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Epidemiology and Prevention

CD4 Cell Responses to Combination Antiretroviral Therapy in Patients Starting Therapy at High CD4 Cell Counts

Wright, Stephen T BMath, MAppStat*; Carr, Andrew MD; Woolley, Ian MBBS, FRACP, DTMH‡§; Giles, Michelle MBBS, FRACP, PhD‡§‖; Hoy, Jennifer MBBS, FRACP‡‖; Cooper, David A MD, DSc*; Law, Matthew G MA, MSc, PhD*  On Behalf of The Australian HIV Observational Database

Author Information
JAIDS Journal of Acquired Immune Deficiency Syndromes: September 1, 2011 - Volume 58 - Issue 1 - p 72-79
doi: 10.1097/QAI.0b013e318225ba62



A number of observational and randomized clinical trials in resource replete and resource-poor settings have shown the benefit of combination antiretroviral therapy (cART) to suppress viral replication in patients with low CD4 cell counts. This has led to a marked reduction in AIDS and death due to immune recovery in these HIV-infected individuals on treatment.1 However, few studies have been published comparing the CD4 cell count increases and disease progression in patients who commence treatment at differing high CD4 levels (ie, 351-500, 501-650, >650 cells/μL).

Several cohort study groups have shown the majority of patients who commence cART at CD4 counts as low as 350 cells per microliter achieve a CD4 count above 500 cells per microliter within 6-12 months.2-5 In one cohort, it was also observed that the majority of patients starting cART within a CD4 count range 350-500 cells per microliter could achieve CD4 cell restoration (during follow-up) to levels comparable with uninfected HIV patients (>800 cells/μL).2 The demonstrated rapid recovery of CD4 cell counts to levels well above 500 cells per microliter indicates that patients commencing cART with a CD4 count >350 cells per microliter are well protected against new AIDS illnesses and AIDS-related mortalities, especially relative to HIV-infected individuals with impaired immune function, ie, CD4 count <200 cells per microliter.6

There is good randomized clinical trial data, reinforced by large observational cohort data supporting initiation of cART at a CD4 count of 350 cells per microliter as opposed to delaying treatment until counts fall below 200 cells per microliter.7-12 However, for patients who initiate cART at CD4 cell counts above 500 cells per microliter, the benefits of starting cART earlier rather than deferring treatment are less clear. The published data on disease progression has found little evidence of large beneficial differences in event rates (AIDS/non-AIDS/death) in patients with a current CD4 count above 650 cells per microliter compared with patients with a CD4 count in the range 350-500 or 501-649 cells per microliter.13,14 Analyses specifically comparing early versus deferred cART from large cohort observational studies (North American AIDS Cohort Collaboration on Research and Design [NN-AACORD] and Antiretroviral Cohort Collaboration [ART-CC])15,16 have shown somewhat conflicting results.

The primary objective of this study was to evaluate CD4 cell count responses in patients from the Australian HIV Observation Database (AHOD) who initiated cART with no prior treatment experience and had a baseline CD4 count greater than 350 cells per microliter. A key secondary objective was to estimate the effects of any differences observed in CD4 cell count responses to cART on clinical outcomes (mortality, AIDS, or both).


Data Collection

Analyses were based on patients recruited to AHOD, for which a comprehensive description has been previously described elsewhere.17 Briefly, AHOD data are collected from 27 sites throughout Australia, including hospitals, sexual health clinics, and general medical practices, with a special interest in HIV. Prospective data collection commenced in 1999, and retrospective data is provided where available. Written informed consent is obtained at time of enrollment. Ethics approval for AHOD was granted to all participating sites by relevant Research and Ethics committees. Data for AHOD are collected every 6 months on a core set of demographic and clinical variables including sex, age, HIV exposure, hepatitis B virus surface antigen status, hepatitis C virus antibody status, CD4 and CD8 cell counts, HIV viral load, antiretroviral treatment history, AIDS illnesses, and date and cause of death. Data are transferred electronically to the Kirby Institute and are subjected to quality control and quality assurance procedures.17

Data for this analysis included retrospective and prospective data collected from participants in AHOD who commenced their first cART regiment after January 1, 1997 and when their pre-cART CD4 counts were greater than 350 cells per microliter. Patients with previous exposure to mono/dual antiretroviral therapy were excluded. We assume “intention-to-treat” principles, and no account was taken for any change or interruption to cART during the follow-up period. To minimize potential survivorship/follow-up bias, data were restricted to a maximum of 72 months of follow-up.

