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Absolute Count and Percentage of CD4+ Lymphocytes Are Independent Predictors of Disease Progression in HIV-Infected Persons Initiating Highly Active Antiretroviral Therapy

  1. Todd Hulgan1,2,4,
  2. Bryan E. Shepherd3,
  3. Stephen P. Raffanti1,4,
  4. Jennifer S. Fusco5,
  5. Robin Beckerman6,
  6. Gema Barkanic1 and
  7. Timothy R. Sterling1,2
  1. 1Department of Medicine, Division of Infectious Diseases, Nashville, Tennessee
  2. 2Center for Health Services Research, Nashville, Tennessee
  3. 3Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee
  4. 4Comprehensive Care Center, Nashville, Tennessee
  5. 5EpiQuest Sciences, Inc., Durham, North Carolina
  6. 6Durham, North Carolina
  1. Reprints or correspondence: Dr. Todd Hulgan, Div. of Infectious Diseases, Vanderbilt University School of Medicine, 345 24th Ave. N, Ste. 105, Nashville, TN 37203 (todd.hulgan{at}vanderbilt.edu).
 
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Abstract

Background. Highly active antiretroviral therapy (HAART) is recommended when the absolute CD4+ T lymphocyte count is <200 cells/mm3, and it should be considered when that count is ≥200, although the optimal timing when it is ≥200 is unclear. Because preliminary data had suggested that a low CD4+ T lymphocyte percentage (%CD4) is associated with disease progression in persons initiating HAART who have a higher absolute CD4, we sought to further characterize the predictive utility of %CD4.

Methods. We conducted an observational study of persons in Collaborations in HIV Outcomes Research/US cohort who initiated their first HAART regimen between 1997 and 2004, received ≥30 days of therapy, and had baseline values of absolute CD4, %CD4, and HIV-1 RNA. Cox proportional-hazards models determined associations between %CD4 and disease progression (to either a new AIDS-defining event [ADE] or death).

Results. Of 1891 persons, 11% were female and 18% were African American; the median age was 38 years. Median follow-up was 55 months (interquartile range, 23–83 months), and 468 (25%) had disease progression. Multivariable analysis including age, race, sex, HIV-1 RNA, prior antiretroviral therapy, probable route of infection, prior ADE, absolute CD4, and %CD4 was performed; prior ART (P<.0001), injection-drug use (P=.04), lower absolute CD4 (P=.002), and lower %CD4 (P=.002) predicted disease progression.

Conclusions. %CD4 at initiation of the first HAART regimen predicted disease progression independent of absolute CD4; %CD4 may be used to determine the timing of HAART.

Highly active antiretroviral therapy (HAART) has substantially decreased morbidity and mortality in HIV-infected persons [1, 2]. The optimal timing of initiation of HAART is difficult to determine because clinicians and patients must weigh the benefits and risks of therapy; benefits include suppressing viral replication, preserving immune function, prolonging disease-free survival, and decreasing viral transmission. Even with complete virologic suppression, however, eradication of HIV-1 infection cannot be achieved with current therapy [3, 4]. The risks of therapy include development of drug resistance (which limits future treatment options) and drug toxicity, including lipodystrophy, peripheral neuropathy, dyslipidemia, and premature cardiovascular disease [5–7].

In persons with HIV-1 infection, absolute CD4+ T lymphocyte count (absolute CD4) concentration in peripheral blood predicts development of opportunistic illnesses and death due to AIDS [8–11]. Treatment guidelines regarding initiation of HAART have generally relied on absolute CD4. The current treatment guidelines from the US Department of Health and Human Services [11] and the International AIDS Society-USA [12] base their recommendations on a combination of absolute CD4 and HIV-1 RNA level in plasma. HAART is recommended for all symptomatic persons and, regardless of symptoms, for those with absolute CD4 <200 cells/mm3. When absolute CD4 is the basis for decision, treatment should be considered when absolute CD4 is 200–350 cells/mm3, although there are insufficient data to support more specific recommendations for asymptomatic persons with an absolute CD4 >200 cells/mm3 [11, 12].

