A Pilot Study Evaluating Time to CD4 T-cell Count <350 cells/mm3 After Treatment Interruption Following Antiretroviral Therapy ± Interleukin 2: Results of ACTG A5102 : JAIDS Journal of Acquired Immune Deficiency Syndromes

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A Pilot Study Evaluating Time to CD4 T-cell Count <350 cells/mm3 After Treatment Interruption Following Antiretroviral Therapy ± Interleukin 2: Results of ACTG A5102

Henry, Keith MD; Katzenstein, David MD; Cherng, Deborah Weng MS; Valdez, Hernan MD; Powderly, William MD; Vargas, Michelle Blanchard MA; Jahed, Nasreen C. MPH; Jacobson, Jeffrey M. MD; Myers, Laurie S. MS; Schmitz, John L. PhD; Winters, Mark MS; Tebas, Pablo MD A5102 Study Team of the AIDS Clinical Trials Group

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JAIDS Journal of Acquired Immune Deficiency Syndromes 42(2):p 140-148, June 2006. | DOI: 10.1097/01.qai.0000225319.59652.1e
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Potent antiretroviral therapy (ART) has resulted in a marked decrease in the morbidity and mortality and dramatic immune reconstitution among patients with advanced HIV disease.1,2 Multiple studies have demonstrated that ART is generally associated with significant increases in CD4 T-cell counts and a reduction of incidence of opportunistic infections upon stopping prophylaxis.3-7

The current paradigm of treating HIV infection recommends lifelong ART.8-10 However, to spare long-term drug cost and toxicities, CD4 T-cell count-based intermittent treatment might be an alternative therapeutic approach; upon falling below a designated threshold CD4 T-cell count, patients would reinitiate ART for a limited period to suppress viral replication and increase the CD4 T-cell count above a safe target value (as depicted in Fig. 1A). ART may be interrupted in repeated cycles to maintain the CD4 T-cell count above a level associated with significant risk of opportunistic infections while minimizing drug exposure, costs, and toxicities. This strategy is being evaluated in large randomized trials such as STACCATO and Strategies for Management of Anti-Retroviral Therapy (SMART).11,12 Recently, enrollment into SMART, a large international HIV/AIDS trial comparing continuous ART with episodic drug treatment guided by levels of CD4 T-cells, has been stopped because the patients receiving episodic therapy had twice the risk of disease progression.13 Because of the increased risk for disease progression, immunologic or pharmacologic interventions need to be evaluated to more effectively and safely prolong treatment interruption (TI) by increasing the CD4 T-cell count before or during the TI, with or without an effect on the rate of CD4 T-cell decline (as depicted in Fig. 1B).

A, Intermittent or "pulse" therapy concept. ART is stopped and restarted at specific thresholds. The duration of the times off and on ART varies in each individual. B, The impact of potential interventions on an intermittent ART strategy: Line A (green): Spontaneous CD4 decay after ART discontinuation. Therapy has to be restarted at time X. Line B (red): Interventions (red arrows) affect the slope of the CD4 decay after treatment discontinuation. Therapy has to be restarted at time Y. The benefit of the intervention is the difference between Y and X. Line C (blue): Interventions that increase the CD4 before TI without affecting the slope of the CD4 decay. Therapy has to be restarted at time Y. The benefit of the intervention is the difference between Y and X. C, A5102 Steps and Disposition. The yellow areas represent periods of time where the participants receive ART. The duration of these phases is indicated below the dotted line. The black arrows represent IL-2 cycles. The white area represents variable periods of time where the subject is not receiving ART. The larger numbers below the dotted line represent the number of participants in each step at the time of analysis.

In this pilot study we used intermittent potent ART, started and interrupted based exclusively on CD4 T-cell counts, with or without interleukin-2 (IL-2), as an intervention that could potentially prolong the time off ART and prevent clinical events. We focused on the effect of IL-2 on CD4 T-cell counts during TI and identified predictors of the CD4 T-cell decay and the emergence of drug resistance mutations associated with virologic rebound.