Statistical Analysis

The primary endpoint for this analysis was CD4 cell count measured post cART initiation every 6 months up to 72 months. If a patient had multiple CD4 cell measurements within a 3-month window either side of any time point, then the CD4 cell count closest to the time point was selected. A patient's baseline CD4 cell count was identified by selecting the corresponding record closest to initiation of cART date and within a window of 6 months prior and 1 month post initiation of cART. Patients were deemed lost to follow-up if they did not have a clinical visit recorded within 1 year since the last routine data transfer (March 2010).

CD4 cell responses were summarized by average CD4 cell count, proportion of patients with CD4 counts >500 cells per microliter, proportion with CD4 counts >650 cells per microliter and compared according to initial pre-cART CD4 cell count strata and time since commencing cART. Patients were categorized into different baseline CD4 count strata: 351-500, 501-650, and >650 cells per microliter. Data were modelled using maximum likelihood, random-intercept (unstructured correlation structure), repeated-measurement linear regression, and models were adjusted a priori for age, sex, baseline HIV viral load, evidence of seroconversion within 6 months before initiating cART, hepatitis C virus, initial cART regimen, and calendar year. Time since commencing cART was split into 3 periods 0-12, 18-30, 36-72 months to allow for varying slopes, and the knot points were chosen by inspection of the data. Interaction between baseline CD4 category and time since initiating cART were tested for significance. Covariate levels for the categorical adjustment variables are outlined in Table 1. We estimated HIV seroconversion date as the midpoint between a patient's last known negative HIV serology test result and the patient's first HIV-positive test.

Study Population Baseline Characteristics and Demographics, Stratified by Baseline CD4 Cell Count Strata

The mixed model regression framework is ideal for analyses with missing observations or incomplete follow-up, as patients who are lost to follow-up or have missing (assumed at random) observations at any time point can be included in the analysis. This approach minimizes lost to follow-up and missing data bias for sample-based model prediction summaries (ie, mean, median, percentiles). Sensitivity analyses were completed to examine the robustness of our results under various data assumptions, including, restricting data to 24 months of follow-up; expanding data to include all follow-up (greater than 72 months); prospective CD4 cell measurements only; baseline CD4 strata determined from the average of at least 2 pre-cART CD4 cell measurements, all within 1 year of initiating cART; and baseline CD4 cell count restricted to 6 months prior and 7 days post initiating cART.

To investigate what our observed differences in CD4 count responses might mean in terms of clinical outcomes, we selected from the literature the available data on AIDS, death, and AIDS/death incidence rates by CD4 count from 3 large different observational cohorts.13,14,18 External rates of incidence were preferred as we felt that our internal rates calculated from a small number of endpoints (AIDS defining illness = 15, mortality = 5) were too unreliable. Two of the studies13,18 presented adjusted relative risk ratios, which were extrapolated into approximate incidence rates by multiplying the appropriate base group incidence rate by the relative risk ratio (Table 2 footnote for the base group incidence rate assumed). Using the published disease progression incidence rates by CD4 cell count strata and multiplying these rates with summarized data across baseline CD4 count strata, time and predicted CD4 cell count, we calculated crude event rates and hazard ratio estimates according to the differing baseline CD4 counts.

