Should the CD4 threshold for starting ART be raised?

CD4(+) T-cell recovery after initiation of antiretroviral therapy in a resource-limited setting: a prospective cohort analysis

BACKGROUND

CD4(+) T-cell count recovery after antiretroviral therapy (ART) initiation is associated with improved health outcomes. It is unknown how the CD4(+) T-cell counts of African HIV patients recover following ART initiation.

METHODS

We examined CD4(+) T-cell count recovery in a large cohort of HIV-positive patients initiating ART in Uganda between 2004 and 2011. We categorized patients according to their CD4(+) T-cell count at ART initiation. All patients received CD4(+) T-cell count evaluations on a biannual basis. We used quantile regression to model the recovery of CD4(+) T-cells during ART.

RESULTS

A total of 5,271 patients aged ≥14 years at baseline were included. The median number of CD4(+) T-cell count measurements was 6 (IQR 4-8), and vital status at censoring was known in 97.2% of individuals. Most CD4(+) T-cell count recovery occurred within the first 12 months, with marginal increases beyond 18 months and stabilization after 5 years. The strongest predictor of CD4(+) T-cell count recovery was baseline CD4(+) T-cell count. After 5 years on treatment, the median CD4(+) T-cell count was 334 cells/mm(3) for patients initiating ART with <100 cells/mm(3). Only those initiating ART with >200 cells/mm(3) reached a 5-year median >500 cells/mm(3). Adolescents had the most robust CD4(+) T-cell count recovery with a median increase after 12 months that was 109 cells/mm(3) greater than those initiating ART at age ≥50 years.

CONCLUSIONS

In individuals from a resource-limited setting, baseline CD4(+) T-cell count was highly predictive of the maximum CD4(+) T-cell count level achieved while on ART.

Treatment failure and drug resistance is more frequent in HIV-1 subtype D versus subtype A-infected Ugandans over a 10-year study period

OBJECTIVES

To determine the impact of HIV-1 subtype on treatment outcomes and the emergence of drug resistance in the resource limited setting of Kampala, Uganda.

DESIGN

The Joint Clinical Research Centre (JCRC) in Kampala, Uganda has provided over 2000 drug-resistant genotypes (DRGs) over the past 10 years as standard of care for patients failing therapy and 1403 from treatment-naive and experienced patients over the past 10 years have been analyzed for this study.

METHOD

Viral loads, CD4 cell count, treatment histories and other relevant clinical data was compared with the infecting HIV-1 subtype and DRGs of Ugandan patients failing treatment.

RESULTS

Patients failing HAART with DRGs (n = 937) were more frequently infected with subtype D than expected on the basis of the subtype distribution in the treatment-naive population (n = 655) in Kampala (P < 0.001). Higher proportions of treatment failures among subtype D-infected patients were driven by resistance to nucleoside reverse transcriptase inhibitors (NRTI) (P < 0.0002) more than to non-NRTIs (P > 0.04) or protease inhibitors.

CONCLUSION

Higher rates of treatment failure among subtype D as compared with subtype A-infected Ugandans was analogous to the faster disease progression in subtype D-infected patients. The mechanism(s) by which drug resistance may emerge faster in subtype D HIV-1 may relate to higher replicative fitness and increased propensity for a CXCR4 tropism.

Impact of time to start treatment following infection with application to initiating HAART in HIV-positive patients

We estimate how the effect of antiretroviral treatment depends on the time from HIV-infection to initiation of treatment, using observational data. A major challenge in making inferences from such observational data arises from biases associated with the nonrandom assignment of treatment, for example bias induced by dependence of time of initiation on disease status. To address this concern, we develop a new class of Structural Nested Mean Models (SNMMs) to estimate the impact of time of initiation of treatment after infection on an outcome measured a fixed duration after initiation, compared to the effect of not initiating treatment. This leads to a SNMM that models the effect of multiple dosages of treatment on a time-dependent outcome, in contrast to most existing SNNMs, which focus on the effect of one dosage of treatment on an outcome measured at the end of the study. Our identifying assumption is that there are no unmeasured confounders. We illustrate our methods using the observational Acute Infection and Early Disease Research Program (AIEDRP) Core01 database on HIV. The current standard of care in HIV-infected patients is Highly Active Anti-Retroviral Treatment (HAART); however, the optimal time to start HAART has not yet been identified. The new class of SNNMs allows estimation of the dependence of the effect of 1 year of HAART on the time between estimated date of infection and treatment initiation, and on patient characteristics. Results of fitting this model imply that early use of HAART substantially improves immune reconstitution in the early and acute phase of HIV-infection.

Changing pattern of lymphoma subgroups at a tertiary academic complex in a high-prevalence HIV setting: a South African perspective

BACKGROUND

HIV infection has been associated with an increased risk of non-Hodgkin lymphoma, particularly in the first world. Despite the high burden of HIV infection in sub-Saharan regions, published data on HIV and malignancies are sparse from these areas.