CD4+ T lymphocyte percentage (%CD4) could potentially add prognostic information and assist in the decision about when to start antiretroviral therapy. Studies from the pre-HAART era found that %CD4 was more stable [13] and had greater prognostic significance than did absolute CD4 [13, 14]. More recently, Gebo et al. found that, in predicting short-term (median, 90 days) development of new opportunistic infections in persons with an absolute CD4 <350 cells/mm3, %CD4 did not provide more prognostic information than did absolute CD4, regardless of receipt of HAART [15]. Moore et al. reported that %CD4 <15 was associated with an increased risk of death in persons without AIDS who were starting HAART and whose absolute CD4 was 200–350 lymphocytes/mm3 [16]. In a single-center observational cohort study, however, we found that, in persons with an absolute CD4 ≥350 cells/mm3 at the time of initiation of HAART, a %CD4 <17 was associated with an increased risk of progression to a new AIDS-defining event (ADE) or death [17]. The relatively small sample size in that study precluded controlling for other factors via multivariable analysis. Thus, we sought to examine, in HIV-infected persons initiating HAART in a large, multicenter cohort, %CD4's independent predictive utility with respect to progression to either a new ADE or death.

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Subjects and Methods

Study cohort. The study population was the Collaborations in HIV Outcomes Research/US (CHORUS) cohort. Details regarding CHORUS have been published elsewhere [18–20]. The present study included persons from CHORUS who were ≥18 years of age and who had initiated HAART between 1 August 1997 and 1 January 2005. HAART was defined as (1) at least 2 nucleoside reverse-transcriptase inhibitors (NRTIs) in combination with at least 1 protease inhibitor (PI) and/or non-NRTI (NNRTI) or (2) at least 3 NRTIs. For inclusion within the study, persons had to have remained on their first HAART regimen for ≥30 days; have baseline demographic data; absolute CD4, %CD4, and HIV-1 RNA values available either ≤180 days before or 7 days after the date of initiation of the first HAART regimen; and have a baseline HIV-1 RNA ≥5000 copies/mL. Data on prior mono- and/or dual-NRTI antiretroviral therapy (ART) and on prior ADEs were collected. The CHORUS cohort study was approved by local Institutional Review Boards, and all patients gave written informed consent for use of clinical data. To maintain patient confidentiality, only aggregated nonidentifiable patient data were used in the analysis.

Definition of study events. A study event was defined as either the first new ADE or death after initiation of the first HAART regimen. ADEs were identified on the basis of 1993 Centers for Disease Control and Prevention (CDC) classification criteria [21], excluding diagnoses based on an absolute CD4 <200 cells/mm3. Persons who did not experience a study event were followed in the cohort either until the end of the study period (1 August 2005) or until they were lost to follow-up (defined as the date of either withdrawal from CHORUS or, for persons who did not visit the clinic for >9 months), the last clinic visit.

Statistical analysis. To evaluate the association between %CD4 before initiation of HAART and either ADE or death, we fitted a Cox proportional-hazards model adjusting for absolute CD4, baseline log10-transformed HIV-1 RNA, sex, race (white or nonwhite), baseline age, prior ART, prior ADE, and probable route of infection (injection-drug use [IDU] or non-IDU). These variables were chosen for inclusion in the model a priori. %CD4 and absolute CD4 were square-root trans-formed to normalize the data; hazard ratios (HRs) were based on reverse-transformed values. To avoid assuming linearity in the hazard, continuous predictors (%CD4, absolute CD4, viral load, and age) were expanded by fitting restricted cubic splines. For candidate models, each continuous predictor was assigned the same number of knots (0, 3, 4, or 5). The final model used 3 knots (the optimal number of knots, on the basis of the Akaike information criteria; knots were located at the 10th, 50th, and 90th percentiles). An interaction term (including linear and nonlinear terms) between %CD4 and absolute CD4 was also included in the model. On the basis of evidence of a possible violation to the proportional-hazards assumption, the models were stratified on age quintiles and included age as a continuous predictor to account for possible residual confounding. Colinearity between %CD4 and absolute CD4 was examined by computing the variance inflation factor. Unless otherwise stated, the P values (all 2-sided) and confidence intervals (CIs) did not adjust for the model-fitting uncertainty; the HR CI adjusted for model uncertainty was obtained by bootstrapping the entire model-fitting procedure (1000 bootstrap replications) [22]. All analyses were performed by R statistical software (version 2.3.1; available at: http://www.r-project.org) [23].

