Skip Navigation LinksHome > January 2, 2004 - Volume 18 - Issue 1 > Short-term risk of AIDS according to current CD4 cell count...
AIDS:
Clinical Science

Short-term risk of AIDS according to current CD4 cell count and viral load in antiretroviral drug-naive individuals and those treated in the monotherapy era

CASCADE Collaboration

Free Access
Article Outline
Collapse Box

Author Information

From the Cascade collaboration. *See the Appendix for members.

Requests for reprints: Dr K. Porter, MRC Clinical Trials Unit, 222 Euston Rd, London NW1 2DA, UK. Email: k.porter@ctu.mrc.ac.uk

Correspondence to: Dr A. Phillips, Royal Free Centre for HIV Medicine and Department of Primary Care & Population Sciences, Royal Free & University College Medical School, Royal Free Campus, Rowland Hill St, London NW3 2PF, UK. Email: a.phillips@pcps.ucl.ac.uk

Received: 4 November 2002; revised: 14 April 2003; accepted: 23 June 2003.

Collapse Box

Abstract

Background: One key piece of information required when deciding whether to initiate antiretroviral therapy is the risk of AIDS before the next clinic visit. Information on the short-term (6-month) risk of AIDS according to the current viral load and CD4 cell count in untreated individuals and those treated in the zidovudine monotherapy era (i.e., pre-September 1995), especially in those with CD4 cell count > 200 × 106 cells/l, is lacking.

Methods: Risk of AIDS was assessed in 3226 subjects with viral load and CD4 cell count known before initiation of antiretroviral therapy or during the zidovudine monotherapy era. These were from CASCADE Collaboration in which data from 20 cohorts of individuals with known dates of seroconversion to HIV, based in clinics in Europe and Australia, have been combined.

Results: During a total 5126.0 person-years of follow-up, 219 individuals developed AIDS. In those with current CD4 cell count < 200 × 106 cells/l, 6-month risks were 4.9, 12.7, 17.7 and 22.4% for viral load groups < 10 000, 10 000–29 999, 30 000– 99 999 and ≥ 100 000 copies/ml, respectively. For CD4 cell counts 200–349 × 106 cells/l risks were 0.5, 1.6, 3.2 and 4.7%, respectively, for the four viral load groups while the corresponding values for group with CD4 cell count ≥ 350 × 106 cells/l were 0.2%, 0.5%, 0.9% and 2.2%, respectively. Results were similar when analysis was restricted to those with no antiretroviral drug experience. Older people had a higher risk of AIDS for a given CD4 cell count and viral load than younger people.

Conclusion: Combined with consideration of other issues, these estimates should prove useful information when deciding whether to initiate antiretroviral therapy in HIV-infected individuals.

Back to Top | Article Outline

Introduction

When deciding on whether to initiate antiretroviral therapy in asymptomatic individuals, one key issue is the risk of development of AIDS if treatment is deferred. Of principal interest is the extent of AIDS risk over the period of time before the patient is next assessed, typically 3–6 months. If treatment is deferred, then the issue can be reassessed at the next patient visit – using newly available information on AIDS predictors – and the treatment decision revised if appropriate. While lower CD4 cell count and higher HIV viral load in plasma are well-established predictors of raised risk of AIDS [1–12], information on the absolute risk of AIDS over the 3–6-month time scale is surprisingly lacking and treatment guidelines have tended to rely on longer term estimates [13–15]. Such longer term estimates (e.g., over 3 years) were perhaps more relevant before the advent of triple combination therapies, when risk of AIDS could not be so rapidly reversed by initiation of therapy as is now the case.

In the present study, data from Cascade (Concerted Action on SeroConversion to AIDS and Death in Europe; a multicohort collaborative project) is used to produce estimates of the short-term risk of AIDS and death for deciding when/whether to initiate therapy.

Back to Top | Article Outline

Methods

Details of CASCADE are reported elsewhere [16]. In brief, CASCADE is a collaboration among the investigators of 20 cohorts in Europe and Australia that began in April 1997. All are cohorts of HIV-1-infected individuals for whom it was possible to estimate the time of HIV seroconversion, most commonly because there was a negative HIV antibody test result at most 3 years before the first positive test result. Data pooled from these cohorts in July 2001 included demographic and HIV-exposure information, date and type of initial AIDS diseases, antiretroviral treatments, CD4 lymphocyte counts and HIV viral load values.

Back to Top | Article Outline
Statistical methods

In brief, the approach was to categorize person-time in AIDS-free people according to the most recent CD4 cell count and viral load (which had to have been measured in the previous 6 or 12 months, respectively). By counting numbers of people developing AIDS during the person-time in each of these categories, the rate of AIDS for each CD4 cell count/viral load category could be calculated. Any person-time for which the viral load had not been measured in the previous year and the CD4 cell count in the previous 6 months was not included in the analysis, so if a person developed AIDS during such a time then this also was not included. This is because an analysis was wanted that related to situations where an up-to-date level of both markers was available. The details are as follows.

