The medical management of HIV infection has evolved rapidly since the introduction of HAART in 1996 [1–7]. This is largely due to advances in virologic monitoring, including viral load assays and resistance testing, and the availability of new drugs and new drug classes, fixed dose combinations. Full and long-term suppression of HIV-1 RNA plasma viral load (pVL) is now the accepted goal of therapy at all stages of HIV disease even among those infected with multiple drug-resistant HIV. As a result, HAART can predictably suppress viral replication, which in turn allows for immune reconstitution to take place, preventing the emergence of resistance and AIDS-related morbidity and mortality [8–13].
Several studies [10–12] have demonstrated that high levels of adherence are needed to secure long-term therapeutic benefit from HAART. However, recent data suggest that the resilience to incomplete adherence may differ among various HAART regimes. A reassessment of the relationship between varying levels of adherence and HAART outcomes is, therefore, needed, taking into account different HAART regimens currently recommended [1–7].
Current therapeutic guidelines define treatment failure based on virologic (pVL increase, drug resistance), immunologic (CD4 cell count response), and clinical (emergence of AIDS-defining conditions or mortality) criteria [6,7]. However, the vast majority of studies evaluating the relationship between adherence and HAART outcomes have been focused on virologic failure [6–9]. Hence, further studies relating to adherence and clinical outcomes among HAART-treated patients stratified by type of regimen are needed.
We, therefore, undertook the present study to examine the long-term impact of different adherence levels on virologic and immunologic responses stratified by type of HAART regimen in treatment-naive patients starting HAART in British Columbia, Canada. We further sought to assess the degree of virologic and immunologic response that is associated with emergence of drug resistance, progression to AIDS, and mortality.
HIV patients on treatment in British Columbia
This study was conducted using data from the British Columbia Centre for Excellence in HIV/AIDS (the Centre). In brief, since 1992 the Centre has distributed antiretroviral agents at no cost to all eligible HIV-infected individuals through its HIV/AIDS Drug Treatment program according to specific guidelines generated by the Therapeutic Guidelines Committee. The Centre's guidelines have remained consistent with those put forward by the International AIDS Society-USA (IAS-USA) since 1996 [1–6]. The details of this program have been described elsewhere [13,14]. The Centre has received ethical approval from the University of British Columbia Ethics Review Committee at the St Paul's Hospital site
Eligible study participants were of at least 18 years of age, naive to antiretroviral therapy when they started HAART consisting of two nucleosides, or a nucleoside and a nucleotide reverse transcriptase inhibitor plus either a nonnucleoside reverse transcriptase inhibitor (NNRTI), or a protease inhibitor boosted with 200 mg/day ritonavir or less (boosted protease inhibitor), or a single protease inhibitor (nonboosted protease inhibitor). Participants started HAART between 1 January 2000 and 30 November 2004, were followed until 30 November 2005, and had at least 2 years of follow-up. Finally, to be eligible for analysis, participants were required to have at least one baseline CD4 cell count and a pVL measurement available within 6 months prior to the first antiretroviral starting date. Study data from eligible participants were extracted from the Centre's monitoring and evaluation system to form the HAART Observational Medical Evaluation and Research (HOMER) cohort [11,13–15].
The Centre's guidelines recommend that pVL and CD4 cell count be monitored at baseline, at 4 weeks after starting antiretroviral therapy, and every 3 months thereafter. The Roche Amplicor Monitor ultrasensitive assay (Roche Diagnostics, Laval, Quebec, Canada) is used to measure pVL centrally at the St Paul's Hospital virology laboratory in British Columbia. CD4 cell counts are measured by flow cytometry, followed by fluorescent monoclonal antibody analysis (Beckman Coulter, Inc., Mississauga, Ontario, Canada). HIV drug resistance genotyping is performed centrally at the Centre's laboratory on samples with pVL of at least 250 copies/ml collected at baseline and following initiation of HAART. Samples have been assigned to one of four resistance categories based on a modification of the IAS-USA table [16,17]. Samples are considered resistant if they display one or more major resistance mutations in any of the four categories: lamivudine/emtricitabine (184I/V); any other nucleoside reverse transcriptase inhibitors (41L, 62V, 65R, 67N, 69D or insertion, 70R, 74V, 75I, 151M, 210W, 215F/Y or 219E/Q); any NNRTIs (100I, 103N, 106A/M, 108I, 181C/I, 188C/H/L, 190A/S, P225H, M230L or 236L); and any protease inhibitors (30N, 46I/L, 48V, 50L/V, 54V/L/M, 82A/F/S/T, 84V, or 90M). Lamivudine/emtricitabine resistance is considered a separate resistance category because of its high frequency and the lack of cross-resistance conferred to other nucleoside reverse transcriptase inhibitors.
