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.
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ABSTRACT |
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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
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INTRODUCTION |
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MATERIALS AND METHODS |
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Immunologic response was measured by the evolution of CD4+ and CD8+ cell counts after treatment initiationi.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 1546 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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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 (13). 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 (14
).
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 (15). 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 () beyond which the effect of CD4+ evolution would be different, this was tested in a Cox model designed as follows:
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The effect of CD4(t) CD4(0) was measured by ß' before time 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 (5
7
). 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 (16).
Models were compared using the Akaike Information Criterion (AIC) (17) 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.
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RESULTS |
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Immunologic response under HAART
During follow-up, a median of nine measurements of CD4+ cell count were available for each patient (interquartile range, 711). 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, 1.78.9 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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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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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 () 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.
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DISCUSSION |
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Other authors had already proposed CD4+ modeling with a mixed linear approach prior to the introduction of HAART (1820
). 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 (14
). 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 (21
) and the fact that there may be considerable variation among individuals in the number and timing of observations (19
). 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 (5
, 22
). Touloumi et al. (23
) 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 (24). 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 (25
).
HIV RNA level has also been shown to be an important predictive factor for clinical endpoints (26). 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 (27
). 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 (28
, 29
). Conversely, a reduction in viral load has been shown to be favorably associated with CD4+ cell reconstitution in both the short term (30
) and the long term (1
3
, 31
, 32
). 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 (6, 31
, 33
), 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 (2
, 3
, 31
). 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 (34
). 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 (35). 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 (5
, 6
). A majority of opportunistic infections occurred soon after initiation of protease inhibitor treatment (36
, 37
), 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 (6
, 38
) to the peripheral blood. Pakker et al. (39
) 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 36 months of treatment (40
), 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 (25) 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. (41
) 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.
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ACKNOWLEDGMENTS |
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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 Aquitaineorganization 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 HospitalPr. 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 HospitalDr. M. Loste (Dr. I. Blanchard); Bayonne HospitalDr. F. Bonnal (Drs. Y. Blanchard, S. Farbos, and M. C. Gemain); Libourne HospitalDr. J. Ceccaldi (Dr. X. Jacquelin); Villeneuve-sur-Lot HospitalDrs. 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.
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NOTES |
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REFERENCES |
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