1996, and who had at least two available measurements of CD4+ and CD8+ cell counts: one conducted at initiation of treatment and at least one carried out during subsequent follow-up. Generally, patients using protease inhibitors are reexamined 1 month after treatment initiation and then every 3–4 months as recommended in France (11Go) and internationally (12Go). Data collection and case definitions of acquired immunodeficiency syndrome (AIDS)-defining opportunistic infections have been previously described in detail elsewhere (8Go). In summary, 661 HIV-1-infected patients in the Aquitaine Cohort were first prescribed at least one protease inhibitor during the study period. Among them, 553 (84 percent) had at least two CD4+ and CD8+ measurements available and were included in our analysis.

Immunologic response was measured by the evolution of CD4+ and CD8+ cell counts after treatment initiation–i.e., the difference between measurements made at time t and those made at baseline, denoted CD4(t) – CD4(0) and CD8(t) – CD8(0). For plotting of immunologic response (figure 1), cell counts were grouped according to their date of measurement: measurements done less than 15 days after protease inhibitor initiation were considered baseline values, those performed 15–46 days after initiation were considered month 1 values, and so on, up to 18 months. Because of the limited number of available measurements beyond 18 months, the data were censored at that time, but all measurements were taken into account for statistical modeling, using the exact time of measurement in days.



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FIGURE 1. Evolution of CD4-positive (CD4+) T lymphocyte count (median (•) and interquartile range (bars)) among human immunodeficiency virus-positive patients receiving highly active antiretroviral therapy with at least one protease inhibitor (553 patients and 4,141 measures), Aquitaine Cohort, France, 1996–1997.

 
Statistical analysis
The date of first prescription of a protease inhibitor was considered the baseline date for our study sample. These dates ranged from March 1996 to the end of December 1996. Follow-up extended to December 31, 1997. Patients were considered lost to follow-up if their last follow-up visit occurred more than 3 months prior to that date.

In a first step of modeling, the evolutions of CD4+ and CD8+ cell counts were estimated separately using a mixed effects linear model (the PROC MIXED procedure in SAS). The model included a polynomial function of time in fixed and random components assuming individual variations in the changes in CD4+ and CD8+ count, testing for an auto-regressive process for these changes (13Go). This method allowed us to estimate changes in CD4+ and CD8+ cell counts for each patient at the time of each occurrence of opportunistic infection (14Go).

In a second step, estimated changes in CD4+ and CD8+ counts from this initial model were entered into a Cox proportional hazards model as time-dependent covariates (15Go). The hypothesis of variable constancy between two consecutive measurements, which is the default option for time-dependent covariates in all software programs, was therefore avoided. Follow-up time for each subject was calculated as the interval between the date of first prescription of a protease inhibitor and the date of opportunistic infection occurrence or the date of last follow-up. Patients were censored at death, which was assumed not to be due to an opportunistic infection. The model was used to estimate the effects of baseline CD4+ and CD8+ cell counts and changes in CD4+ and CD8+ cell counts, adjusted for the following clinical and laboratory variables measured at baseline: age, gender, HIV transmission group, history of an AIDS-defining event, HIV-1 RNA level, hemoglobin level, prior treatment with nucleoside analogs, opportunistic infection prophylaxis, and type of protease inhibitor prescribed. A reduced model was produced by backward elimination. Results are expressed in terms of the hazard ratio, which estimates how each independent variable affects the instantaneous hazard of opportunistic infection.

In a third step, we added a term of interaction between time and immunologic response in order to test for different effects of CD4+ evolution over time. Because we were also interested in the existence of a time threshold ({tau}) beyond which the effect of CD4+ evolution would be different, this was tested in a Cox model designed as follows:

Z is the vector of all explanatory variables considered and X represents the vector of fixed covariates, included in Z. As in the second step, CD4(t) – CD4(0) was the value estimated by the mixed effects linear model (first step of the analysis).