Individuals were eligible for inclusion if they were treatment naive or in the zidovudine monotherapy era (pre-September 1995, although it is recognized that some dual therapy regimens were used during this time period also), AIDS-free, and with at least 1 day of active follow-up with viral load and CD4 cell count both available (defined for viral load as measured within the past 12 months and for CD4 cell count as measured in the past 6 months). A longer period for the viral load to remain ‘current’ was chosen as this parameter tends to exhibit smaller changes over time [6,7,10]. For each eligible person, the first period of follow-up (accordingly allocated to a CD4 cell count/viral load category) was counted from the date (s)he first became eligible until the first occurrence of AIDS, death (therefore, follow-up was censored at death if this occurred before an AIDS diagnosis), date last known AIDS free, a new CD4 cell count or viral load measure, expiry of the viral load (12 months after it was measured) or CD4 cell count (6 months after it was measured) or start of antiretroviral therapy/interleukin 2/hydroxyurea (or last date that these were known not to have been started). This last censoring at start of antiretroviral therapy was only applied after September 1995 (i.e., after the end of the zidovudine monotherapy era). Consequently, a period of follow-up could not exceed 6 months (because the CD4 cell count to which it relates expired at this time). A person contributed further periods of follow-up (but not necessarily to the same CD4 cell count/viral load category) if (s)he was again eligible at any later point. This was the case for those whose first period of follow-up ended through measurement of a new viral load/CD4 cell count and for those whose first period ended because of expiry of a viral load or CD4 cell count, if a new measure was subsequently made.

The main analysis consisted simply of numbers of individuals with an AIDS event, person-years of follow-up and rate of AIDS (and cumulative risk of AIDS over 6 months, based on assumption of constant rate over this period) according to CD4 cell count and viral load category. Poisson regression [17] was used to explore the influence on CD4 cell count/viral load-specific AIDS rates of additional factors such as type of assay, gender, age, exposure group, ethnicity, calendar year (before/after January 1997; as a proxy for whether the assay was carried out in real-time or on a frozen sample). We also fitted Cox models with the date of seroconversion as time zero (allowing for late entry) and with CD4 cell count and viral load fitted as time dependent covariates (and remaining current for the same time periods) to check that results were similar.

A Poisson regression model was then fitted including only CD4 cell count, viral load and age fitted as continuous covariables, in order to generate predictions of the rate for any given specific value of these three variables. Log and square root transformations were considered for the CD4 count, as well as leaving it untransformed, because these have been previously found possibly to improve the model fit (e.g. 11). For viral load and age, a log transformation was also considered (i.e. for viral load a log log transformation).

Assays used for measuring viral load were classified broadly into Roche (Roche Molecular Systems, Branchburg, New Jersey, USA), NASBA (Organon Teknika, Durham, North Carolina, USA), Chiron (Emeryville, California, USA) and other.

Back to Top | Article Outline

Results

Of the total 9133 individuals included in the CASCADE data set, 3226 fulfilled the eligibility criteria for inclusion in this analysis, as set out above. Table 1 shows the characteristics of these patients: 77.4% were male and the median age was 32 years. HIV exposure groups were homosexual men 51.8%, injection drug use 14.3% and heterosexual sex 21.5%. Approximately half of the subjects had their first viral load measure before 1997. Since the test was only widely introduced around the end of 1996, in most cases these measurements would have been made on frozen samples. There were a median of eight viral load values available per person [interquartile range (IQR), 4–13] and median of 12 CD4 cell counts (IQR, 7–19). Ethnic origin was only available for 1911 subjects, of whom 92% were white. This breakdown of patients reflects the fact that these were individuals for whom a date of seroconversion was known.

Table 1
Table 1
Image Tools

Included in Table 2 are the numbers of person-years of experience among AIDS-free subjects who had not started antiretroviral therapy or were in the zidovudine monotherapy era (pre-September 1995), according to the current CD4 cell count (within last 6 months) and viral load (within past 12 months). In total, there were 5126.0 person-years of such experience in the 3226 individuals included. The median calendar date of the person years was March 1995 (IQR, July 1991 to February 1998). Overall, for 55.0% of the person-years, viral load values used were measured using the Roche method, 14.6% with NASBA, 7.8% with Chiron, 4.8% with another method and for 17.9% the method was unknown. During the 5126.0 person-years of follow-up, 219 individuals developed AIDS (43 Kaposi's sarcoma, 36 Pneumocystis carinii pneumonia, 36 oesophageal candidiasis, 14 toxoplasmosis, 11 non-Hodgkin's lymphoma, 10 cryptosporidiosis, 10 HIV encephalopathy, 59 others). The distribution of person-years in CD4 cell count/viral load categories reflects the fact that subjects were identified at the time of seroconversion in that most experience is in individuals with relatively high CD4 cell counts. The tendency for those with lower CD4 cell count to have a higher viral load can be seen from the fact that, for example, only 8% of the person-years with CD4 cell count ≥ 350 × 106 cells/l are with viral load ≥ 100 000 copies/ml, compared with 14% of those with CD4 cell count 200–349 × 106 cells/l and 26% of those with CD4 cell count < 200 × 106 cells/l.