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Results

Of a total of 1891 persons who met the inclusion criteria (table 1), 11% were female, and 18% were African American; the median age at initiation of HAART was 38 years (range, 18–82 years). The probable route of HIV infection reported most frequently, by 1369 persons (72%), was men having sex with men, whereas 108 (6%) reported IDU; for 115 (6%), the probable route of HIV infection was unknown, and these persons were more likely to be white (P=.04). At baseline, median (interquartile range [IQR]) absolute CD4 and %CD4 were 240 cells/mm3 (107–384 cells/mm3) and 16% (9%–24%), respectively. The first HAART regimen contained a PI in 1261 persons (67%), an NNRTI in 400 (21%), both in 30 (2%), and only ≥3 NRTIs in 200 (11%). Before initiation of HAART, 541 persons (29%) had a history of an ADE, and 1001 (53%) had received ART: mono-NRTI therapy in 122 (6%), dual NRTIs in 289 (15%), and both in 590 (31%). Over a median follow-up period of 55 months (IQR, 23–83 months), 468 persons (25%) either had a new ADE (281 [15%]) or died (187 [10%]).

Figure 1 shows the relationship between baseline absolute CD4 and %CD4 in the cohort, as a scatter plot and stratified according to traditional CDC cutoffs. The overall correlation was 0.84 (P<.0001; Spearman's ρ), but there was evidence of discordance: of persons in the 2 highest absolute-CD4 strata (≥350 cells/mm3), 25% had a %CD4 that was less than expected (<21%); of persons in the 2 lowest absolute-CD4 strata (<350 cells/mm3), 17% had a %CD4 ≥21% (figure 1, inset table).

Figure 1
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Figure 1

Relationship between percentage of CD4+ T lymphocytes (%CD4) and absolute no. of CD4+ T lymphocytes (absolute CD4). The table in the upper-right corner of the plot indicates the no. of persons falling into each of several categories defined by absolute CD4 and %CD4. ADE, AIDS-defining event.

In the Cox proportional-hazards model adjusted for baseline absolute CD4, viral load, age, sex, race, prior ADE, prior ART, and route of infection, %CD4 was a significant predictor of ADEs and death (P=.002) (table 2). At a given absolute CD4, persons with a lower %CD4 were more likely to either have an ADE or die; for example, for persons with an absolute CD4 of 240 cells/mm3 (cohort baseline median), the HR for ADE or death for a %CD4 of 9 versus 24 (25th vs. 75th percentiles) was 1.65 (95% CI, 1.17–2.34; 95% bootstrap CI adjusting for model-fitting uncertainty, 1.17–3.13). The interaction between %CD4 and absolute CD4 was also significant (P=.003), in-dicating that the relationship between %CD4 and ADEs or death varied with different levels of absolute CD4. This sig-nificant interaction, along with the nonlinear components of the model (P=.02), precludes describing the relationship between %CD4 and disease progression by use of a single HR. Tables 3 and 4 contain HRs for several other %CD4 and absolute CD4 levels that are of interest. In addition to %CD4, absolute CD4 (P=.002), receipt of ART before initiation of the first HAART regimen (HR, 1.82 [95% CI, 1.47–2.27]; P<.001), and IDU as a route of infection (HR, 1.46 [95% CI, 1.02–2.08]; P=.04) were independent predictors of disease progression and death. Higher baseline HIV RNA also tended to predict disease progression and death (HR, 1.18 [95% CI, 1.00–1.39] for quartile 3 [5.2 log10 copies/mL] vs. quartile 1 [4.3 log10 copies/mL]; P=.09).