HIV-1-infected subjects at least 18 years old who had been on a stable ART regimen for more than 6 months with virologic suppression (HIV RNA <200 copies/mL) and good immune function (CD4 cell count ≥500 cells/mm3) were eligible. ART was defined as 3 or more antiretroviral agents or dual protease inhibitors (PIs). Subjects could not have previously failed treatment (defined as HIV-1 RNA level ≥500 copies/mL3 on 2 occasions >6 months) after starting a regimen, including either a PI or a nonnucleoside reverse-transcriptase inhibitor (NNRTI). Patients with a history of mono or dual nucleoside reverse-transcriptase inhibitor therapy were allowed into the study. Previous IL-2 use was excluded. Subjects were stratified at randomization based on their lifetime documented CD4 cell count nadir of at most 200/mm3 or more than 200/mm3, when available. The protocol was approved by the Institutional Review Board of each of the participating institutions, and all subjects provided informed consent.


Subjects entering the study were randomly assigned to 2 treatments; Arm A = potent ART + 3 cycles of IL-2 (cycle = 4,500,000 IU, SC, bid × 5 days every 8 weeks) or Arm B = potent ART alone (control arm). After 18 weeks of continuous ART with or without 3 cycles of IL-2 (step 1), ART was discontinued (step 2). Subjects receiving NNRTIs were recommended to discontinue their NNRTI at least 24 to 48 hours before discontinuing their other drugs. Subjects were followed monthly with monitoring of their CD4 T-cell counts and plasma HIV-1 RNA levels. ART was restarted when the CD4 T-cell count was confirmed to be <350 cells/mm3 (step 3). At that point ART was restarted for 6 months, and if the CD4 cells again reached at least 500/mm3, the cycle was repeated (steps 4-7; Fig. 1C).


Recombinant human IL-2 (Proleukin) was provided by Chiron Corporation. ART was provided by prescription through the subject's physician. There were protocol-defined recommendations for the management of IL-2 toxicities during step 1 of the study.

Safety Assessments

Safety was assessed using standard AIDS Clinical Trials Group (ACTG) criteria for assessing the occurrence of grade 3 or higher signs and symptoms, laboratory abnormalities, or adverse events.14 Drug resistance mutations, as assessed by genotype of emergent virus after TI, were considered as a potential adverse event.


CD4 and CD8 T-cell counts were measured using standard flow cytometry techniques at the same local ACTG-certified flow laboratory throughout the study (every 4 weeks during step 1 and at weeks 0, 2, 4, 8, and every 4 weeks thereafter on step 2). HIV viral load was determined at weeks 0, 8, and 16 of step 1 and weeks 2, 4, 6, 8, 16, and every 8 weeks thereafter in step 2 using the UltraSensitive Roche Amplicor HIV-1 Monitor assay. Genotypic resistance analysis was performed by population sequencing of genomic DNA (during step 1) and by RT-polymerase chain reaction from the first plasma specimen with HIV RNA more than 500 copies/mL after TI by dideoxynucleoside sequencing.15,16 Mutations associated with drug resistance were identified using the HIVSeq program17 (GenBank accession numbers are DQ009810-DQ009864). The number and percentage of naive (CD45RA+/62L+) and memory (CD45RO+) CD4 T cells, the percentage of CD4 and CD8 T cells expressing CD28, and the percentage of CD8 T cells expressing activation markers (CD38+/HLA-DR+) were measured at entry and at week 16 of step 1 and at weeks 2, 4, and 8 of step 2 (TI) using 3-color flow cytometry following ACTG consensus methodology.18


The primary objectives of this study were to evaluate the safety of TI and change in CD4 T cells during the first TI, including a randomized comparison of ART + IL-2 versus ART alone, on the time to CD4 levels of <350 cells/mm3 during step 2. Secondary objectives included evaluation of HIV-1 RNA viral load, proviral DNA level, HIV resistance mutations, T-cell subset levels, and assessment of adverse and clinical events.

Statistical Analysis

This planned primary intention-to-treat analysis was conducted when the last participant had completed at least 12 months of follow-up after the initial discontinuation of ART (step 2). Because of the pilot nature of the study, all statistical tests were exploratory, 2-sided, 5% level of significance without adjustment for multiple testing. The Wilcoxon rank sum and signed rank, Fisher exact, and log-rank tests were used to compare quantitative outcomes between 2 groups to test if the median of changes in 1 group was different from zero, to compare categorical outcomes between 2 groups, and to compare the times to an event (CD4 T cells falling below 350 cells/mm3) between 2 groups, respectively. Cox proportional hazard models were also used to analyze times to an event. Mixed-effect nonlinear models were fitted to the CD4 T-cell declines over time on step 2, within treatment arm. Because most subjects had CD4 T-cells counts above 350 cells/mm3 longer than that we had expected when the trial was planned, the low number of events (CD4 counts <350 cells/mm3) meant that only one predictive factor at a time could be evaluated in these Cox proportional factor analyses.