Cumulative Proportion of Time (Patient-Years) Predicted for CD4 Strata Over Follow-Up (72 Months) and the Estimated ARR and the Expected RRR of AIDS and (or) Death Incidence

The amount of time a patient spent in each predicted CD4 cell count strata was calculated using linear interpolation over the time point ti to ti+6 (I = 0, …, 66). For example, if a patient had a baseline CD4 count of 400 cells per microliter and at the time point of 6 months had a count of 700 cells per microliter, then the patient had spent 2 months in the less 500 cells per microliter strata, 3 months in the 501-650 cells per microliter strata, and 1 month in the >650 cells per microliter strata. The total time in each predicted CD4 count strata was calculated by summation across the cohort over 72 months since cART initiation and aggregated into a table by baseline CD4 stratum and predicted CD4 count strata.

As sensitivity analyses, we further split the proportion of time a patient spent below 500 cells per microliter into the proportion of time spent <350 cells per microliter and 350-500 cells per microliter and applied appropriate event rates. Additionally, we repeated the above calculations using observed CD4 cell values, where missing CD4 cell values were replaced with previously last known CD4 cell count and patients that were lost to follow-up stopped contributing to the cumulative time totals at the point of dropout. All statistical calculations were performed with SAS/STAT software, Version 9.2 of the SAS system for Windows.


Of the 3173 AHOD patients recruited, 1555 patients recorded a pre-cART CD4 cell measurement and initiated cART with no prior antiretroviral therapy (ie, mono or dual therapy); of these patients, 432 patients were eligible for the analysis. A total of 4057 CD4 count measurements were recorded over a combined total of 1957 observed patient years. Of the 432 patients, 71 (16%) were lost to follow-up {estimated rate of 3.0 persons per 100 person-years [95% confidence interval (CI): 2.4 to 3.8]}. Table 1 outlines the baseline clinical characteristics and patient demographics by baseline CD4 strata. Covariates have similar proportions in each covariate level across the different baseline CD4 strata. A notable difference is the proportion of patients who are estimated to have evidence of recent seroconversion in the >650 group, which is higher compared with others. Also, the proportion of patients whose year of first cART is 2005 onwards is also higher in the 351-500 cells per microliter group.

Regardless of baseline CD4 count stratum, similar proportions of patients had CD4 counts greater than 500 cells per microliter after 60 months of cART (Fig. 1). The proportion of patients whose CD4 counts were above 500 cells per microliter at 12 months (by baseline CD4 strata) was 64%, 84%, 93% and by 72 months was 70%, 69%, 73%. The full time-series and proportion of patients with CD4 counts >650 cells per microliter over time can be found in the Supplemental Table (see Supplemental Digital Content 1,

The proportion of patients with CD4 count >500 cells per microliter over time since initiation of cART. The shaded regions represent a 95% binomial proportion confidence interval.

We found that CD4 cell counts were largely predicted by time since initiation of cART, baseline CD4 cell count and their interaction (Table 3). The interaction effect between baseline CD4 strata and time period 1 (0, 6, 12 months) was significant (P value <0.0001), indicating that the response to cART differs depending on baseline CD4 strata. Patients with a baseline CD4 cell count between 350 and 650 cells per microliter on average increased their CD4 cell count by approximately 50-100 cells per microliter per 6 months and patients in the baseline CD4 count >650 cells per microliter group had a mixed response (−23 to 30 cells/μL per 6 months). Globally, no difference between baseline CD4 strata and time period 2 (18, 24, 30 months) was found (P value = 0.12), however, marginally, there seems to be some evidence that CD4 cell counts still increased slightly over the period within the baseline CD4 group 351-500 cells per microliter (1-15 cells/μL per 6 months). The interaction between baseline CD4 strata and time period 3 (36-72 months) was significant (P value = <0.0001). The interaction showed very little changes in CD4 cells counts over this period for baseline CD4 groups 351-500 and 501-650 cells per microliter (Table 3), however, a slight decrease in CD4 cell counts (−16 to −8 cells/μL per 6 months) for the baseline CD4 group >650 cells per microliter.