Outcome measures and statistical analyses
Virologic and immunologic cut-offs were based on the median distributions of patients according to each outcome, as these outcomes did not adhere to a symmetric distribution. Therefore, we decided to let the data define these limits. Patients were then classified into mutually exclusive groups based on the presence (+) or absence (−) of virologic and immunologic responses. Virologic response was defined as the percentage of follow-up time with pVL less than 50 copies/ml dichotomized at the 65% mark. Immunologic response was defined as a median CD4 cell count increase between baseline and the end of follow-up of at least 145 cells/μl.
In this dataset, patients have multiple data points for CD4 cell count over time. To summarize the data, we calculated the area under the curve (AUC). We used this technique given that observations within a given patient are not independent. The trapezoidal rule was used in this case. This rule is a numerical integration method used to approximate the AUC .
Proportional odds models were used to assess the impact of adherence and regimen on response, adjusting for other explanatory variables [19–22]. A backward stepwise technique was used in the selection of covariates for an explanatory model . The selection of variables was based on two criteria: Akaike Information Criterion (AIC) and Type III P values. These two criteria balance the model choice on finding the best explanatory model (Type III P values – lower P values indicate more significance) and at the same time a model with the best goodness-of-fit statistic (AIC – lower values indicate better fit). At each step of this process, the AIC value and the Type III P values of each variable are recorded, and the variable with the highest Type III P value is dropped, until there are no more variables left. The final model has the lowest AIC. Categorical variables were compared using the Fisher exact test and continuous variables were compared using the Wilcoxon rank sum test.
The main exposures in this analysis were adherence level and type of HAART regimen (NNRTI, boosted, and nonboosted protease inhibitors). Estimates of adherence to antiretroviral therapy were defined as the number of days of antiretroviral drugs dispensed divided by the number of days of follow-up (expressed as percentage) [10,11,13,14]. For this study, we limited our measure of adherence to the first year of therapy. This measure of adherence has been found to be independently associated with HIV viral suppression and survival among HAART-treated HIV-infected persons [10,11]. Adherence was categorized into four groups: 0–<40%, 40–<80%, 80–<95%, and ≥95% . The other variables investigated were baseline factors, including age, sex, CD4 cell count, pVL (log10 transformed), AIDS diagnosis, history of injection drug use, year of first therapy, time on therapy, and physician's experience. Physician's experience was defined as the number of HIV-positive patients the follow-up physician had previously treated at the time the study participant was enrolled into the Centre .
The outcomes in this analysis were emergence of drug resistance (yes/no); emergence of an AIDS-defining illness (yes/no); and death (yes/no). Deaths occurring during the follow-up period were identified on a continuous basis from physician's reports and through record linkages carried out with the British Columbia Division of Vital Statistics [13,15].
In these analyses, we built a confounder model with the main exposure being the variable based on the presence (+) or absence (−) of virologic and immunologic responses. Logistic regression was used to assess the impact of this exposure on each disease outcome. Potential confounders included baseline factors such as age, sex, CD4 cell count, pVL (log10 transformed), AIDS diagnosis, history of injection drug use (IDU), year of first therapy, adherence (0–<40%, 40–<80%, 80–<95%, and ≥95%), first regimen, and physician's experience. Potential confounders were selected for inclusion in the final models using a backward selection approach, which considered the magnitude of change in the coefficient of the exposure variable. Starting with a fixed model, which considered all available variables, potential confounders were dropped one at a time, using the relative change in the coefficient for the variable related to the exposure variable as a criterion, until the maximum change from the full model exceeded 5% [24,25]. All analyses were performed using SAS software version 9.1.3 Service Pack 3 (SAS Institute Inc., Cary, North Carolina, USA).