All subjects were enrolled at clinical research sites affiliated with the AIDS Clinical Trials Group in the United States. The original sample size of 40 per arm was calculated by assuming a 10% dropout rate. With 36 evaluable subjects per arm, the 95% confidence interval of estimated step 2 CD4 T-cell slope is within 0.65 of the standard deviation of the slope distribution. Because of the pilot nature of the study and slower-than-expected accrual, enrollment was closed after 47 subjects entered the study. Subjects were randomized with equal probability to Arm A and Arm B by permutation blocks with one stratification factor and main institutional balance. The allocation sequence was generated at the Data Management Center of Frontier Science. Laboratories performing the main outcome measure (CD4 T-cell counts) were blinded to the treatment assignment.


The study opened on June 11, 2001 and closed to accrual on January 19, 2003. Forty-seven subjects participated (IL-2 group = Arm A, n = 23; control group = Arm B, n = 24). The disposition of all study patients at the time of study closure is shown in Figure 2. Ten subjects voluntarily dropped out of the study (5 from each study arm) during step 2 (8 due to subject or private physician concern about being off ART, 1 due to pregnancy, and 1 because of diagnosis of squamous cell carcinoma). Baseline demographic and laboratory features for the study subjects are summarized in Table 1. Study subjects were mostly men (91%), white (81%), with a median age of 42, and a median CD4 T-cell count at the time of enrollment of 810 cells/mm3. Nadir CD4 T-cell count (before the initiation of ART) was available for 42 of 47 (median, 344 for Arm A and 314 cells/mm3 for Arm B) participants (P value for difference = 0.18). The median pre-ART log10 HIV RNA level was 4.42 (Arm A) and 4.50 (Arm B). Twenty-one of the study subjects were on a PI-based regimen. Twenty-four of the subjects had received nucleoside reverse-transcriptase inhibitors (NRTIs) for 2 to 5 years before starting potent ART. The distribution of subjects at the time of this analysis (data retrieved on March 30, 2004) by step is shown in Figure 1C representing a median follow-up since TI of 78 weeks with 14 of the study subjects off study at the time of the analysis.

Flow of study subjects through steps 1 and 2 through end of study.
Baseline Demographics of ACTG A5102 Subjects

CD4 Evolution and Time to Restart ART

The CD4 T-cell count was balanced between the 2 treatment groups at baseline (median CD4 T-cell count was 791 cells/mm3 for Arm A and 819 cells/mm3 for Arm B). At the beginning of step 2, the median CD4 T-cell count had increased to 1331 cells/mm3 for subjects in the IL-2 arm (Arm A) but did not change significantly (757 cells/mm3) for subjects in arm B (P < 0.0001, Wilcoxon rank sum test). After TI the CD4 T-cell count decreased rapidly for several months then fell more slowly (more rapid initial decrease seen in the IL-2 arm), as shown in Figure 3A. The median CD4 T-cell counts at weeks 24, 48, and 72 after TI were 666, 671, and 614 cells/mm3 for Arm A and 540, 463, and 534 cells/mm3 for Arm B, respectively (CD4 T-cell data shown in Fig. 3A). IL-2 did not prolong the time to CD4 T-cell count less than 350 cells/mm3 with convergence of the CD4 T-cell levels as shown in Figure 3A, C (P = 0.749, log-rank test). The difference between CD4 T-cell counts was significant only at week 48 (P value for difference at weeks 24, 48, and 72 was 0.0699, 0.0456, and 0.9586, respectively, by Wilcoxon test). Subjects who received IL-2 maintained significantly higher CD4 cells when measured over a 72-week period (P = 0.0003, Wei-Johnson method).

Surrogate marker results during step 2 (TI). A, CD4 T-cell counts. B, Plasma HIV RNA levels. C, KM plot-time to restart ART.

Viral Rebound Characteristics

There were no differences in the magnitude of the virologic rebound measured at week 8 of step 2 (median log10 HIV-1 RNA level = 4.23 for Arm A and 4.20 for Arm B) or in the viral set point HIV-1 RNA level after week 8 (HIV-1 RNA data shown in Fig. 3B). Rebounding HIV-1 RNA values >500 copies/mL were identified at 2 weeks in 20 of 45, after 4 weeks in 35 of 45, and for 45 of 45 by week 8 of TI. There was no statistical difference in time from the start of TI to HIV-1 viral load rebound between the 2 study arms (P = 0.3099, Wilcoxon test), the magnitude of the viral load (median = 4.44 log10 for Arm A and 4.37 for Arm B with P = 0.7455, Wilcoxon test) or in the median time to reach viral load SP (16 weeks for both treatment arms).