Table 2
Table 2
Image Tools

Also shown in Table 2 are the numbers of individuals developing AIDS in each CD4 cell count/viral load category and the AIDS incidence rate. As expected, there is a trend for increasing AIDS rate with lower CD4 cell count and higher viral load. The trend for increasing rate with higher viral load seems roughly consistent in each CD4 cell count category and, similarly, the increasing rate with lower CD4 cell count is approximately consistent across viral load categories. Overall, the rate ranges from 0.004 per person year in individuals with CD4 cell count ≥ 350 × 106 cells/l and viral load < 10 000 copies/ml to 0.507 per person-year in individuals with CD4 cell count < 200 × 106 cells/l and viral load ≥ 100 000 copies/ml, a 127-fold increase. If it is assumed that these rates are constant over a 6-month period, then they can be translated into the percentage risk of developing AIDS in 6 months [1 − exp(−0.5Rate)] and these percentage risks are also shown. The risks over 3 months are approximately half of these values.

A Poisson regression model was fitted to assess the influence of other factors on the AIDS rate. First, a basic model was fitted containing CD4 cell count and viral load in the groups shown in Table 2. The rate ratios for those with current CD4 cell count < 200 × 106 cells/l and those with current CD4 cell count 200–349 × 106 cells/l, relative to those with CD4 cell count ≥ 350 × 106 cells/l were 18.7 and 3.0, respectively. Rate ratios for those with viral load ≥ 100 000 copies/ml, 30 000–99 999 copies/ml and 10 000–29 999 copies/ml, relative to those with < 10 000 copies/ml were 6.5, 2.4 and 1.4, respectively. There was no significant interaction between the effects of CD4 cell count and viral load on AIDS rate, even when fitted as continuous variables (P = 0.1; Wald test for interaction term).

Sex, age (current), HIV exposure group and calendar date (pre/post January 1997, after which most viral load assays would be measured on fresh samples) were also introduced into the model. Age was significantly associated with the AIDS rate, with older people experiencing a higher rate [rate ratio 1.23 per 10 years older; 95% confidence interval (CI), 1.07–1.42; P = 0.003, Wald test] and there was a tendency for lower rate of AIDS in more recent calendar times (rate ratio 0.64 for post- versus pre-January 1997; 95% CI, 0.45–0.91; P = 0.01, Wald test). Analyses considering only experience since January 1997 gave rates for < 10 000, 10 000–29 999, 30 000–99 999 and ≥ 100 000 copies/ml at CD4 cell count < 200 × 106 cells/l of 0.050, 0.125, 0.150 and 0.486, respectively; for CD4 200–349 × 106 cells/l of 0.018, 0.012, 0.065 and 0.058, respectively; and for CD4 cell count ≥ 350 × 106 cells/l of 0.004, 0.003, 0.011 and 0.032, respectively. There was no significant effect of sex or HIV exposure group.

Models were also fitted that allowed for the association between viral load and AIDS rate to vary according to assay (Roche, Chiron, NASBA, other/unknown) and by sex, but in neither case did these significantly improve the model fit [chi-square 16.2 on 12 degrees of freedom (df); P = 0.2 and chi-square 8.4 on 4 df; P = 0.08, respectively; likelihood ratio test]. AIDS rates were also calculated according to viral load groups separately for women (numbers were too small to calculate rates for each viral load/CD4 cell count combination). The rates were 0.009 (95% CI, 0.003–0.019), 0.048 (95% CI, 0.022–0.091), 0.048 (95% CI, 0.018–0.105) and 0.108 (95% CI, 0.040–0.236) for viral load groups < 10 000, 10 000–29 999, 30 000–99 999 and ≥ 100 000 copies/ml, respectively, compared with values of 0.010 (95% CI, 0.006–0.016), 0.031 (95% CI, 0.020–0.042), 0.082 (95% CI, 0.063–0.101) and 0.154 (95% CI, 0.119–0.189) for men.

In the main analysis, person-years in which therapy had been started were included if these occurred in the zidovudine monotherapy era (pre-September 1995). Analyses were also run on the subset of person-years (4427.6 years of the total 5126.0 person years) for which no antiretroviral therapy had been started. Rates were very similar to the main analysis, particularly in the higher two CD4 cell count categories. In a further sensitivity analysis, follow-up that occurred up to 30 days after the initiation of antiretroviral therapy was included. This was because of concern that occurrence of symptoms that subsequently lead to an AIDS diagnosis can trigger initiation of antiretroviral therapy before the date of a formal AIDS diagnosis. Rates were again very similar to the main analysis.

The risk of AIDS in 6 months given in Table 2 are to some extent limited by the fact that individuals with appreciably differing CD4 cell count and/or viral load values are categorized together, because each category has to be sufficiently large for a rate to be calculated with reasonable precision. Also, for the same reason, the effect of age is not incorporated. Therefore, a Poisson model was also fitted in which CD4 cell count, viral load and age were continuous variables; this allowed generation of a model that can be used to calculate a predicted 6-month risk from exact individual CD4 cell count and viral load values and age. In this model, CD4 cell count was fitted as square root, because this led to a model with lower likelihood. The results of the model are shown in Table 3. The unadjusted rate ratios for viral load and age were reduced markedly towards 1 after adjustment for the CD4 cell count (although still remained statistically significant), while the rate ratio for the CD4 cell count remained almost unchanged after adjustment for viral load and age. For a given CD4 cell count, viral load and age, the predicted AIDS rate can be calculated as

Table 3
Table 3
Image Tools

rate = exp{−3.55 + [−0.21√(CD4 cell count)] + 0.71 (log viral load) + 0.024(Age)}

From this rate, the 6-month percentage risk of AIDS can be calculated as

[1 − exp(−0.5Rate)] × 100%

Values of the 6-month risk of AIDS according to various CD4 cell count, viral load and age combinations, calculated using this formula, are shown in Table 4.