The effect of CD4(t) – CD4(0) was measured by ß' before time {tau} and by ß' + ß'' thereafter. Moreover, ß'' allowed us to test for the effect of this covariate on the second period with regard to the first one. The following possible thresholds were tried: each 10 days during the first 6 months after the introduction of HAART and each 30 days thereafter (5GoGo–7Go). The choice of these thresholds was based on the current literature on HIV infection. The best threshold was chosen by profile likelihood.

In a fourth step, changes in CD4+ cell count were categorized to test for a "dose-response" relationship with the rate of subsequent opportunistic infection. Thresholds were chosen according to recommendations for opportunistic infection prophylaxis (16Go).

Models were compared using the Akaike Information Criterion (AIC) (17Go) or the likelihood ratio test, when appropriate. We checked the proportional hazards assumption using graphical methods, by examining log(–log(survival probability)) versus time plots for each of the covariates in the final model.

STATA software, version 5.1 (STATA Corporation, College Station, Texas), and SAS software, version 6.11 (SAS Institute, Inc., Cary, North Carolina), were used for statistical analysis.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Study sample
The 553 study participants were 39 years old (standard error 5 months), on average, and were predominantly males (n = 417; 75 percent). Homosexuality was the most frequent HIV transmission category (n = 227; 41 percent), followed by at-risk heterosexual intercourse (n = 136; 25 percent) and intravenous drug use (n = 135; 24 percent). Among AIDS patients (n = 245; 44 percent), 158 subjects (29 percent) had experienced at least one opportunistic infection prior to initiation of protease inhibitor treatment. The most common opportunistic infection before entry into the study was Pneumocystis carinii pneumonia (n = 61; 11 percent), followed by toxoplasmic encephalitis (n = 55; 10 percent) and esophageal or pulmonary candidiasis (n = 49; 9 percent). The proportion of antiretroviral naive patients was 6 percent. All patients were prescribed a protease inhibitor for the first time. The drug prescribed was indinavir in 283 patients (51 percent), saquinavir in 189 (34 percent), and ritonavir in 79 (14 percent). Two patients received a combination of saquinavir and ritonavir. Overall, the protease inhibitor prescribed was changed in 272 patients during their follow-up, but only 33 (6 percent) had to definitely stop using protease inhibitors. At baseline, the median CD4+ lymphocyte count was 95 cells/mm3 (interquartile range, 28–181); the median CD8+ lymphocyte count was 563 cells/mm3 (interquartile range, 316–878); the median plasma HIV RNA level was 4.5 log10 copies/ml (interquartile range, 4.0–5.1); and the median hemoglobin level was 13.0 g/dl (interquartile range, 12.0–14.0).

Immunologic response under HAART
During follow-up, a median of nine measurements of CD4+ cell count were available for each patient (interquartile range, 7–11). Two slopes described the shape of median CD4+ changes over time (figure 1): a rapid increase of 23.5 cells/mm3/month on average during the first 2 months, followed by a slower increase of 6.4 cells/mm3/month on average between month 2 and month 12. These slopes did not differ when we stratified the sample according to baseline CD4+ cell count or CD4+/CD8+ ratio (data not shown).

The best adjustment of a mixed model with CD4+ change as the dependent covariate included time to the sixth power for fixed effects and time to the fourth power for random effects (AIC = 45,890.6). The best adjustment of a mixed model with CD8+ change as the dependent covariate included time to the fifth power for fixed effects and time to the fourth power for random effects (AIC = 58,798.0).

Occurrence of opportunistic infections during follow-up
By December 31, 1997, after a median follow-up time of 16 months, 82 patients had experienced at least one episode of opportunistic infection, and 39 had died. Forty-two patients (7.6 percent) were considered lost to follow-up. The cumulative probability of opportunistic infection occurrence since first prescription of a protease inhibitor was 9.2 percent at 6 months (95 percent confidence interval (CI): 7.0, 11.9), 13.3 percent at 12 months (95 percent CI: 10.7, 16.5), and 15.4 percent at 18 months (95 percent CI: 12.5, 18.9).