Baseline HIV-1 DNA sequence information was obtained from the peripheral blood mononuclear cells (PBMCs) from 43 (91%) of 47 of study subjects, and PBMC proviral DNA drug resistance mutations were identified in 4 of the 22 subjects with a documented history of remote, dual nucleoside treatment. HIV RNA sequences were obtained from the first detectable virus in 45 (98%) of 47 of subjects. Ten (23%) subjects had evidence of resistance mutations in HIV RNA, including the 4 with PBMC resistance mutations, and 6 without detectable evidence of archival mutations in consensus DNA sequence (Table 2). Twenty-four subjects were on an NNRTI-based regimen (efavirenz = 11 and nevirapine = 13). Despite the precaution of recommending stopping the NNRTI component at least 1 to 2 days before the NRTI component of the regimen (due to the long plasma half-life of those NNRTIs), a new NNRTI resistance mutation was found in 2 of these 24 subjects (one subject each on a nevirapine- or efavirenz-based regimen developed the Y181C and K103BN mutation, respectively) in emergent plasma RNA at 2 weeks (Table 2).

Ten Subjects With Drug Resistance Mutation(s) in HIV RNA at Viral Rebound

Factors Associated With CD4 T-Cell Decline During TI

We evaluated the associations between time to CD4 T-cell count less than 350 cells/mm3 during TI and several biologic markers measured before and after TI using a Cox proportional hazard model with 1 predictive variable at a time (Fig. 4). The baseline variables evaluated were treatment assignment, age, step 1 ART including PI or not, pro-viral DNA level, nadir CD4 T-cell count, naive CD4 T-cell count at entry, change from nadir CD4 T-cell count to step 1 baseline, change from nadir CD4 T-cell count to step 2 baseline, and percent-activated CD8 T-cell count at entry. The markers evaluated during TI were log10 HIV-1 RNA set point (defined as 2 consecutive log10 HIV-1 RNA levels within ≤0.3 log after at least 8 weeks of TI), HIV viral load at week 8 of TI, weeks to viral load rebound, maximum HIV RNA level between weeks 8 and 40 of TI, percent of activated CD8+ T-cell count at week 8 of TI, and the presence of detected resistance mutations at virologic breakthrough. Of the baseline variables analyzed, only a higher nadir CD4 T-cell count before the initiation of highly active antiretroviral therapy [unit = 50 cells; HR (hazard ratio) = 0.49 with 95% confidence interval (CI) 0.33-0.73; P= 0.0003] and a higher naive CD4 T-cell count at entry (unit = 50 cells, HR = 0.77 with 95% CI = 0.62-0.96; P = 0.02) were associated with a longer time of CD4 T-cell count above 350 cells/mm3 (Fig. 4A).

Factors associated with time to CD4 <350 cells/mm3.

Among variables during the TI, only the detection of drug-associated mutations at virologic breakthrough (HR = 9.11; 95% CI = 2.81-29.57; P = 0.0002) and a higher log HIV-2 RNA set point (unit = 1 log10 copy, HR = 3.88; 95% CI = 1.05-14.26: P = 0.042) were significantly associated with a shorter time to CD4 T-cell count less than 350 (Fig. 4B). Maximum HIV RNA and the SP between weeks 8 and 40 of TI were highly correlated so the viral SP was selected as the variable of interest. A moderately significant relationship was also noted between the nadir CD4 T-cell count and the weeks 24 and 48 post-TI CD4 T-cell count (the Pearson correlation coefficients were 0.404 and 0.656, and the P values were 0.0177 and 0.0001, respectively).

Adverse and Clinical Events

Safety was evaluated using standard ACTG criteria.14 During step 1, 19 subjects had grade 3/4 adverse events (AEs), with a significantly higher rate seen in Arm A (P = 0.017): 14 (10 grade 3, 4 grade 4) occurred in the IL-2 arm (7 subjects experienced grade 3 general body discomfort with rigors, chills, or fever), and 5 (4 grade 3, 1 grade 4) occurred in Arm B. The most frequent AEs were observed in the general body symptom category. There were no unexpected toxicities. During step 2 (TI), 7 subjects had grade 3 or higher AEs in each of the 2 treatment groups. During 46 TIs (step 2 or 5) there were 2 cases of possible acute retroviral syndrome-like events. There were no AIDS-defining events or deaths. No study subject refused IL-2 treatment or dropped out of the study due to IL-2-related adverse effects.