MATERIALS AND METHODS

We recently published data on lymphomas diagnosed from January 2004 to December 2006, at a single center in Johannesburg, to serve as a baseline for long-term comparison during the period of highly active antiretroviral therapy rollout. We report a retrospective analysis of the follow-up data collected from January 2007 to December 2009 at the Johannesburg academic hospital complex (Gauteng, South Africa).

RESULTS

There were 2225 new diagnoses of lymphoproliferative disorders made during 2007-2009 as compared with 1897 cases diagnosed during 2004-2006. A significant increase in both high-grade B-cell lymphomas and Hodgkin lymphoma was documented during 2007-2009. This was associated with a statistically significant increase in HIV prevalence in those tested (from 44.3% in 2004-2006 to 62.0% in 2007-2009). HIV-positive patients presented at a statistically significantly younger median age and showed a relative overrepresentation of females when compared with HIV-negative patients. HIV-positive patients were diagnosed at later stages of HIV infection when compared with patients in the first world.

CONCLUSIONS

The pattern of lymphoma subtypes and the demographics of the patients diagnosed have altered in association with significantly increased HIV prevalence. These changes have important public health implications. In particular, scale-up and earlier access to highly active antiretroviral therapy is essential with continued monitoring as access to therapy improves.

Efficacy and safety of maraviroc versus efavirenz, both with zidovudine/lamivudine: 96-week results from the MERIT study

BACKGROUND

The MERIT study evaluated maraviroc versus efavirenz, both with zidovudine/lamivudine, in treatment-naïve patients with CCR5-tropic (R5) HIV-1. Post hoc analyses previously assessed week 48 outcomes in patients rescreened with R5 virus by a more sensitive tropism assay.

METHODS

Week 96 efficacy (post hoc, n = 614) and safety (n = 721) were assessed.

RESULTS

Proportions of subjects <50 copies/mL (58.8% maraviroc, 62.7% efavirenz) and time to loss of virologic response (TLOVR) responders (<50 copies/mL: 60.5% vs 60.7%) were similar. Maraviroc recipients had greater CD4 increases (+ 212 vs + 171 cells/mm(3)) and fewer adverse event discontinuations (6.1% vs 15.5%), malignancies, and category C events.

CONCLUSION

Week 96 data confirm week 48 observations in MERIT.

Long-term increase in CD4+ T-cell counts during combination antiretroviral therapy for HIV-1 infection

OBJECTIVE

To inform guidelines concerning when to initiate combination antiretroviral therapy (ART), we investigated whether CD4(+) T-cell counts (CD4 cell counts) continue to increase over long periods of time on ART. Losses-to-follow-up and some patients discontinuing ART at higher CD4 cell counts hamper such evaluation, but novel statistical methods can help address these issues. We estimated the long-term CD4 cell count trajectory accounting for losses-to-follow-up and treatment discontinuations.

DESIGN

The study population included 898 US patients first initiating ART in a randomized trial (AIDS Clinical Trials Group 384); 575 were subsequently prospectively followed in an observational study (AIDS Clinical Trials Group Longitudinal Linked Randomized Trials).

METHODS

Inverse probability of censoring weighting statistical methods were used to estimate the CD4 cell count trajectory accounting for losses-to-follow-up and ART discontinuations, overall and for pretreatment CD4 cell count categories (<or=200, 201-350, 351-500, and >500 cells/microl).

RESULTS

Median CD4 cell count increased from 270 cells/microl pre-ART to an estimated 556 cells/microl at 3 and 532 cells/microl at 7 years after starting ART in analyses ignoring treatment discontinuations, and to 570 and 640 cells/microl, respectively, had all patients continued ART. However, even had ART been continued, an estimated 25, 9, 3, and 2% of patients with pretreatment CD4 cell counts of 200 or less, 201-350, 351-500, and more than 500 cells/microl would have had CD4 cell counts of 350 cells/microl or less after 7 years.

CONCLUSION

If patients remain on ART, CD4 cell counts increase in most patients for at least 7 years. However, the substantial percentage of patients starting therapy at low CD4 cell counts who still had low CD4 cell counts after 7 years provides support for ART initiation at higher CD4 cell counts.

When to start antiretroviral therapy

The question of when to start combination antiretroviral therapy for treatment-naïve patients has always been controversial. This is particularly true in the current era, with major guidelines recommending very different treatment strategies. Despite a lack of clarity regarding the optimal time to begin therapy, there has been a recent shift toward earlier initiation. This more aggressive approach is driven by several observations. First, effective viral suppression with therapy can prevent non-AIDS-related morbidity and mortality. Second, therapy can prevent irreversible harm to the human immune system. Third, therapy may prevent transmission of HIV to others, and thus have a potential public health benefit. For patients who are motivated and willing to initiate early treatment, the collective benefits of early therapy may outweigh the well-documented risks of antiretroviral medications.