The most frequent opportunistic infection was cyto-megalovirus disease (n = 22), and it occurred within the shortest interval after protease inhibitor initiation: a median of 2.7 months (table 1). The second most common infection was Mycobacterium avium bacteriemia (n = 17; 5 months), followed by esophageal candidiasis. The 22 other infections were tuberculosis (n = 8), cryptosporidiosis or microsporidiosis (n = 5), recurrent chronic herpes infection (n = 5), cryptococcus meningoencephalitis (n = 1), recurrent bacterial pneumonia (n = 1), isosporosis (n = 1), and progressive multifocal leukoencephalitis (n = 1). Opportunistic infection occurred within a median interval of 4.8 months (interquartile range, 1.7–8.9 months). Overall, the median CD4+ cell count was less than 50 cells/mm3 at the time of opportunistic infection diagnosis (table 1).


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TABLE 1. Opportunistic infections* occurring during the use of highly active antiretroviral therapy with at least one protease inhibitor (n = 553), Aquitaine Cohort, France, 1996–1997

 
Among the 82 patients who experienced at least one opportunistic infection, only four did not receive prophylactic treatment at any time during follow-up. One additional patient stopped his prophylactic treatment 36 days after protease inhibitor initiation (CD4+ count 135 cells/mm3) and had a mycobacterial infection diagnosed 1 year later. Among the 78 patients receiving prophylactic treatment, 32 had a prophylactic treatment considered efficient against the opportunistic infection experienced: cytomegalovirus disease (n = 10), M. avium bacteriemia (n = 6), P. carinii pneumonia (n = 5), esophageal candidiasis (n = 5), toxoplasmosis (n = 4), cryptococcus meningoencephalitis (n = 1), and chronic herpes infection (n = 1).

Predictors of opportunistic infection occurrence
The presence of clinical manifestations of AIDS prior to prescription of a protease inhibitor, baseline CD4+ and CD8+ cell counts, hemoglobin level, and prophylactic treatment at baseline were univariate predictors of the occurrence of an opportunistic infection (table 2). The table shows results for CD8+ count obtained after CD8+ count was categorized (<500 and >=500 cells/mm3) to fit the hypothesis of hazards proportionality. Other factors, such as age, gender, transmission group, prior treatment with nucleoside analogs, type of protease inhibitor initially prescribed, and HIV RNA level (except when unknown), were not associated with the occurrence of an opportunistic infection. Data concerning prior clinical manifestations of AIDS, duration of AIDS before protease inhibitor prescription, and stage of HIV disease (according to the Centers for Disease Control and Prevention) at baseline were highly correlated. To avoid collinearity, we used only the indicator of prior AIDS clinical manifestations in further modeling steps, because it was the most highly predictive in terms of its contribution to the maximization of the log-likelihood.


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TABLE 2. Effect of baseline variables and evolution of CD4-positive (CD4+) T lymphocyte count (modeled with a mixed linear approach) on rates of opportunistic infection in patients receiving highly active antiretroviral therapy with at least one protease inhibitor, Aquitaine Cohort, France, 1996–1997*

 
In the multivariate analysis taking into account estimated CD4+ and CD8+ changes as time-dependent covariates (table 2), change in CD8+ count was not associated with the subsequent rate of opportunistic infection (p = 0.72). Each increase of 50 cells/mm3 in the CD4+ count was associated with a 49 percent reduction (95 percent CI: 33, 61) in the risk of opportunistic infection (model 2). This estimation was adjusted for history of AIDS clinical manifestations prior to protease inhibitor prescription (hazard ratio = 3.23; 95 percent CI: 1.81, 5.78), baseline CD4+ count (hazard ratio = 0.70 (95 percent CI: 0.57, 0.85) for each 50-cell/mm3 higher CD4+ count), a baseline CD8+ count of at least 500 cells/mm3 (hazard ratio = 0.59; 95 percent CI: 0.36, 0.95), and baseline hemoglobin level (hazard ratio = 0.63; 95 percent CI: 0.40, 0.98). Other variables did not remain in the final model. The AIC was minimized for model 2 (AIC = 893.1), which took into account CD4+ changes estimated by a mixed linear approach, as compared with model 1, which contained only baseline covariates (AIC = 923.6).