Overall cohort characteristics
A total of 878 antiretroviral-naive adults (80% men) were eligible to participate in this study. At baseline, the median age was 40 years [interquartile range (IQR): 34–47 years], CD4 cell count was 165 cells/μl (IQR: 70–270 cells/μl), pVL was 5.0 log10 copies/ml (IQR: 4.7–5.1 log10 copies/ml), and the median number of patients for whom physicians had written HAART prescriptions was 97 patients (IQR: 11–213 patients). Of these, 28% had a history of IDU, 17% had AIDS at baseline, 34% were first prescribed therapies containing boosted protease inhibitors, and 38% of participants were less than 95% adherent during the first year of follow-up. The median percentage of time with virologic suppression was 65% (IQR: 31–86%), the median CD4 cell count increase during follow-up was 145 cells/μl (IQR: 75–235 cells/μl), and median CD4 cell AUC (i.e. median CD4 cell count increase during entire follow-up) was 456.3 cells/μl (IQR: 112.7–827.9 cells/μl). The overall median follow-up was 3.7 years (IQR: 2.8–4.8 years).
Factors associated with virologic and immunologic response
The distribution of patient responses was 394 (44.9%) for CD4+/pVL+ (best), 350 (39.9%) for CD4−/pVL+ or CD4+/pVL− (incomplete), and 134 (15.3%) for CD4−/pVL− (worst). Responses CD4−/pVL+ and CD4+/pVL− were grouped because of a small sample size in the response group CD4−/pVL+ (48 patients). We found a positive correlation between adherence and percentage of time with suppressed pVL and immunologic response (P < 0.01). The distribution of individuals by percentage of follow-up time with suppressed pVL had a U-shape, with 12% of individuals never achieving viral suppression during follow-up and 18.7% being suppressed for at least 90% of their follow-up time. The median CD4 cell change from baseline stratified by follow-up time is shown in Fig. 1a. At 48 months of follow-up, the best response group showed a dramatic increase in CD4 cell count from baseline, with a median CD4 cell change of 270 cells/μl (IQR: 130–380 cells/μl). Highly adherent patients (≥95%) achieved a median suppression 78.8% (IQR: 55.6–90.0) of the follow-up time, with a median area under the CD4 cell curve of 616 (IQR: 312–980) (Fig. 1b,c). Further, if patients' pVL was suppressed for at least 65% of follow-up time, these patients were very likely to experience a substantial CD4 response. The pretherapy median CD4 nadir count was 130 cells/μl (IQR: 50–210 cells/μl) for the best, 150 cells/μl (IQR: 60–260 cells/μl) for the incomplete, and 160 cells/μl (IQR: 50–280 cells/μl) for the worst responses (P = 0.01).
A further breakdown of virologic and immunologic responses by baseline characteristics is provided in Table 1. Factors such as male sex, no history of IDU, boosted protease inhibitor and NNRTI regimens, rate of adherence of at least 95%, CD4 cell count less than 350 cells/μl, physicians with more experience and older age were associated with best response. Female sex, history of IDU, nonboosted protease inhibitor regimens, adherence level ranging from 40 to less than 95%, and CD4 cell count of at least 350 cells/μl were factors associated with incomplete response. Adherence rate of less than 40% and high CD4 nadir count were factors associated with worst response (P < 0.001). Year of first antiretroviral, pVL, AIDS diagnosis at baseline, and follow-up time were not significantly associated with virologic and immunologic response.
Table 2 shows the results for the estimated model-based probabilities for different virologic and immunologic responses, stratified by adherence and HAART regimen. Overall, having 0–<40%, 40–<95%, and ≥95% adherence was associated with high probabilities of having worst, incomplete, and best responses, respectively. When we additionally stratified these probabilities by HAART regimen, we observed that if individuals were at least 95% adherent, they were more likely to have best responses, unless they were prescribed nonboosted protease inhibitor regimens. If the adherence was between 80 and less than 95% and individuals were on NNRTI-based regimens, they were likely to have either best or incomplete responses. Every other adherence/regimen combination will result in either an incomplete or worse response.