Figure 2
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Figure 2

Adjusted 6-year AIDS-defining event (ADE)-free survival rate as a function of percentage of CD4+ T lymphocytes (%CD4) at different levels of absolute no. of CD4+ T lymphocytes (aCD4). Each curve represents the predicted survival rate for a person with the given absolute CD4, as a function of %CD4 and adjusted for all other variables at their median values (age, 38 years; sex, male; race, white; log-viral load, 4.7; prior antiretroviral therapy, yes; injection-drug use, no). Curves extend only over the range for which there are data; that is, all persons in the present study who had an aCD4 near 100 cells/mm3 (±25 cells/mm3) had %CD4 values between 3.8% and 25%. For reference, the predicted ADE-free survival rate for a person with an absolute CD4 of 200 cells/mm3, after adjustment for all other variables at their median values (including a %CD4 of 16%), is included as a horizontal line.

Table 1
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Table 1

Clinical and demographic characteristics of the study cohort (n=1891).

Table 2
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Table 2

Multivariable Cox regression model examining predictors of time to either an AIDS-defining event (ADE) or death.

Table 3
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Table 3

Multivariable Cox regression model examining baseline percentage of CD4+ T lymphocytes (%CD4) as a predictor of either an AIDS-defining event (ADE) or death, based on various fixed absolute numbers of CD4+ T lymphocytes (absolute CD4).

Table 4
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Table 4

Multivariable Cox regression model examining baseline absolute number of CD4+ T lymphocytes (absolute CD4) as a predictor of either an AIDS-defining event (ADE) or death, based on various fixed percentages of CD4+ T lymphocytes (%CD4).

Predicted 6-year ADE-free survival curves for a person with a given absolute CD4 were generated as a function of %CD4 (figure 2). These curves demonstrate that, for a given absolute CD4, the adjusted 6-year ADE-free survival rate decreased with lower %CD4 values; for example, after adjustment for all other variables at their median values, the predicted 6-year ADE-free survival rate for a person starting HAART with an absolute CD4 of 250 cells/mm3 and a %CD4 of 13 is approximately equal to that for a person with an absolute CD4 of 200 cells/mm3 and a %CD4 of 16 (the cohort median). Persons with an absolute CD4 of 250 cells/mm3 and a %CD4 below or above 13 are estimated to have, respectively, lower or higher 6-year ADE-free survival rates. Despite the high correlation between %CD4 and absolute CD4, there was no evidence of colinearity (variance inflation factor, 4.5 and 4.6 for %CD4 and absolute CD4, respectively [HR estimates and 95% CI widths were similar in univariate and multivariable models]).

In secondary analyses, %CD4 remained a significant predictor of disease progression (P<.05; data not shown). These analyses consisted of multivariable models that adjusted for CHORUS site location; accounted for the missing route of infection variable by using multiple imputation techniques, including a third category in the model that defined route of infection as unknown, or removing the variable from the model; included type of initial HAART; and assumed a linear relationship between continuous predictors and the hazard. In multivariable models that included CD8+ T lymphocyte number, CD8+ T lymphocytes were not associated with HIV disease progression (HR, 1.02; P=.41), nor did their inclusion in the model affect the independent effect that %CD4 had on disease progression (HR, 1.57; P=.005). %CD4 remained a significant predictor of ADE and death when only those persons having an absolute CD4 >200 cells/mm3 were included in the analysis (P=.03), and it tended to predict either progression to ADE or death when only those persons without a prior ADE were included in the multivariable model (HR, 1.72; P=.06).

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Discussion

In this cohort, we observed that %CD4 at the time of initiation of HAART was associated with risk of HIV disease progression independent of other clinical factors, including absolute CD4. Our model predicted that, for a person with an absolute CD4 of 240 cells/mm3 and a %CD4 of 9, the hazard of ADE or death is 65% (95% CI, 17%–134%) greater than that for a person with the same absolute CD4 and a %CD4 of 24, independent of prior ART exposure, prior ADE, HIV RNA level, and other factors shown in table 2. Similarly, a person starting HAART who has an absolute CD4 of 350 cells/mm3 and a %CD4 of 14 has a slightly lower predicted 6-year ADE-free survival than does a similar person with an absolute CD4 of 200 cells/mm3 and a %CD4 of 28 (figure 2). Thus, there may be persons with absolute CD4 levels >200 cells/mm3 who could derive greater benefit from earlier initiation of HAART than do others; %CD4 may identify such persons and clarify the optimal timing of initiation of HAART.