The study was closed on November 22, 2004, after the ACTG A5102 Safety Monitoring Committee determined that a preestablished stopping rule threshold (>50% of study subjects in an arm were observed to have a single HIV-1 RNA level ≥100,000 copies/mL or a decline in CD4 T-cell count <350 cells/mm3 within 6 months of entering step 2) for a study arm had been reached for Arm B (the control arm) and recommended the closure of that arm. Twelve of 23 subjects in Arm B reached that threshold, 7 of 12 solely based on the HIV-1 RNA level threshold.


Intermittent therapy has been proposed as an alternative treatment strategy for management of chronic HIV infection. Pulsed ART based on preset periods or CD4 T-cell count-based strategies have been evaluated. Short-cycle (1 week on, 1 week off)19,20 or intermediate cycle (2 months on, 1 month off)21 pilot trials have been conducted. Those studies have reported no benefit and/or unacceptable rates of virologic failure or drug resistance. Preliminary studies evaluating outcomes after stopping effective ART or using the CD4 T-cell count as the threshold to stop and restart therapy supported efforts to further study intermittent therapeutic strategies.11,22-27

The recent closure of the SMART trial due to the clinical inferiority of the CD4 count driven arm underscores the need for an intervention to improve the safety and utility of that therapeutic strategy.12,13 The intervention could be the use of immunomodulators, vaccines, or the selection of specific thresholds or populations (ie, higher CD4 T-cell threshold for restarting ART and/or restricting enrollment to patients with no history of AIDS and/or higher CD4 T-cell count nadirs). A more complete understanding of the repercussions of TI is important not only because patients will continue to interrupt therapy (due to toxicities, drug access, or adherence issues)28 but also because in vitro correlates of immune protection have not been identified so analytical TI will be needed to evaluate immunomodulatory interventions.29-31

The outcome of the SMART study needs to be viewed in the context of other TI-focused studies, like BASTA32 (ART restarted at a CD4 T-cell count <400 cells/mm3), ACTG 506833 (which focused on rebounding HIV RNA levels), and ACTG 517034 (stopped ART in patients with a nadir CD4 >350 cells/mm3. Those studies reported a reasonable safety profile for analytical TI, although there is concern about an increased risk for serious organ dysfunction possibly related to rebound of inflammatory markers.13,34

Our study is among the first to evaluate the role of an immunologic intervention intended to prolong the time off ART. IL-2 has been evaluated for its potential role in HIV pathogenesis and therapy since 1983.35-37 IL-2 at the doses we used has been shown to increase CD4 T-cell counts in HIV-1-infected patients with or without ART.38-42 Although a lower nadir CD4 T-cell count has been reported to diminish response to IL-2,43 patients with CD4 T-cell count nadirs less than 200 cells/mm3 often still respond.44,45 The short-term CD4 T-cell benefit of IL-2 administered pre-TI may be driven by the marked increase in the number of cells before TI (Fig. 1B, depicted by the blue line). Interruption of ART has been shown to blunt but not abrogate the CD4 T-cell response to IL-2 compared with continuation of ART.46 Use of alternative IL-2 dosing strategies (such as administration during the TI) with larger or different study populations (ie, among those with a higher nadir CD4 T-cell count and no prior suboptimal ART) still merit evaluation. Results of several large studies (ESPRIT=Evaluation of Subcutaneous Proleukin in a Randomized International Trial, SILCAAT=Subcutaneous Recombinant, Human Interleukin-2 in HIV-infected Patients with Low CD4+ Counts Under Active Antiretroviral Therapy, and the ANRS 118 study=ET de l'Agence Nationale de Recherche sur le SIDA 118-ILIADE) evaluating ART with or without IL-2, in continuous ART or intermittent strategies, will be crucial in determining the clinical safety and merit of IL-2-induced CD4 T-cell boosting strategies.47-49

Our prospective study confirmed previous observations that the major factor influencing the slope of the CD4 T-cell count after a TI was the nadir CD4 T-cell count.50-56 In our study, among the baseline variables analyzed, only a higher nadir CD4 T-cell count before the initiation of ART and higher naive CD4 T-cell count at entry (16 weeks before TI) predicted a longer time off therapy. Among variables we analyzed during the TI, a lower viral SP and the absence of resistance mutations at rebound were significantly associated with a longer time for CD4 T-cell count to remain above 350 cells/mm3.