Table 4
Table 4
Image Tools
Back to Top | Article Outline

Discussion

One of the several issues [13–15] that need to be considered when deciding whether to start antiretroviral therapy is the risk of development of AIDS if therapy is delayed. The CD4 cell count and the plasma viral load are known to be associated with this risk [1–12] and both are typically monitored every 3–6 months in individuals under clinical care for HIV infection [13–15]. This suggests that this is the most relevant time period over which to assess AIDS risk and to guide clinicians and patients in deciding whether/when to initiate therapy. We have used data from a large collaborative project that pools data from several seroconverter studies to estimate 6-month risk of AIDS according to the viral load and CD4 cell count and age. Although estimates of the risk of AIDS over a longer time period [e.g., 3–9 years in the Multicenter AIDS Cohort Study (MACS) [2]] have been published, our results are the first to our knowledge that focus on the risk of AIDS occurring before the next clinical assessment. This is an important distinction. For example, estimates from the MACS suggest that for a person with viral load [measured using reverse transcriptase–polymerase chain reaction (RT-PCR)] of > 55 000 copies/ml and CD4 cell count > 350 × 106 cells/l there is an estimated 39.6% risk of developing AIDS in the next 3 years (85.0% in 9 years) [15]. These values are used to help to decide on when to initiate therapy and it may appear, at first sight, that this risk is sufficiently high to indicate that therapy should be started. However, our estimates suggest that the 6-month risk for such a person (CD4 cell count ≥ 350 × 106 cells/l and viral load > 100 000 copies/ml) is 2.2% (Table 2). Many might consider this level of risk acceptable and defer therapy at least until the next patient visit in 3–6 months, when a further CD4 cell count and viral load measurement would be available. It is also worth noting that there are 1442 individuals in our analysis contributing to risk estimates for the CD4 cell count range 200–350 × 106 cells/l – a key area of uncertainty over therapy initiation – compared with 231 used for estimates in the treatment guidelines [2,15]. It is difficult to compare 6-month risks directly with those from the MACS, but visual inspection of Kaplan–Meier curves from the MACS suggests that in the highest viral load group (> 55 000 copies/ml using RT-PCR) risks are approximately 40%, 10% and 3% for CD4 cell count groups < 200, 200–349 and > 350 × 106 cells/l, respectively, somewhat higher than values we obtained [2]. However, in the lower viral load categories, the estimated 6-month risks in the MACS were zero in most CD4 cell count/viral load groups and hence lower than our estimates.

The viral load assay used was known for 82% of values. Of these, 67% were measured using one of the Roche RT-PCR assays. We could not detect a statistically significant difference in the association between CD4 cell count/viral load and risk of AIDS according to assay in our models. However, the Chiron branched DNA assay, which has previously been shown to measure approximately twofold lower than the Roche RT-PCR assay [2], was known to have been used for only 7.9% of viral load values. Although in our analysis we did not adjust viral load values measured using the Chiron assay, it should be born in mind when using our risk estimates in conjunction with this assay that the true risk might be more accurately estimated from two times the viral load value than from the crude value.

It has been suggested that there may be differences between men and women in the association between viral load and risk of AIDS [18]. We found no significant evidence for this in our data, but the number of women was not sufficiently large to rule out such gender differences definitively. There have also been suggestions that viral load levels may be different in those of non-white ethnic group [19,20]. We did not have data on ethnicity for a sufficient proportion of the cohort to be able to study whether CD4 cell count and viral load-specific AIDS risks differ by race; of those with data on race available, 92% were white, so these estimates should be used with caution outside the Caucasion, developed world setting. In agreement with previous reports, we identified an effect of age on risk of AIDS that was independent of the CD4 cell count and viral load [21,22]. Hence we included this in our predictive model for assessing the 6-month risk of AIDS. Although dates of seroconversion could be estimated in these individuals, we did not include this as a potential predictor because this is likely to be unknown in most clinical situations.

There was a statistically significant tendency for the risk of AIDS, for a given CD4 cell count and viral load, to be lower in the period post-January 1997, when viral load assays were mainly measured on fresh samples. There are several possible explanations. It could relate to some tendency for viral loads to be underestimated when the measurement is performed on frozen samples (which have perhaps sometimes been thawed and refrozen) or for stored specimens to be serum rather than plasma [23]. Also, it could be that there was some selection effect, such that those who had a higher risk of AIDS were more likely to have samples frozen. This is difficult to ascertain; however, in general, storing of samples was part of a routine protocol and not dependent on the patient's health status. In either case, this would mean that our 6-month risk estimates, based on viral load measurements measured before and after January 1997, may actually overestimate the risk associated with a given viral load value derived from a fresh sample. Conversely, the risk after January 1997 could be underestimated. There may have been some underreporting of use of therapy, which has increased in recent years, meaning that some individuals on antiretroviral therapy were incorrectly included in our analysis. This should not have occurred to a great extent because we did not include follow-up for individuals when it was uncertain whether or not antiretroviral therapy had been started. Equally, as more potent regimens have become available, individuals at higher risk of AIDS may have been more likely to start therapy, leaving a selected group of low-risk individuals in the analysis after January 1997. We partly addressed this issue by performing an analysis in which we extended follow-up to 1 month after the start date of antiretroviral therapy, to include AIDS events that perhaps triggered the start of therapy but for which formal diagnosis date was after starting highly active antiretroviral therapy. Rates from this analysis did not differ substantially from the main analysis. Another potential explanation for the lower AIDS risk in more recent years is that there may have been increased use of disease-specific prophylaxis. We do not have data to study this possibility directly. However, it would not explain why AIDS risk is lower since January 997 even in those with CD4 cell counts > 200 × 106 cells/l.