Our results are important because we quantified the relative contribution of %CD4 to risk of disease progression in persons in this cohort who received HAART. Predictors of HIV disease progression in adults after initiation of HAART have been assessed in several cohorts [24–27]. To our knowledge, only 2 studies in addition to our previous report [17] have included %CD4 as a covariate. Gebo et al. found that %CD4 did not predict development of an opportunistic infection in persons with an absolute CD4 <350 cells/mm3, but their follow-up period was for only 90 days, and persons who did not receive HAART were included [15]. Moore et al. reported that %CD4 was no better than absolute CD4 in predicting mortality in all HIV-infected persons who started HAART but that %CD4 <15 was predictive of death in the subset of patients who were AIDS free and initiated HAART at an absolute CD4 of 200–350 cells/mm3 [16]. We studied a large population, including substantial numbers of persons with discordant absolute CD4 and %CD4 values. This approach allowed us to adjust for other important variables that influence HIV disease progression and to determine that %CD4 predicts disease progression independent of absolute CD4. We modeled %CD4, absolute CD4, and HIV RNA as continuous variables, rather than grouping them into 2 or more categories, as we and others had done previously. Maintaining such predictors as continuous variables typically improves power, decreases residual confounding, and mini-mizes bias [28].

That %CD4 and absolute CD4 were independent predictors of ADE or death could imply that the 2 together provide a more precise measurement of actual CD4+ T cell immunity. In the United States, the current standard for measuring absolute CD4 is to use a flow cytometry-based methodology in which total absolute lymphocyte number and %CD4 are calculated and the 2 are multiplied to obtain absolute CD4. The total-lymphocyte count includes many important subpopulations, including CD4+ lymphocytes, CD8+ lymphocytes, and NK cells. To assess whether differences in %CD4 and the corresponding differences in HIV disease progression might be due to CD8+ lymphocytes, we included the latter in secondary multivariable models. CD8+ lymphocyte number was not associated with HIV disease progression, nor did its inclusion in the model negate the independent effect of %CD4 on disease progression. Additionally, in a multivariable model that included the CD8 number but not %CD4, the CD8 number was not associated with disease progression (HR, 0.94; P=.76). We elected not to include CD8+ lymphocytes in the primary multivariable model because these data were missing for 545 (29%) of the persons in this cohort. NK cells comprise a very small proportion of total lymphocytes, are not routinely quantified in clinical practice, and were not measured in the present study, and therefore they could not be included in these analyses.

A limitation of cohort studies in which persons are followed from the time of initiation of HAART is that they do not provide direct information about the optimal time to initiate therapy. The ideal study would be a controlled trial in which persons are randomized to initiate HAART at different levels of either absolute CD4 or %CD4. This is neither ethical nor feasible. Another potential study design might include persons in a cohort at the same absolute CD4 or %CD4 who did not initiate HAART. This is challenging, however, because there are many potential time-dependent confounders associated with the decision to initiate treatment and with disease progression.

Our study has other limitations. The generalizability of our results is uncertain. Our population consisted primarily of white men in North America who acquired HIV through sex with men. It is unclear whether the model derived from this population will apply to populations with different demographic and clinical characteristics. Furthermore, we included in our analysis persons who developed an ADE prior to initiation of HAART. These persons, who comprised more than one quarter of our study cohort, would be considered “symptomatic” and, according to treatment guidelines, should receive HAART. When they were removed from the analysis, %CD4 remained associated with disease progression, although with borderline statistical significance (P=.06), which was likely due to the decreased sample size. We included these persons in the primary analysis because our main study objective was to assess disease progression after initiation of HAART, regardless of prior ADE. Other study limitations included our inability to control for adherence to HAART beyond 30 days or other clinical factors that have previously been reported to be associated with either immune recovery or disease progression, such as absolute-CD4 nadir [29], hemoglobin [30, 31], or hepatic enzymes [32]. Not all persons included in the present study were antiretroviral-naive at the time of initiation of HAART, although in the multivariable analyses we did control for prior ART exposure. During much of the study period, combination ART that included 3 NRTIs was considered to be HAART. 3-NRTI regimens are now considered to be less effective than efavirenz-based HAART [33]. However, the type of initial HAART was not associated with an increased risk of disease progression in a secondary analysis.