Model 2 being fitted, we studied the interaction between time and changes in the CD4+ cell count. The global interaction was statistically significant, as the effect of CD4+ changes was not constant over time (p < 10-3). We examined the possibility of there being one or more time thresholds ({tau}) for the effect of the change in CD4+ count on the occurrence of opportunistic infection by hypothesizing different effects of CD4+ change over time. Two phases were thus identified: 1) during the first 120 days of treatment, an increase in the CD4+ count was not associated with the rate of opportunistic infection, but 2) after 120 days, an increase in the CD4+ count was associated with a 60 percent (95 percent CI: 45, 72) reduction in the incidence of opportunistic infection (model 3). When we considered CD4+ changes after 120 days in categories, an increase of at least 50 cells/mm3 seemed necessary for a reduction in the subsequent incidence of opportunistic infection (model 4). Beyond this threshold, the greater was the increase in CD4+ count, the higher was the reduction in the incidence of opportunistic infection: An increase of >=100 cells/mm3 was associated with a reduction in opportunistic infection incidence of 94 percent (95 percent CI: 80, 98), after adjustment for other covariates.


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Our observation of a cohort of HIV-1-infected patients being treated with protease inhibitors showed that the relation between changes in CD4+ lymphocyte count and risk of opportunistic infection varied over time. During the first 4 months, changes in the CD4+ cell count were not associated with a change in the rate of opportunistic infection; after 4 months, an increase in the CD4+ count of at least 50 cells/mm3 was associated with a reduction in the rate of opportunistic infection, and the greater the increase of CD4+ count the greater the reduction in the rate of opportunistic infection, after data were controlled for other prognostic covariates.

Other authors had already proposed CD4+ modeling with a mixed linear approach prior to the introduction of HAART (18GoGo–20Go). This method allowed us to estimate changes in CD4+ and CD8+ cell counts for each patient at the time of each occurrence of opportunistic infection and then enter estimations from this initial model into a Cox proportional hazards model as time-dependent covariates (14Go). We thus avoided use of the hypothesis of variable constancy between two consecutive measurements, which was difficult to assume in our study. The use of mixed linear models allowed us to take into account within-subject variability (21Go) and the fact that there may be considerable variation among individuals in the number and timing of observations (19Go). Moreover, taking into account changes in CD4+ cell count helped us avoid the interpretation of a precise value for CD4+ count, which could be controversial in the case of protease inhibitor treatment (5Go, 22Go). Touloumi et al. (23Go) showed that random effects models for changes in CD4+ count are likely to give overly optimistic population-average estimates, since subjects who progress quickly to AIDS or death and are likely to have steeper drops in CD4+ count have shorter follow-up times and hence are weighted less in the estimation of the population average slope. However, in our study sample, only 39 subjects died, and we believe that this bias would not have had enough impact to change our conclusions.

The sample we studied, through its wide composition of subjects (both genders, all HIV transmission categories, all clinical stages, and all treatments used), can be considered representative of the population of HIV-infected patients in Western countries, at least in Europe (24Go). However, it must be underlined that patients studied in this sample began protease inhibitor-associated treatment at a more advanced stage of HIV infection than patients who currently begin using HAART. This had at least one advantage for our modeling exercise. Since these patients tended to be at substantial AIDS risk at the time they started using protease inhibitors, we had sufficient statistical power to analyze the relation between CD4+ changes and subsequent risk of opportunistic infection. This might not have been the case with patients who started using protease inhibitors more recently (25Go).

HIV RNA level has also been shown to be an important predictive factor for clinical endpoints (26Go). One limitation of our study was that only baseline values of HIV RNA measurements were available for use in the modeling. Indeed, measurement of viral load was performed at baseline and at least once during follow-up for less than 60 percent of our patients. Therefore, we could not stratify the patients we studied according to suppression of viral load in response to HAART. It would have been useful to study changes in CD4+ count and HIV RNA level at the same time, because they are not always correlated with each other and with clinical stabilization (27Go). However, for patients in a late stage of HIV disease, HIV RNA level has been shown to be a less reliable prognostic marker than CD4+ count in some studies (28Go, 29Go). Conversely, a reduction in viral load has been shown to be favorably associated with CD4+ cell reconstitution in both the short term (30Go) and the long term (1GoGo–3Go, 31Go, 32Go). This should be taken into account in future studies on this topic.