We concentrated on risk of AIDS rather than of death because we wished to focus on the endpoint that would be potentially preventable by antiretroviral therapy. This would seem to be the most relevant consideration if the risk estimates are used to decide on when to initiate therapy. A limitation of our approach is that some individuals will die from an AIDS disease but this will never have been formally diagnosed. We do not have sufficiently accurate data on cause of death on all individuals in the joint cohort to distinguish reliably HIV-related deaths from other deaths.

Although our main focus is on the risk of AIDS in the absence of therapy, we did include individuals who were on therapy before September 1995. At this time, most individuals on antiretroviral therapy were treated with zidovudine monotherapy, although some individuals in clinical trials were using dual nucleoside therapy. We felt that the association between viral load and CD4 cell count and risk of AIDS was not likely to be markedly affected by use of therapy at this time. In agreement with this, results from a subanalysis of those who had never started any antiretroviral therapy gave similar results to our main analysis.

Another feature of our analysis was that person-years were attributable to the most recent CD4 cell count and viral load measurement. This means that if a new CD4 cell count or viral load was measured then the period of follow-up was ended and continued follow-up was reallocated to a new CD4 cell count/viral load category (possibly to the same one). In some sense, there is the possibility of informative censoring, if visits and CD4 cell count/viral load measurements were driven by clinical symptoms. However, the fact that we were censoring from an observation period but not from the entire analysis makes it unlikely there was an overall tendency to underestimate CD4 cell count/viral load-specific rates.

Recently, some studies have assessed the risk of AIDS according to the latest CD4 cell count and viral load in individuals who have started antiretroviral therapy with three or more drugs [24,25]. In this setting, it appears that the viral load has relatively less independent prognostic value than in the untreated/monotherapy situation we have studied.

In summary, we have generated estimates of the short-term risk of AIDS diseases according to the current CD4 cell count and viral load in untreated or monotherapy-treated individuals. These estimates are potentially useful for decisions concerning whether to initiate antiretroviral therapy.

Sponsorship: CASCADE is funded through a grant from the European Union (QLK2–2000–01431) and has received additional funding from GlaxoSmithKline.

Back to Top | Article Outline

References

1. Mellors JW, Rinaldo CR, Gupta P, White RM, Todd JA, Kingsley LA, et al. Prognosis in HIV-1 infection predicted by the quantity of virus in plasma. Science 1996, 272:1167–1170.

2. Mellors JW, Muñoz A, Giorgi JV, Margolick JB, Tassoni CJ, Gupta P, et al. Plasma viral load and CD4+ lymphocytes as prognostic markers of HIV-1 infection. Ann Intern Med, 1997, 126: 946–954.

3. Ruiz L, Romeu J, Clotet B, Balague M, Cabrera C, Sirera G, et al. Quantitative HIV-1 RNA as a marker of clinical stability and survival in a cohort of 302 persons with a mean CD4 cell count of 300 × 106/l. AIDS 1996, 10:F39–F44.

4. de Wolf F, Spijkerman I, Schellekens PT, Langendam M, Kuiken C, Bakker M, et al. AIDS prognosis based on HIV-1 RNA, CD4 T-cell count and function: Markers with reciprocal predictive value over time after seroconversion. AIDS 1997, 11: 1799–1806.

5. Spijkerman IJB, Prins M, Goudsmit J, Veugelers PJ, Coutinho RA, Miedema F, et al. Early and late HIV-1 RNA level and its association with other markers and disease progression in long-term AIDS-free homosexual men. AIDS 1997, 11:1383–1388.

6. Lyles RH, Muñoz A, Yamashita TE, Bazmi H, Detels R, Rinaldo CR, et al. Natural history of human immunodeficiency virus type 1 viremia after seroconversion and proximal to AIDS in a large cohort of homosexual men. J Infect Dis 2000, 181:872–880.

7. Sabin CA, Devereux H, Phillips AN, Hill A, Janossy G, Lee CA, et al. Course of viral load throughout HIV-1 infection. J AIDS 2000, 23:172–177.

8. Hubert JB, Burgard M, Dussaix E, Tamalet C, Deveau C, Le Chenadec J, et al. Natural history of serum HIV-1 RNA levels in 330 persons with a known date of infection. AIDS 2000, 14:123–131.

9. Sabin CA, Devereux H, Phillips AN, Janossy G, Loveday C, Lee CA. Immune markers and viral load after HIV-1 seroconversion as predictors of disease progression in a cohort of haemophilic men. AIDS 1998, 12:1347–1352.