Our observation that %CD4 is a predictor of ADE-free survival, even after adjustment for absolute CD4, may be particularly relevant in light of recent studies suggesting that there are benefits to early initiation of HAART [34–37]. Absolute CD4 and %CD4 should be considered when decisions are made regarding which patients may benefit most from earlier initiation of therapy.

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Acknowledgments

We gratefully acknowledge all patients and providers who have taken part in CHORUS. We also acknowledge the members of the Epidemiology and Outcomes Unit of the Vanderbilt-Meharry Center for AIDS Research, for helpful discussions.

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Footnotes

  • Potential conflicts of interest: S.P.R. serves on the CHORUS advisory board; J.S.F. and R.B. were employees of GlaxoSmithKline (GSK) at the time the study was performed. Neither the funding agencies nor GSK were involved in study design, data analysis or interpretation, or drafting of the manuscript.

  • Financial support: National Institutes of Health (NIH; grant K23 AT002508-01 to T.H. and grant K24 AI065298 to T.R.S.); Vanderbilt-Meharry Center for AIDS Research (NIH program P30 AI 54999).

  • Received June 2, 2006.
  • Accepted September 15, 2006.
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References

  1. ↵
    1. Mocroft A,
    2. Ledergerber B,
    3. Katlama C,
    4. et al
    . Decline in the AIDS and death rates in the EuroSIDA study: an observational study. Lancet 2003;362:22-9.
    CrossRefMedlineWeb of Science
  2. ↵
    1. Palella FJ Jr.,
    2. Delaney KM,
    3. Moorman AC,
    4. et al
    . Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N Engl J Med 1998;338:853-60.
    CrossRefMedlineWeb of Science
  3. ↵
    1. Finzi D,
    2. Hermankova M,
    3. Pierson T,
    4. et al
    . Identification of a reservoir for HIV-1 in patients on highly active antiretroviral therapy. Science 1997;278:1295-300.
    Abstract/FREE Full Text
  4. ↵
    1. Siliciano JD,
    2. Siliciano RF
    . Latency and viral persistence in HIV-1 infection. J Clin Invest 2000;106:823-5.
    MedlineWeb of Science
  5. ↵
    1. Barbaro G,
    2. Klatt EC
    . Highly active antiretroviral therapy and cardiovascular complications in HIV-infected patients. Curr Pharm Des 2003;9:1475-81.
    CrossRefMedlineWeb of Science
    1. Carr A
    . HIV lipodystrophy: risk factors, pathogenesis, diagnosis and management. AIDS 2003;17:S141-8.
    CrossRefMedlineWeb of Science
  7. ↵
    1. Montessori V,
    2. Harris M,
    3. Montaner JS
    . Hepatotoxicity of nucleoside reverse transcriptase inhibitors. Semin Liver Dis 2003;23:167-72.
    CrossRefMedlineWeb of Science
  8. ↵
    1. Cameron DW,
    2. Heath-Chiozzi M,
    3. Danner S,
    4. et al
    . Randomised placebo-controlled trial of ritonavir in advanced HIV-1 disease. The Advanced HIV Disease Ritonavir Study Group. Lancet 1998;351:543-9.
    CrossRefMedlineWeb of Science
    1. Gulick RM,
    2. Mellors JW,
    3. Havlir D,
    4. 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.
    CrossRefMedlineWeb of Science
    1. Hammer SM,
    2. Squires KE,
    3. Hughes MD,
    4. 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 millimeter or less. AIDS Clinical Trials Group 320 Study Team. N Engl J Med 1997;337:725-33.
    CrossRefMedlineWeb of Science
  11. ↵
    Department of Health and Human Services. Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents, October 10, 2006. [18 December 2006]. Available at: http://aidsinfo.nih.gov/ContentFiles/AdultandAdolescentGL.pdf.