Selected Multivariate Model Parameter Estimates and 95% CIs

Observed and modelled mean CD4 cell counts by baseline CD4 strata and time since commencing cART are summarized in Figure 2 and the Supplemental Table (see Supplemental Digital Content 1, Absolute mean CD4 counts were above 500 cells per microliter in all baseline CD4 strata by 12 months (means of 596, 717, and 881 cells per microliter in baseline CD4 strata 351-500, 501-650, and >650 cells/μL, respectively). Plotting the mean and interquartile range for CD4 counts by each baseline CD4 strata (Fig. 2) indicates a slight downward trend for the baseline CD4 cell count group >650 cells per microliter. Caution is recommended when interpreting the trend for the highest baseline CD4 group. It is probable that this trend is due to a combination of patients with very high counts of CD4 cells dropping out of follow-up and differing proportions of detectable viral load over time. Modelled mean CD4 cell count responses were broadly similar across a variety of model specifications and sensitivity analyses (not shown); model summary results include predictions made for lost to follow-up and missing data.

Mean CD4 cell count (modeled—dots) over time since initiating cART, stratified by baseline CD4 count. Shaded bands are the interquartile range for the observed CD4 cell counts at a given time point.

The covariate adjusted risk ratios used for the expected reduction in mortality were originally published by Lodwick et al.18 These rates of mortality by CD4 cell count for ART-naive patient are presented relative to those with CD4 counts in the range 350-499 cells per microliter. The rates found were 0.77 (95% CI: 0.61 to 0.95) and 0.66 (95% CI: 0.52 to 0.85) for CD4 count strata 500-699 and ≥700+ cells per microliter, respectively. Expressing these relative risk ratios as approximate incidence rates, we took the reported population incidence rate (5.2 per 1000 person-years) and multiplied by the relative risk ratios. Table 2 shows the calculated proportion of follow-up time each individual would spend in each predicted CD4 count stratum, that is, 351-500, 501-650, and >650 cells per microliter. We found that the expected reduction in mortality for a patient starting cART with a CD4 count >650 cells per microliter compared with a count in the range 351-500 cells per microliter over a 72-month period was around 8%, an absolute reduction in risk of 0.33 per 1000 patient-years (Table 2). The expected reduction in risk for initiating cART at >650 cells per microliter relative to those with 501-650 cells per microliter was 4%, an absolute reduction in risk of 0.16 per 1000 patient-years (calculated from Table 2).

Two other studies13,14 found AIDS/death (composite endpoint) crude incidence rates of 13, 9, and 7, per 1000 patient-years for CD4 count groupings 350-499, 500-649, and >650 cells per microliter, respectively; and AIDS incidence risk ratios of 1.00, 0.86 (95% CI: 0.66 to 1.14), and 0.62 (95% CI: 0.44 to 0.87) for CD4 strata 350-499, 500-699, and ≥700 cells per microliter, respectively. Applying these event rates and risk ratios to the duration of follow-up in each predicted CD4 cell count strata, the equivalent relative risk reduction for >650 cells per microliter relative to 351-500 cells per microliter was 14% and 13%, respectively. The calculated absolute risk reductions were 1.25 and 1.03 per 1000 patient-years (Table 2).

Qualitatively, very similar results were found across the scenarios when repeating the calculations using observed CD4 cell counts and also when splitting the proportion of time predicted CD4 count <500 cells per microliter into <350 cells per microliter and 351-500 cell per microliter (data not shown).


We found that the observed and modelled CD4 cell response to cART for patients who commenced treatment at higher CD4 cell counts varied depending on the initial baseline CD4 level, the time since cART initiation, and their interaction. On average, patients who commenced treatment with a baseline CD4 count 351-500 cells per microliter, typically and rapidly (within 6-12 months of commencing cART) achieved and maintained a CD4 count greater than 500 cells per microliter. Additionally, the proportion of patients that maintained a CD4 cell count >500 cells per microliter after 72 months of follow-up remained above 65% across all baseline CD4 strata. Our data also showed that there is minimal absolute difference between the baseline CD4 strata for predicted mean CD4 cell counts at 72 months after initiating cART (676, 734, 763 cells per microliter for baseline CD4 counts 351-500, 501-650, and >650 cells/μL, respectively).