10. Lyles CM, Dorrucci M, Vlahov D, Pezzotti P, Angarano G, Sinicco A, et al. Longitudinal human immunodeficiency virus type 1 load in the Italian seroconversion study: correlates and temporal trends of virus load. J Infect Dis 1999, 180:1018–1024.

11. Cozzi Lepri A, Katzenstein TL, Ullum H, Phillips AN, Skinhoj P, et al. The relative prognostic value of plasma HIV RNA levels and CD4 lymphocyte counts in advanced HIV infection. AIDS 1998, 12:1639–1643.

12. Engels EA, Rosenberg PS, O'Brien TR, Goedert JJ. Plasma HIV viral load in patients with hemophilia and late-stage HIV disease: a measure of current immune suppression. Ann Intern Med 1999, 131:256.

13. BHIVA Writing Committee on behalf of the BHIVA Executive Committee. British HIV Association guidelines for the treatment of HIV infected adults with antiretroviral therapy. HIV Med 2001, 1:76–101.

14. Carpenter CCJ, Cooper DA, Fischl MA, Gatell JM, Gizzard BG, Hammer SM, et al. Antiretroviral therapy in adults. Updated recommendations of the International AIDS Society-USA Panel. JAMA 2000, 283:381–390.

15. Department of Health and Human Services. Guidelines for the use of Antiretroviral Agents in HIV-infected Adults and Adolescents. Aug 2001 http://www.hivatis.org.

16. CASCADE Collaboration. Changes in uptake of antiretroviral therapy and survival in persons with known duration of infection in Europe. HIV Med 2000, 1:224–231.

17. Clayton D, Hills M. Statistical Models in Epidemiology. Oxford: Oxford University Press; 1993.

18. Farzadegan H, Hoover DR, Astemborski J, Lyles CM, Margolick JB, Markham RB, et al. Sex differences in HIV-1 viral load and progression to AIDS. Lancet 1998 352:1510–1514.

19. Anastos K, Gange SJ, Lau B, Weiser B, Detels R, Giorgi JV, et al. Association of race and gender with HIV-1 RNA levels and immunologic progression. J AIDS 2000 24:218–226.

20. Saul J, Erwin J, Sabin CA, Kulasegaram R, Peters BS. The relationships between ethnicity, sex, risk group, and virus load in human immunodeficiency virus type 1 antiretroviral-naive patients. J Infect Dis 2001, 183:1518–1521.

21. Carré N, Boufassa F, Hubert JB, Chavance M, Rouzioux C, Goujard C, et al. Predictive value of viral load and other markers for progression to clinical AIDS after CD4+ cell count falls below 200 μl. Int J Epidemiol 2001, 27:897–903.

22. Operskalski EA, Mosley JW, Busch MP, Stram DO. Influences of age, viral load, and CD4+ count on the rate of progression of HIV-1 infection to AIDS. Transfusion Safety Study Group. J AIDS 1997, 15:243–244.

23. Bruisten SM, Oudshoorn P, van Swieten P, Boeser Nunnink B, van Aarle P, Tondreau SP, et al. Stability of HIV-1 RNA in blood during specimen handling and storage prior to amplification by NASBA-QT. J Virol Meth 1997, 67:199–207.

24. Lundgren JD, Mocroft A, Gatell J, Ledergerber B, d'Arminio Monforte A, Hermans P, for the EuroSIDA Study Group. A clinically prognostic scoring system for patients receiving highly active antiretroviral therapy: results from the EuroSIDA study. J Infect Dis 2001, 185:178–187.

25. Hogg RS, Yip B, Chan KJ, Wood E, Craib KJ, O'Shaughnessy MV, et al. Rates of disease progression by baseline CD4 cell count and viral load after initiating triple-drug therapy. JAMA 2001, 286:2568–2577.

Back to Top | Article Outline
Appendix

Analysis and Writing Committee: Andrew N Phillips and Patrizio Pezzotti.

Steering Committee: Valerie Beral, Roel Coutinho, Janet Darbyshire (Project Leader), Julia Del Amo, Noël Gill (Chairman), Christine Lee, Laurence Meyer, Giovanni Rezza.

Coordinating Centre: Kholoud Porter (Scientific Coordinator), Abdel Babiker, A. Sarah Walker, Janet Darbyshire, Freya Tyrer.