Our characterization of changes in CD4+ cell count showed a two-phase increase, irrespective of the baseline value. With regard to evolution of CD4+ count, a prompt increase within the first 2 months after initiation of HAART was followed by a slower increase (6Go, 31Go, 33Go), maintained at least up to 18 months after protease inhibitor prescription. The increase in CD4+ count observed at 12 months was similar to changes described in other cohorts or trials (2Go, 3Go, 31Go). Baseline CD4+ count remained significantly associated with subsequent risk of opportunistic infection. This implies that even if immune reconstitution occurs, patients with initial profound immunosuppression remain at a disadvantage (34Go). Moreover, patients who had already experienced an AIDS-defining event at the point of initiating HAART also were at higher risk of developing a new event, irrespective of their CD4+ evolution during treatment. These patients might have a lower capacity to restore their immunity.

It seems that both quantitative and functional immunologic improvements are important for optimal immune reconstitution (35Go). Although this study was not designed to measure the benefits of any qualitative CD4+ cell improvements, our results suggest that the decrease in the rate of opportunistic infection may be due to improved functionality of CD4+ cells among patients started on HAART (5Go, 6Go). A majority of opportunistic infections occurred soon after initiation of protease inhibitor treatment (36Go, 37Go), and a lower incidence of opportunistic infection was observed thereafter, which is consistent with a delayed restoration of protection against opportunistic infection. Moreover, the statistically significant interaction between CD4+ change and time is an additional factor in favor of a delayed but at least partial immune recovery. The initial rapid increase in CD4+ cell count could be linked to a redistribution of memory T cells from lymphoid tissues (6Go, 38Go) to the peripheral blood. Pakker et al. (39Go) suggested that this increase in lymphocyte cell count could reach a plateau after 3 weeks of treatment. This evolution could be followed by a slower and apparently selective increase in naive T cells associated with maturation of newly generated T cells. This hypothesis is supported by evidence for diversification of the CD4+ cell repertoire after 3–6 months of treatment (40Go), which is consistent with our observation.

One implication of our results in clinical management is that an unfavorable trend in CD4+ count after at least 4 months of treatment is an indication of drug failure and suggests a need to change the therapy. Our data also supply epidemiologic findings in favor of immune recovery. In this context, interruption of certain types of prophylaxis against opportunistic infection seems possible when patients show every sign of immunologic, virologic, and clinical stabilization after having been given a protease inhibitor. This is in agreement with a recent study suggesting that prophylaxis against P. carinii pneumonia or toxoplasmic encephalitis (25Go) might be interrupted after the CD4+ count has reached a threshold of >=200 cells/mm3 for at least 12 weeks. In our study, an increase of at least 50 CD4+ cells/mm3 seemed necessary for a reduction in the risk of opportunistic infection after 4 months of HAART. These results are consistent with the requirement of a minimal duration of immune recovery before prophylaxis may be stopped. Moreover, it seems that a minimal increase in CD4+ cell count is also necessary to reach this decision. Beyond a CD4+ increase of at least 50 cells/mm3, the greater the increase the greater the reduction in risk of opportunistic infection. In their study, Li et al. (41Go) showed that the minimal CD4+ increase proven to have a favorable impact on immunologic status was 60 cells/mm3.

These results, based on reasonable modeling of CD4+ cell response, at least indirectly reinforce the concept of a delayed but possible immune recovery after initiation of a treatment regimen including a protease inhibitor. It will be important to examine the same relation in populations of HIV-infected patients who started using protease inhibitors more recently and at less advanced stages of HIV disease, also properly taking into account changes in HIV RNA levels over time. These data support the concept of interruption of prophylaxis against certain types of opportunistic infection under reasonable conditions of duration of HAART and CD4+ cell response.


    ACKNOWLEDGMENTS
 
Financial support for study of the Aquitaine Cohort is provided by the French National AIDS Research Agency through its Coordinated Action no. 7.