  12. ↵
    1. Hammer SM,
    2. Saag MS,
    3. Schechter M,
    4. et al
    . Treatment for adult HIV infection: 2006 recommendations of the International AIDS Society-USA panel. JAMA 2006;296:827-43.
    Abstract/FREE Full Text
  13. ↵
    1. Taylor JM,
    2. Fahey JL,
    3. Detels R,
    4. Giorgi JV
    . CD4 percentage, CD4 number, and CD4:CD8 ratio in HIV infection: which to choose and how to use. J Acquir Immune Defic Syndr 1989;2:114-24.
    MedlineWeb of Science
  14. ↵
    1. Burcham J,
    2. Marmor M,
    3. Dubin N,
    4. et al
    . CD4% is the best predictor of development of AIDS in a cohort of HIV-infected homosexual men. AIDS 1991;5:365-72.
    MedlineWeb of Science
  15. ↵
    1. Gebo KA,
    2. Gallant JE,
    3. Keruly JC,
    4. Moore RD
    . Absolute CD4 vs. CD4 percentage for predicting the risk of opportunistic illness in HIV infection. J Acquir Immune Defic Syndr 2004;36:1028-33.
    CrossRefMedlineWeb of Science
  16. ↵
    1. Moore D,
    2. Hogg R,
    3. Yip B,
    4. Craib K,
    5. Wood E,
    6. Montaner J
    . CD4 percentage is an independent predictor of survival in patients starting antiretroviral therapy with absolute CD4 cell counts between 200 and 350 cells/muL. HIV Med 2006;7:383-8.
    CrossRefMedlineWeb of Science
  17. ↵
    1. Hulgan T,
    2. Raffanti S,
    3. Kheshti A,
    4. et al
    . CD4 lymphocyte percentage predicts disease progression in HIV-infected patients initiating highly active antiretroviral therapy with CD4 lymphocyte counts >350 lymphocytes/mm3. J Infect Dis 2005;192:950-7.
    Abstract/FREE Full Text
  18. ↵
    1. Raffanti SP,
    2. Fusco JS,
    3. Sherrill BH,
    4. et al
    . Effect of persistent moderate viremia on disease progression during HIV therapy. J Acquir Immune Defic Syndr 2004;37:1147-54.
    CrossRefMedlineWeb of Science
    1. Justice AC,
    2. Stein DS,
    3. Fusco GP,
    4. et al
    . Disease progression in HIV-infected patients treated with stavudine vs. zidovudine. J Clin Epidemiol 2004;57:89-97.
    CrossRefMedlineWeb of Science
  20. ↵
    1. Becker SL,
    2. Raffanti SR,
    3. Hansen NI,
    4. et al
    . Zidovudine and stavudine sequencing in HIV treatment planning: findings from the CHORUS HIV cohort. J Acquir Immune Defic Syndr 2001;26:72-81.
    MedlineWeb of Science
  21. ↵
    Centers for Disease Control. 1993 Revised classification system for HIV infection and expanded surveillance case definition for AIDS among adolescents and adults. MMWR Recomm Rep 1992;41(RR-17):1-19.
    Medline
  22. ↵
    1. Steyerberg EW,
    2. Harrell FE Jr.,
    3. Borsboom GJ,
    4. Eijkemans MJ,
    5. Vergouwe Y,
    6. Habbema JD
    . Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001;54:774-81.
    CrossRefMedlineWeb of Science
  23. ↵
    1. Team RDC
    . R: a language and environment for statistical computing. 2.3.1 ed. Vienna, Austria: R Foundation for Statistical Computing; 2006.
  24. ↵
    1. Egger M,
    2. May M,
    3. Chene G,
    4. et al
    . Prognosis of HIV-1-infected patients starting highly active antiretroviral therapy: a collaborative analysis of prospective studies. Lancet 2002;360:119-29.
    CrossRefMedlineWeb of Science
    1. van Sighem AI,
    2. van de Wiel MA,
    3. Ghani AC,
    4. et al
    . Mortality and progression to AIDS after starting highly active antiretroviral therapy. AIDS 2003;17:2227-36.
    CrossRefMedlineWeb of Science
    1. Sterne JA,
    2. Hernan MA,
    3. Ledergerber B,
    4. et al