Collaborators: Aquitaine cohort, France: Francois Dabis, Rodolphe Thiebaut, Geneviève Chêne, Sylvie Lawson-Ayayi; SEROCO cohort, France: Laurence Meyer, Faroudy Boufassa; Lyon Primary Infection cohort, France: Philippe Vanhems; German cohort: Osamah Hamouda, Klaus Fischer; Italian Seroconversion Study: Patrizio Pezzotti, Giovanni Rezza; Greek Haemophilia cohort: Giota Touloumi, Angelos Hatzakis, Anastasia Karafoulidou, Olga Katsarou; Edinburgh Hospital cohort, UK: Ray Brettle; Royal Free Haemophilia Cohort, UK: Caroline Sabin, Christine Lee; UK Register of HIV Seroconverters, UK: Anne M. Johnson, Andrew N. Phillips, Abdel Babiker, Janet H. Darbyshire, Noël Gill, Kholoud Porter; MRC Biostatistics Unit, Cambridge, UK: Nicholas E. Day, Daniela De Angelis; Madrid cohort, Spain: Julia Del Amo, Jorge del Romero; Valencia IDU cohort, Spain: Ildefonso Hernández Aguado, Santiago Pérez-Hoyos; Badalona IDU hospital cohort, Spain: Roberto Muga; Amsterdam Cohort Studies among Homosexual Men and Drug Users, the Netherlands: Liselotte van Asten, Birgit van Benthem, Maria Prins, Roel Coutinho; Copenhagen cohort, Denmark: Ole Kirk, Court Pedersen; Oslo and Ulleval Hospital cohorts, Norway: Anne Eskild, Johan N. Bruun, Mette Sannes; Swiss HIV cohort: Patrick Francioli, Philippe Vanhems, Matthias Egger, Martin Rickenbach; Sydney AIDS Prospective Study, Australia: David Cooper, John Kaldor, Lesley Ashton; Sydney Primary HIV Infection cohort, Australia: David Cooper, John Kaldor, Lesley Ashton, Jeanette Vizzard. Cited Here...

Cited By:

This article has been cited 65 time(s).

Plos One
Changing Mortality Profile among HIV-Infected Patients in Rio de Janeiro, Brazil: Shifting from AIDS to Non-AIDS Related Conditions in the HAART Era
Grinsztejn, B; Luz, PM; Pacheco, AG; Santos, DVG; Velasque, L; Moreira, RI; Guimaraes, MRC; Nunes, EOP; Lemos, AS; Ribeiro, SR; Campos, DP; Vitoria, MAA; Veloso, VG
Plos One, 8(4): -.
ARTN e59768
CrossRef
AIDS Research and Human Retroviruses
Patients with Advanced HIV Type 1 Infection Initiating Antiretroviral Therapy in Botswana: Treatment Response and Mortality
Mujugira, A; Wester, CW; Kim, S; Bussmann, H; Gaolathe, T
AIDS Research and Human Retroviruses, 25(2): 127-133.
10.1089/aid.2008.0172
CrossRef
Diagnostic Microbiology and Infectious Disease
Comparison of 3 nucleic acid isolation methods for the quantification of HIV-1 RNA by Cobas Taqman real-time polymerase chain reaction system
Alp, A; Hascelik, G
Diagnostic Microbiology and Infectious Disease, 63(4): 365-371.
10.1016/j.diagmicrobio.2008.12.014
CrossRef
Antiviral Therapy
CD4(+) T-cell percentage is an independent predictor of clinical progression in AIDS-free antiretroviral-naive patients with CD4+T-cell counts > 200 cells/mm(3)
Guiguet, M; Kendjo, E; Carcelain, G; Abgrall, S; Mary-Krause, M; Tattevin, P; Yazdanpanah, Y; Cotagliola, D; Duval, X
Antiviral Therapy, 14(3): 451-457.

Antiviral Therapy
Late presentation of HIV-infected individuals
Bottegay, M; Fluckiger, U; Hirschel, B; Furrer, H
Antiviral Therapy, 12(6): 841-851.

Journal of Infectious Diseases
Current CD4 cell count and the short-term risk of AIDS and death before the availability of effective antiretroviral therapy in HIV-infected children and adults
Dunn, D; Woodburn, P; Duong, T; Peto, J; Phillips, A; Gibb, D; Porter, K
Journal of Infectious Diseases, 197(3): 398-404.
10.1086/524686
CrossRef
Annals of Internal Medicine
Screening for HIV: A review of the evidence for the US Preventive Services Task Force
Chou, R; Huffman, LH; Fu, RW; Smits, AK; Korthuis, PT
Annals of Internal Medicine, 143(1): 55-73.

AIDS Research and Human Retroviruses
Evolution of CD4(+) T Cell Count in HIV-1-Infected Adults Receiving Antiretroviral Therapy with Sustained Long-Term Virological Suppression
Byakwaga, H; Murray, JM; Petoumenos, K; Kelleher, AD; Law, MG; Boyd, MA; Emery, S; Mallon, PW; Cooper, DA
AIDS Research and Human Retroviruses, 25(6): 569-576.
10.1089/aid.2008.0149
CrossRef
Annals of Internal Medicine
Risk for opportunistic disease and death after reinitiating continuous antiretroviral therapy in patients with HIV previously receiving episodic therapy - a randomized trial
El-Sadr, WM; Grund, B; Neuhaus, J; Babiker, A; Cohen, CJ; Darbyshire, J; Emery, S; Lundgren, JD; Phillips, A; Neaton, JD
Annals of Internal Medicine, 149(5): 289-W62.

Vaccine
Use of predictive markers of HIV disease progression in vaccine trials
Gurunathan, S; El Habib, R; Baglyos, L; Meric, C; Plotkin, S; Dodet, B; Corey, L; Tartaglia, J
Vaccine, 27(): 1997-2015.
10.1016/j.vaccine.2009.01.039
CrossRef
British Medical Journal
When should antiretroviral therapy for HIV be started?
Phillips, AN; Gazzard, BG; Clumeck, N; Losso, MH; Lundgren, JD
British Medical Journal, 334(): 76-78.