Using published data for AIDS incidence and mortality rates, we calculated approximate risk ratios for different CD4 cell count strata when initiating cART. We acknowledge that the event rates published by these large cohorts are based on patient populations that differ from AHOD, and typically these rates are not generalizable. However, to our knowledge, these are the only published data available that give clinical event rates/risk ratios in high CD4 count strata, and we felt that it was important to try to illustrate how the CD4 count differences we observed might translate into clinical outcomes.

Our results suggest that for patients enrolled in AHOD who commence treatment at higher CD4 cell count levels (501-650, >650 cells/μL), a small difference in all cause mortality would be expected as compared with patients who commenced treatment with lower CD4 counts (ie, 351-500 cells/μL). It is possible to attribute this minimal reduction in risk finding to the point that overall our patients who commenced cART with a CD4 cell count between 351-500 cells per microliter had a favorable immune reconstitution although receiving cART. The accumulated time spent in differing high CD4 cell count strata for the patients who initiated treatment with a CD4 count in the range 351-500 cells per microliter typically spend the majority of the time having a CD4 count greater than 500 cells per microliter and hence are deemed to have a smaller risk of mortality. Additionally, the reduction in risk of AIDS when comparing patients initiating cART at >650 cells per microliter with 351-500 cells per microliter is notable (13%, or approx 1 per 1000 patients-years), and our results show that potentially initiating cART at CD4 cell counts >650 cells per microliter could yield the prevention of hundreds or thousands AIDS events or illnesses.

There are some limitations to our analysis. First, AHOD is an observational cohort study of HIV-infected persons under routine clinical care from a nonrandom selection of sexual health clinics, hospitals, and general practitioners across Australia. Therefore, the subset of patients from the AHOD cohort used in the analysis is unlikely to be representative of all HIV-infected persons in Australia. Second, it is possible that several patients in our study population initiated cART early by their own admissions, potentially leading to an unmeasured and (or) unmeasurable confounder (ie, risk aversion, health obsessive, etc) that could influence our results. Third, due to the design of AHOD, no information is collected on date of seroconversion. We can estimate a seroconversion date by taking the midpoint between a patient's last known HIV-negative result and their first positive result. However, not all patients are screened regularly for HIV, and a large proportion of last known negative test result are unpopulated with values; as a consequence, we are unable to determine if we have correctly adjusted for the known influence of seroconversion on CD4 cell counts.19 Fourth, although we have used the appropriate statistical methods to account for lost to follow-up and missing data as best as possible, it is not possible to eliminate or quantify any remaining bias within our results. Naturally, this is not only applicable to our study, but it is an inherent trait of any study utilizing incomplete observational data. Finally, cART may also increase other illnesses (non-AIDS events) or increase toxicities. Because minimal data are available at high CD4 cell counts, we were unable to incorporate any associated risks of cART into our analysis.

There is currently no firm consensus on when to start cART in asymptomatic patients with CD4 cell counts >350 cells per microliter. Based on observational studies, there is a view that starting cART before any immunodeficiency has occurred is the most likely strategy to minimize morbidity or mortality.20 We believe our analysis is the first to compare exclusively CD4 count changes in patients who started cART at CD4 counts >350 cells per microliter. Our analysis suggests that patients who start cART at CD4 counts >650 cells per microliter have better preserved immune function, but only to a relatively modest degree. Furthermore, the extent to which this might be expected to result in better clinical outcomes we show is uncertain. The optimal time to commence antiretroviral treatment (early or deferred) for newly HIV-infected patients is being directly addressed by the Strategic Timing of AntiRetroviral Treatment (START) randomized clinical trial (NCT00867048). The trial compares eligible patients (HIV-infected, treatment-naive, CD4 count greater than 500 cells/μL) who are randomized to either a group that receives treatment early (immediately after randomization) or defers treatment until their CD4 count falls below 350 cells per microliter. The results are not expected to be reported before 2014 and should provide the most reliable evidence for the optimal time to initiate treatment in asymptomatic individuals.