    . Long-term effectiveness of potent antiretroviral therapy in preventing AIDS and death: a prospective cohort study. Lancet 2005;366:378-84.
    CrossRefMedlineWeb of Science
  27. ↵
    1. Paredes R,
    2. Mocroft A,
    3. Kirk O,
    4. et al
    . Predictors of virological success and ensuing failure in HIV-positive patients starting highly active antiretroviral therapy in Europe: results from the EuroSIDA study. Arch Intern Med 2000;160:1123-32.
    Abstract/FREE Full Text
  28. ↵
    1. Royston P,
    2. Altman DG,
    3. Sauerbrei W
    . Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 2006;25:127-41.
    CrossRefMedlineWeb of Science
  29. ↵
    1. Kaufmann GR,
    2. Bloch M,
    3. Finlayson R,
    4. Zaunders J,
    5. Smith D,
    6. Cooper DA
    . The extent of HIV-1-related immunodeficiency and age predict the long-term CD4 T lymphocyte response to potent antiretroviral therapy. AIDS 2002;16:359-67.
    CrossRefMedlineWeb of Science
  30. ↵
    1. Mocroft A,
    2. Kirk O,
    3. Barton SE,
    4. et al
    . Anaemia is an independent predictive marker for clinical prognosis in HIV-infected patients from across Europe. EuroSIDA study group. AIDS 1999;13:943-50.
    CrossRefMedlineWeb of Science
  31. ↵
    1. Spacek LA,
    2. Griswold M,
    3. Quinn TC,
    4. Moore RD
    . Total lymphocyte count and hemoglobin combined in an algorithm to initiate the use of highly active antiretroviral therapy in resource-limited settings. AIDS 2003;17:1311-7.
    CrossRefMedlineWeb of Science
  32. ↵
    1. Lewden C,
    2. Raffi F,
    3. Cuzin L,
    4. et al
    . Factors associated with mortality in human immunodeficiency virus type 1-infected adults initiating protease inhibitor-containing therapy: role of education level and of early transaminase level elevation (APROCO-ANRS EP11 study). The Antiproteases Cohorte Agence Nationale de Recherches sur le SIDA EP 11 study. J Infect Dis 2002;186:710-4.
    Abstract/FREE Full Text
  33. ↵
    1. Gulick RM,
    2. Ribaudo HJ,
    3. Shikuma CM,
    4. et al
    . Triple-nucleoside regimens versus efavirenz-containing regimens for the initial treatment of HIV-1 infection. N Engl J Med 2004;350:1850-61.
    CrossRefMedlineWeb of Science
  34. ↵
    1. Palella FJ Jr.,
    2. Deloria-Knoll M,
    3. Chmiel JS,
    4. et al
    . Survival benefit of initiating antiretroviral therapy in HIV-infected persons in different CD4+ cell strata. Ann Intern Med 2003;138:620-6.
    Abstract/FREE Full Text
    1. Sterling TR,
    2. Chaisson RE,
    3. Keruly J,
    4. Moore RD
    . Improved outcomes with earlier initiation of highly active antiretroviral therapy among human immunodeficiency virus-infected patients who achieve durable virologic suppression: longer follow-up of an observational cohort study. J Infect Dis 2003;188:1659-65.
    Abstract/FREE Full Text
    1. Opravil M,
    2. Ledergerber B,
    3. Furrer H,
    4. et al
    . Clinical efficacy of early initiation of HAART in patients with asymptomatic HIV infection and CD4 cell count >350 × 106/l. AIDS 2002;16:1371-81.
    CrossRefMedlineWeb of Science
  37. ↵
    1. Wang C,
    2. Vlahov D,
    3. Galai N,
    4. et al
    . Mortality in HIV-seropositive versus -seronegative persons in the era of highly active antiretroviral therapy: implications for when to initiate therapy. J Infect Dis 2004;190:1046-54.
    Abstract/FREE Full Text
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  1. J Infect Dis. (2007) 195 (3): 425-431. doi: 10.1086/510536
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