In 4 cases we identified evidence of drug resistance in PBMC DNA to previously administered drugs (primarily AZT and 3TC mutations; Table 2). The 4 cases with consensus DNA sequencing indicating archived resistance were among 20 subjects with prolonged dual NRTI experience. The absence of mutations in HIV-1 DNA among the 16 other NRTI-experienced subjects does not exclude their presence at levels below the detection limits of consensus sequencing. However, only these 4 cases with PBMC DNA resistance among the 20 with prior drug experience developed drug resistance in emergent RNA. The other 6 cases with drug resistance mutations had no history of suboptimal therapy.

The identification of drug resistance mutations, more frequently among those with rapidly emergent virus and higher SPs, is consistent with a pharmacologic "tail" where subinhibitory concentrations, particularly of NNRTI drugs, may persist as virus replication takes off, selecting drug resistance. The protocol recommended stopping the NNRTI component of ART more than 48 hours before the NRTIs. In fact, of the 24 subjects on an NNRTI regimen, 6 stopped all drugs on the same day, 6 stopped the NNRTI 1 day before the NNRTIs, and 12 stopped the NNRTI 2 or more days before the NRTIs. Of 24 NNRTI recipients, 2 (8%) who developed new NNRTI mutations included 1 who stopped nevirapine and NRTIs on the same day (Y181C mutation) and 1 who stopped efavirenz 2 days before the NRTIs (K103N mutation). Among the 6 subjects with resistance mutations who restarted ART (step 3), all 6 achieved suppression on reinitiation of the same regimen.

One of the lessons from our study for future TI trials is that a safety stopping rule based on an HIV RNA threshold necessitating reinitiation of ART may not be necessary. Our observed time to viral SP (median, 16 weeks) is germane to the design of future TI studies evaluating the effect of an immune intervention. A relatively brief interruption of 4 to 6 months may suffice for the evaluation of the effect of an intervention on the timing or magnitude of the viral SP.

Increasingly effective and tolerable potent ART regimens decrease the incentive to use TI as an ART-sparing approach to chronic management.8,9 Our study failed to demonstrate a durable ART-sparing benefit of IL-2 (yet with increased toxicity) and resistance concerns for both arms of the study. The study enrolled a heterogeneous population of patients (with and without a history of prior suboptimal ART and/or nadir CD4 T-cell count <350 cells/mm3), so the results may differ in other patient populations (ie, patients with no history of suboptimal ART or CD4 T-cell count nadir >350 cells/mm3). Strategies aimed at avoiding drug resistance (such as avoiding use of NNRTI-based regimens) and interventions to decrease HIV-1 RNA rebound during the TI (such as therapeutic vaccination57-59 or efforts to decrease immune activation) are potential approaches for future efforts to more safely and effectively prolong time off ART.


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The following ACTG sites and investigators participated in this study: Debra DeMarco, BSN; Kim Gray, RN, BSN, MSN, APN, Ed D; and GeYoul Kim, RN, at Washington University, St Louis, MO (site 2101; grant AI25903); Ann Conrad, RN; Jane Baum, RN; and Michael Lederman, MD, at Case Western Reserve University, Cleveland, OH (site 2501; grant AI25879); Ray Nelson, RN; Rebecca Sanders, and Chris Fietzer, RN, at the University of Minnesota, Minneapolis, MN (site 1501; grant AI27661); Susan Swindells, MBBS; Frances Van Meter, APRN; and Sarah Starns, RN, at the University of Nebraska, Omaha, NB (site 1501); Debbie Slamowitz, RN, BSN, ACRN; Sylvania Stoudt, RN; and Jane Norris, PA-C, at Stanford University, Palo Alto, CA (site 0501; grants AI27666 and GCRC M01 RR000070); Donna Mildvan, MD, Ronald D Amico, DO, and Gwendolyn Costantini, FNP, at Beth Israel Medical Center, New York, NY (site 2851; grant AI46370); Joan Riddle, RN, and Kenneth Shipp, RPh, at Duke University, Durham, NC (site 1601; grant AI39156); and Laurie Frarey, NP, Christopher Pilcher, MD, and Cheryl Marcus, RN, at University of North Carolina, Chapel Hill, NC (site 3201; grant AI25868).


antiretroviral therapy; interleukin 2; ACTG A5102

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