Effect of virologic and immunologic response on HAART outcomes
Among the study sample of 878 individuals, 68 [rate: 77.4 per 1000 population; 95% confidence interval (CI): 59.0–95.9 per 1000 population] individuals died during the study period. A total of 42 (rate: 47.8 per 1000 population; 95% CI: 33.4–62.3 per 1000 population) individuals developed new AIDS-defining illnesses and 164 (rate: 47.8 per 1000 population; 95% CI: 33.4–62.3 per 1000 population) individuals developed drug resistance. At the end of follow-up, 714 individuals (81.3%) did not develop resistance to any class and they were more likely to have best response. The remaining 164 (18.7%) individuals developed resistance to at least one class and were more likely to have worst response (P < 0.01). When only patients with incomplete responses were compared to those with best responses (results not shown in table), there was no difference between these two groups of patients regarding the outcomes, death and AIDS.
Table 3 shows the results for the multivariate analysis of virologic and immunologic responses and each disease outcome, adjusted for potential baseline confounders. Individuals with incomplete response were at a lower risk of new AIDS events compared to those with best response. Note that in total, there were 42 new AIDS events during follow-up, with eight (19.0%) new events in the incomplete group in comparison with 20 (47.6%) new events in the best group. Worst response compared with best (reference group) response was associated with higher odds of mortality [odds ratio (OR): 6.09; 95% CI: 2.57–14.42], progression to AIDS/death (OR: 3.25; 95% CI: 1.58–6.68), and emergence of drug resistance (OR: 10.56; 95% CI: 5.93–18.81) even after adjusting for adherence and HAART regimen. Note that the risk of emergence of drug resistance for the incomplete responses versus best (reference group) response was also highly significant (OR: 8.37; 95% CI: 5.05–13.87).
The therapeutic guidelines are clear on the management of patients with combined virologic and immunologic failure. The goal is to reduce pVL and therefore increase CD4 cell count. However, clinical management of patients with discordant virologic and immunologic responses to HAART therapy is more problematic and it may not be clear what effect discordant results have on long-term clinical outcomes. Despite today's antiretroviral regimens being more forgiving to decreased adherence than those used in the past, other authors [26–28] have shown that therapy with lower rates of adherence can still reduce pVL and increase CD4 cell counts. Our results show that adherence to therapy is a key feature influencing both virologic and immunologic responses. When adherence was below the 95% cut-off, patients were more likely to experience discordant or poor responses to treatment. The importance of very high adherence to therapy may be more apparent in our study than in others as the cut-offs for CD4 and pVL responses were designed to be quite rigorous. The significance of this finding is likely that, in order to obtain the most optimal suppression of viral replication, and subsequently, T-cell recovery associated with improved disease outcome, very high adherence rates are necessary [29,30].
Our study also found that the type of HAART regimen played an important role in virologic and immunologic outcomes. Among patients with high adherence, NNRTI and boosted protease inhibitor-based regimens provided the highest likelihood of virologic suppression and CD4 recovery. We also showed that patients on nonboosted protease inhibitors were at a greater risk of poor disease outcomes regardless of their adherence level. Previous work has shown that at least 95% adherence to nonboosted protease inhibitor regimens is required for pVL suppression, and that less than this level of adherence results in significantly more resistance . Taken together, when initiating someone on therapy, especially when their ability to adhere to the regimen is questionable, boosted protease inhibitor-based or NNRTI-based regimens appear to be more suitable to potentially imperfect levels of adherence.
It is also important to mention that individuals with incomplete responses were also at a high risk of developing drug resistance, especially to two or more drugs. Note that the development of drug resistance in this group of patients will not necessarily lead to an immediate clinical adverse event such as AIDS or death but, in the long-term, data from studies with long follow-up suggest that these individuals are at a risk of these clinical adverse events .
Finally, we demonstrated that patients with poor virologic and immunologic responses were at least three times more likely to die, to progress to AIDS/death, and to develop resistance to antiretroviral drugs than those with the best responses, even after controlling for adherence and type of HAART therapy. Of note, there was no effect of virologic and immunologic responses just on the emergence of new AIDS-defining conditions, which may be explained by the few events associated with this outcome.