The authors thank Dr. Jean-François Moreau for valuable discussions and for performing CD4+ lymphocyte immunophenotyping of patients included in the hospital-based surveillance system of the Groupe d'Epidémiologie Clinique du SIDA en Aquitaine. They especially thank Dr. Philippe Morlat, who discussed these results in depth when they were presented as a medical thesis by the first author.

Composition of the Groupe d'Epidémiologie Clinique du SIDA en Aquitaine–organization and methodology: Dr. C. Marimoutou and Prs. G. Chê ne, F. Dabis, and R. Salamon; clinical coordination: Drs. D. Lacoste, D. Malvy, P. Mercié, and I. Pellegrin and Prs. M. Dupon, J. F. Moreau, P. Morlat, J. L. Pellegrin, and J. M. Ragnaud; participating hospital departments (participating physicians): Bordeaux University Hospital–Pr. J. Beylot (Pr. P. Morlat and Drs. N. Bernard, D. Lacoste, and C. Nouts), Pr. C. Beylot (Pr. M. S. Doutre), Pr. C. Conri (Pr. J. Constans), Pr. P. Couzigou, Pr. H. Fleury (Dr. I. Pellegrin), Pr. M. Géniaux (A. Simon), Pr. J. Y. Lacut (Dr. C. Cazorla and Pr. M. Dupon), Pr. B. Leng (Dr. P. Mercié and Pr. J. L. Pellegrin), Pr. M. Le Bras (Drs. F. Djossou, D. Malvy, and J. P. Pivetaud), Pr. J. F. Moreau (Dr. J. L. Taupin), Pr. J. M. Ragnaud (Drs. C. de la Taille, H. Dutronc, and D. Neau), Pr. C. Seriès, and Pr. A. Taytard; Dax Hospital–Dr. M. Loste (Dr. I. Blanchard); Bayonne Hospital–Dr. F. Bonnal (Drs. Y. Blanchard, S. Farbos, and M. C. Gemain); Libourne Hospital–Dr. J. Ceccaldi (Dr. X. Jacquelin); Villeneuve-sur-Lot Hospital–Drs. E. Buy and G. Brossard; data management and analysis: M.-J. Blaizeau, M. Decoin, L. Dequae-Merchadou, A. M. Formaggio, M. Pontgahet, D. Touchard, and G. Palmer.