We would like to acknowledge the 2 anonymous referees whose comments helped improve the article. Finally, we would like to acknowledge all of the contributors to the Australian HIV Observational Database study (Appendix 1) without whom, this work would not have been possible.


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APPENDIX I: The Australian HIV Observational Database

D. Ellis, General Medical Practice, Coffs Harbour, NSW; J. Chuah,* M. Ngieng, B. Dickson, Gold Coast Sexual Health Clinic, Miami, QLD; M. Bloch, T. Franic,* S. Agrawal, L. McCann, N. Cunningham Holdsworth House General Practice, Darlinghurst, NSW; R. Moore, S. Edwards, R. Liddle, P. Locke, Northside Clinic, North Fitzroy, VIC; D. Nolan, J. Skett, Department of Clinical Immunology, Royal Perth Hospital, Perth, WA; N. J. Roth,*† J. Nicolson,* Prahran Market Clinic, South Yarra, VIC; D. Allen, J. L. Little, Holden Street Clinic, Gosford, NSW; D. Smith, C. Gray, Lismore Sexual Health and AIDS Services, Lismore, NSW; D. Baker,* R. Vale, East Sydney Doctors, Darlinghurst, NSW; D. Russell, S. Downing, Cairns Sexual Health Service, Cairns, QLD; D. J. Templeton,* C. O'Connor, C. Dijanosic, Royal Prince Alfred Hospital Sexual Health, Camperdown, NSW; D. Sowden, J. Broom, C. Johnson K. McGill, Clinic 87, Sunshine Coast and Cooloola HIV Sexual Health Service, Nambour, QLD; D. Orth, D. Youds, Gladstone Road Medical Centre, Highgate Hill, QLD; E. Jackson, J. Shakeshaft, K. McCallum, Blue Mountains Sexual Health and HIV Clinic, Katoomba, NSW; T. Read, J. Silvers,* Melbourne Sexual Health Centre, Melbourne, VIC; A. Kulatunga, P. Knibbs, Communicable Disease Centre, Royal Darwin Hospital, Darwin, NT; J. Hoy,* K. Watson,* M. Bryant, S. Price, The Alfred Hospital, Melbourne, VIC; M. Grotowski, S. Taylor, Tamworth Sexual Health Service, Tamworth, NSW; D. Cooper, A. Carr, K. Sinn, K. Hesse, R. Norris St Vincent's Hospital, Darlinghurst, NSW; R. Finlayson, I. Prone, Taylor Square Private Clinic, Darlinghurst, NSW; E. Jackson, J. Shakeshaft, K. McCallum, Nepean Sexual Health and HIV Clinic, Penrith, NSW; M. Kelly, A. Gibson, H. Magon, AIDS Medical Unit, Brisbane, QLD; K. Brown, V. McGrath, Illawarra Sexual Health Clinic, Warrawong, NSW; L. Wray, P. Read, H. Lu, Sydney Sexual Health Centre, Sydney, NSW; W. Donohue, O'Brien Street Practice, Adelaide, SA; I. Woolley, M. Giles, M. Salehin, T. Korman, Monash Medical Centre, Clayton, VIC; Dubbo Sexual Health Centre, Dubbo, NSW; P. Canavan,* J. Watson,* National Association of People Living with HIV/AIDS; C. Lawrence,* National Aboriginal Community Controlled Health Organisation; B. Mulhall,* School of Public Health, University of Sydney, Sydney, NSW; M. Law*, K. Petoumenos,* H. McManus,* S. Wright*, C. Bendall,* M. Boyd,* National Centre in HIV Epidemiology and Clinical Research, University of NSW, Sydney; NSW.

*Steering Committee member 2010, †Current Steering Committee chair.


CD4 cell counts; disease progression; HIV; immune restoration; long-term cART

Supplemental Digital Content

© 2011 Lippincott Williams & Wilkins, Inc.