There are several novel aspects to this study. First, we used the percentage of time an individual experienced virologic suppression to define virologic response. Using a higher cut-off in this last measure makes our definition more strict than most of other studies in which the definition of virologic suppression was two consecutive pVLs less than the limit of quantification of their study's assays [17,26,27,31–50]. This definition is highly susceptible to the common intermittent viremia experienced by individuals receiving antiretroviral therapy, and it is most likely to be due to variability in adherence levels over time. Therefore, we believe that our definition is more robust to the high variability that can be seen in measuring viremia over time. Second, we used an increase of at least 145 cells/μl to define immunologic response. This is a stringent definition and it is based on the full patients' history since their treatment initiation. Previous definitions of immunologic response have typically been arbitrary and varied substantially in the literature . CD4 cell count increases of 25–100 cells/μl above the baseline have been used to define immunologic response. Third, our cohort had a follow-up of approximately 4 years, included patients initiating therapy receiving the three most common classes of antiretroviral drugs, and offered comprehensive adherence information. In contrast, most previous studies [27–45] focused on a particular regimen class (nonboosted protease inhibitors being the most common), lacked comprehensive adherence information, and evaluated short-term responses to therapy (usually 6 months). Fourth, our study was carried out within a province-wide treatment program providing free access to medical attention, combination antiretroviral therapy, and laboratory monitoring. We are confident, therefore, that our results are not highly influenced by access to therapy, a factor that has often compromised the interpretation of other population-based and cohort-based studies. Finally, it is interesting to note that the explanatory model for virologic and immunologic response showed that among the baseline clinical parameters (AIDS, pVL, and CD4), baseline CD4 cell count, as in other studies [17,40,50], highly influenced the long-term response to therapy.
There are important potential limitations in our study. First, we used pharmacy-refill compliance at the end of the first year of therapy as a surrogate for adherence. This is a conservative measure of adherence; however, this measure has been found to be independently associated with HIV viral suppression and survival among HIV-infected individuals enrolled in the HIV/AIDS Drug Treatment Program [10,11]. Second, although we adjusted our analyses for pertinent demographic and clinical characteristics, residual confounding may exist among observational study populations, and for this reason caution is warranted. As shown in our results, individuals with high baseline CD4 cell counts were more likely to be classified as not having an immunological response and they were also more likely to have a lower pVL at the start of their therapy. One possible explanation for their poor CD4 cell rebound may have been their poor adherence to the prescribed therapy. Or perhaps, those with initial high CD4 cell counts are less likely to have a bigger response due to a ceiling effect. Third, the data of patients in the groups CD4+/pVL− and CD4−/pVL+ were combined into one category because of sample size. It is important to mention that there is a big difference between someone who does not respond virologically and someone who does not mount a normal immune recovery either due to starting out with a very low CD4 cell count or achieving the very high standard of immunologic recovery as defined in this article. This is clearly a big limitation, but nonetheless, we clearly identified that adherence and HAART regimens are key factors for achieving best treatment responses.
In summary, we demonstrated that incomplete or poor virologic and immunologic responses are associated with emergence of drug resistance and disease progression. Further, these responses are highly dependent on the adherence level and initial HAART regimen, with ritonavir-boosted protease inhibitor-based and NNRTI-based HAART regimens having the highest resilience to incomplete adherence.
We thank Benita Yip, Nada Gataric, Kelly Hsu, Elizabeth Ferris, Myrna Reginaldo, Marnie Gidman, and Peter Vann for their administrative assistance.
Author contributions: Study concept and design: V.D.L., R.H., M.M., D.M.M., E.W., R.S.H., J.S.G.M.; Acquisition of data: R.S.H., J.S.G.M.; Analysis and interpretation of data: V.D.L.; Drafting of the article: V.D.L.; Critical revision of the article for important intellectual content: V.D.L., R.H., M.M., D.M.M., E.W., R.S.H., J.S.G.M.; Statistical analysis: V.D.L.; Obtained funding: R.S.H., V.D.L., J.S.G.M.; Administrative, technical, or material support: R.S.H., J.S.G.M.; Study supervision: R.H., J.S.G.M.
Ethical approval: The Centre's HIV/AIDS Drug Treatment program has received ethical approval from the University of British Columbia Ethics Review Committee at its St Paul's Hospital site. The program also conforms to the province's Freedom of Information and Protection of Privacy Act.
Financial disclosures: R.S.H., J.S.G.M., D.M.M., and R.H. have received honorariums, travel grants to attend conferences and research grants from pharmaceutical companies working in the area of HIV/AIDS. V.D.L., E.W., and M.M. declare no conflict.
Role of the sponsors: The funding sources had no role in the choice of methods, the contents or form of this work, or the decision to submit the results for publication.
Funding: This work was supported by the Canadian Institutes of Health Research through a Fellowship Award to V.D.L.
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