    NOTES
 
Reprint requests to Dr. Geneviève Chêne, INSERM Unité 330, Université Victor Segalen Bordeaux 2, 146 rue Léo Saignat, 33076 Bordeaux Cedex, France (e-mail: genevieve.chene{at}isped.u-bordeaux2.fr).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Collier AC, Coombs RW, Schoenfeld DA, et al. Treatment of human immunodeficiency virus infection with saquinavir, zidovudine, and zalcitabine. N Engl J Med 1996;334:1011–17.[Abstract/Free Full Text]
  2. Hammer SM, Squires KE, Hughes MD, et al. A controlled trial of two nucleoside analogues plus indinavir in persons with human immunodeficiency virus infection and CD4 cell counts of 200 per cubic millimiter or less. N Engl J Med 1997;337:725–33.[Abstract/Free Full Text]
  3. Gulick RM, Mellors JW, Havlir D, et al. Treatment with indinavir, zidovudine, and lamivudine in adults with human immunodeficiency virus infection and prior antiretroviral therapy. N Engl J Med 1997;337:734–9.[Abstract/Free Full Text]
  4. Cameron DW, Heath-Chiozzi M, Danner S, et al. Randomised placebo-controlled trial of ritonavir in advanced HIV-1 disease. Lancet 1998;351:543–9.[ISI][Medline]
  5. Connors M, Kovacs JA, Krevat S, et al. HIV infection induces changes in CD4+ T-cell phenotype and depletions within the CD4+ T-cell repertoire that are not immediately restored by antiviral or immune-based therapies. Nature Med 1997;3:533–40.[ISI][Medline]
  6. Autran B, Carcelain G, Li TS, et al. Positive effects of combined antiretroviral therapy on CD4+ T cell homeostasis and function in advanced HIV disease. Science 1997;277:112–16.[Abstract/Free Full Text]
  7. Gilquin J, Piketty C, Thomas V, et al. Acute cytomegalovirus infection in AIDS patients with CD4 counts above 100 x 106 cells/l following combination antiretroviral therapy including protease inhibitors. (Letter). AIDS 1997;11:1659–60.[ISI][Medline]
  8. Chêne G, Binquet C, Moreau JF, et al. Changes in CD4+ cell count and the risk of opportunistic infections or death after highly active antiretroviral treatment. AIDS 1998;12:2313–20.[ISI][Medline]
  9. Miller V, Staszewski S, Nisius G, et al. Risk of new AIDS diseases in people on triple therapy. (Letter). Lancet 1999;353:463.[ISI][Medline]
  10. Marimoutou C, Chê ne G, Dabis F, et al. Human immunodeficiency virus infection and AIDS in Aquitaine: 10 years' experience of a hospital information system, 1985–1995. Le Groupe d'Epidemiologie Clinique du SIDA en Aquitaine. (In French). Presse Med 1997;26:703–10.[ISI][Medline]
  11. Dormont J. Prise en charge des personnes atteintes par le VIH. Rapport au Ministère du Travail et des Affaires Sociales, Secrétariat d'Etat à la Santé et à la Sécurité Sociale. Paris, France: Flammarion Médecine-Sciences, 1998.
  12. Carpenter CJ, Fischl MA, Hammer SM, et al. Antiretroviral therapy for HIV infection in 1997. JAMA 1997;277:1262–9.
  13. Taylor JM, Law N. Does the covariance structure matter in longitudinal modelling for the prediction of future CD4 counts? Stat Med 1998;17:2381–94.[ISI][Medline]
  14. DeGruttola V, Wulfsohn M, Fischl M, et al. Modelling the relationship between survival after AIDS diagnosis and progression of markers of HIV disease. J Acquir Immune Defic Syndr 1995;6:359–65.
  15. Cox D. Regression models and life tables (with discussion). J R Stat Soc B 1972;34:187–220.[ISI]
  16. Kaplan J. Preventing opportunistic infections in persons infected with HIV: 1997 guidelines. JAMA 1997;278:337–8.[ISI][Medline]
  17. Akaike H. A new look at the statistical model identification. IEEE Trans Automat Contr 1974;AC-10:716–23.
  18. DeGruttola V, Tu XM. Modelling progression of CD4-lymphocyte count and its relationship to survival time. Biometrics 1994;50:1003–14.[ISI][Medline]
  19. Lavalley MP, DeGruttola V. Models for empirical Bayes estimators of longitudinal CD4 counts. Stat Med 1996;15:2289–305.[ISI][Medline]
  20. Boscardin WJ, Taylor JM, Law N. Longitudinal models for AIDS marker data. Stat Methods Med Res 1998;7:13–27.[Medline]
  21. Hughes M, Stein D, Gundacker H, et al. Within-subject variation in CD4 lymphocyte count in asymptomatic human immunodeficiency virus infection: implications for patient monitoring. J Infect Dis 1994;169:28–36.[ISI][Medline]
  22. Neumann AU, Gorochov G. HIV antivirals and immune recovery. (Letter). Nat Med 1997;3:703–4.[Medline]
  23. Touloumi G, Pocock SJ, Babiker AG, et al. Estimation and comparison of rates of change in longitudinal studies with informative drop-outs. Stat Med 1999;18:1215–33.[ISI][Medline]
  24. Downs A, Heisterkamp S, Brunet J, et al. Reconstitution and prediction of the HIV/AIDS epidemic among adults in the European Union and in low prevalence countries of Central and Eastern Europe. AIDS 1997;11:649–62.[ISI][Medline]
  25. Furrer H, Egger M, Opravil M, et al. Discontinuation of primary prophylaxis against Pneumocystis carinii pneumonia in HIV-1 infected adults treated with combination antiretroviral therapy. N Engl J Med 1999;340:1301–6.[Abstract/Free Full Text]
  26. O'Brien WA, Hartigan PM, Daar ES, et al. Changes in plasma HIV RNA levels and CD4+ lymphocyte counts predict both response to antiretroviral therapy and therapeutic failure. Ann Intern Med 1997;126:939–45.[Abstract/Free Full Text]
  27. Kaufmann D, Pantaleo G, Sudre P, et al. CD4-cell count in HIV-1-infected individuals remaining viraemic with highly active antiretroviral therapy (HAART). Swiss HIV Cohort Study. (Letter). Lancet 1998;351:723–4.[ISI][Medline]
  28. Chêne G, Katlama C, Coulaud JP, et al. Clinical progression and adverse events in a cohort of HIV-infected patients with advanced immunodeficiency started on protease inhibitors (PI) in March 1996. Presented at the 12th World AIDS Conference, Geneva, Switzerland, July 1998.
  29. Yerly S, Perneger TV, Hirschel B, et al. A critical assessment of the prognostic value of HIV-1 RNA levels and CD4+ cell counts in HIV-infected patients. Arch Intern Med 1998;158:247–52.[Abstract/Free Full Text]
  30. Drusano GL, Stein DS. Mathematical modeling of the interrelationship of CD4 lymphocyte count and viral load changes induced by protease inhibitor indinavir. Antimicrob Agents Chemother 1998;42:358–61.[Abstract/Free Full Text]
  31. Renaud M, Katlama C, Mallet A, et al. Determinants of paradoxical CD4 cell reconstitution after protease inhibitor-containing antiretroviral regimen. AIDS 1999;13:669–76.[ISI][Medline]
  32. Ledergerber B, Egger M, Opravil M, et al. Clinical progression and virological failure on highly active antiretroviral therapy in HIV-1 patients: a prospective cohort study. Lancet 1999;353:863–8.[ISI][Medline]
  33. Angel JB, Kumar A, Parato K, et al. Improvement in cell-mediated immune function during potent anti-human immunodeficiency virus therapy with ritonavir plus saquinavir. J Infect Dis 1998;177:898–904.[ISI][Medline]
  34. Miller V, Mocroft A, Reiss P, et al. Relations among CD4 lymphocyte count nadir, antiretroviral therapy, and HIV-1 disease progression: results from the EuroSIDA Study. Ann Intern Med 1999;130:570–7.[Abstract/Free Full Text]
  35. Pakker NG, Roos MT, van Leeuwen R, et al. Patterns of T-cell repopulation, virus load reduction, and restoration of T-cell function in HIV-infected persons during therapy with different antiretroviral agents. J Acquir Immune Defic Syndr Human Retrovirol 1997;16:318–26.[ISI][Medline]
  36. Jacobson MA, Zegans M, Pavan PR, et al. Cytomegalovirus retinitis after initiation of highly active antiretroviral therapy. Lancet 1997;349:1443–5.[ISI][Medline]
  37. Michelet C, Arvieux C, François C, et al. Opportunistic infections occurring during highly active antiretroviral treatment. AIDS 1998;12:1875–22.
  38. Powderly W, Landay A, Lederman M. Recovery of the immune system with antiretroviral therapy: the end of opportunism? JAMA 1998;280:72–7.
  39. Pakker N, Notermans D, deBoer R, et al. Biphasic kinetics of peripheral blood T cells after triple combination therapy in HIV-1 infection: a composite of redistribution and proliferation. Nature Med 1998;4:208–14.[ISI][Medline]
  40. Gorochov G, Neumann A, Kereveur A, et al. Perturbation of CD4+ and CD8+ T-cell repertoires during progression to AIDS and regulation of the CD4+ repertoire during antiviral therapy. Nature Med 1998;4:215–21.[ISI][Medline]
  41. Li S, Tubiana R, Calvez V, et al. Long-lasting recovery in CD4-cell function and viral-load reduction after highly active antiretroviral therapy in advanced HIV-1 disease. Lancet 1998;351:1682–6.[ISI][Medline]
Received for publication October 21, 1999. Accepted for publication May 5, 2000.