Journal of Infectious Diseases
Survival in women exposed to single-dose nevirapine for prevention of mother-to-child transmission of HIV: A stochastic model
Westreich, D; Eron, J; Behets, F; van der Horst, C; Van Rie, A
Journal of Infectious Diseases, 195(6): 837-846.
10.1086/511276
CrossRef
Plos Medicine
Pretreatment CD4 Cell Slope and Progression to AIDS or Death in HIV-Infected Patients Initiating Antiretroviral Therapy-The CASCADE Collaboration: A Collaboration of 23 Cohort Studies
Wolbers, M; Babiker, A; Sabin, C; Young, J; Dorrucci, M; Chene, G; Mussini, C; Porter, K; Bucher, HC
Plos Medicine, 7(2): -.
ARTN e1000239
CrossRef
Infection
SIMIT guidelines for the diagnosis and treatment of HIV infection
[Anon]
Infection, 36(5): 497-508.

Clinical Infectious Diseases
The Absence of CD4(+) T Cell Count Recovery Despite Receipt of Virologically Suppressive Highly Active Antiretroviral Therapy: Clinical Risk, Immunological Gaps, and Therapeutic Options
Gazzola, L; Tincati, C; Bellistri, GM; Monforte, AD; Marchetti, G
Clinical Infectious Diseases, 48(3): 328-337.
10.1086/595851
CrossRef
Clinical Medicine
HIV as a chronic disease
Mahungu, TW; Rodger, AJ; Johnson, MA
Clinical Medicine, 9(2): 125-128.

Infectious Disease Clinics of North America
Antiretroviral management of treatment-naive patients
Gulick, RM
Infectious Disease Clinics of North America, 21(1): 71-+.
10.1016/j.idc.2007.01.002
CrossRef
Jaids-Journal of Acquired Immune Deficiency Syndromes
Late diagnosis of HIV infection: Epidemiological features, consequences and strategies to encourage earlier testing
Girardi, E; Sabin, CA; Monforte, AD
Jaids-Journal of Acquired Immune Deficiency Syndromes, 46(): S3-S8.

Annals of Epidemiology
Determinants of progression to AIDS or death after HIV diagnosis, United States, 1996 to 2001
Hall, HI; McDavid, K; Ling, Q; Sloggett, A
Annals of Epidemiology, 16(): 824-833.
10.1016/j.annepidem.2006.01.009
CrossRef
AIDS
Rate of AIDS diseases or death in HIV-infected antiretroviral therapy-naive individuals with high CD4 cell count
Phillips, AN; Gazzard, B; Gilson, R; Easterbrook, P; Johnson, M; Walsh, J; Leen, C; Fisher, M; Orkin, C; Anderson, J; Pillay, D; Delpech, V; Sabin, C; Schwenk, A; Dunn, D; Gompels, M; Hill, T; Porter, K; Babiker, A
AIDS, 21(): 1717-1721.

AIDS Patient Care and Stds
Immunologic response to protease inhibitor-based highly active Antiretroviral therapy: A review
Wainberg, MA; Clotet, B
AIDS Patient Care and Stds, 21(9): 609-620.
10.1089/apc.2006.0176
CrossRef
Medical Decision Making
The cost-effectiveness of counseling strategies to improve adherence to highly active antiretroviral therapy among men who have sex with men
Zaric, GS; Bayoumi, AM; Deau, MLB; Owens, DK
Medical Decision Making, 28(3): 359-376.
10.1177/0272989X07312714
CrossRef
Hiv Medicine
CD4 cell count and initiation of antiretroviral therapy: trends in seven UK centres, 1997-2003
Stohr, W; Dunn, DT; Porter, K; Hill, T; Gazzard, B; Walsh, J; Gilson, R; Easterbrook, P; Fisher, M; Johnson, MA; Delpech, VC; Phillips, AN; Sabin, CA
Hiv Medicine, 8(3): 135-141.

Journal of the American Geriatrics Society
Human Immunodeficiency Virus in an Aging Population, a Complication of Success
Kirk, JB; Goetz, MB
Journal of the American Geriatrics Society, 57(): 2129-2138.
10.1111/j.1532-5415.2009.02494.x
CrossRef
Journal of Infectious Diseases
Temporal trends in postseroconversion CD4 cell count and HIV load: The Concerted Action on Seroconversion to AIDS and Death in Europe Collaboration, 1985-2002
Dorrucci, M; Rezza, G; Porter, K; Phillips, A
Journal of Infectious Diseases, 195(4): 525-534.

Journal of Infectious Diseases
Major clinical outcomes in antiretroviral therapy (ART)-naive participants and in those not receiving ART at baseline in the SMART study
Emery, S; Neuhaus, JA; Phillips, AN; Babiker, A; Cohen, CJ; Gatell, JM; Girard, PM; Grund, B; Law, M; Losso, MH; Palfreeman, A; Wood, R
Journal of Infectious Diseases, 197(8): 1133-1144.
10.1086/586713
CrossRef
Hiv Medicine
British HIV Association (BHIVA) guidelines for the treatment of HIV-infected adults with antiretroviral therapy (2005)
Gazzard, B
Hiv Medicine, 6(): 1-61.

Bmc Infectious Diseases
Plasma levels of soluble urokinase-type plasminogen activator receptor (suPAR) and early mortality risk among patients enrolling for antiretroviral treatment in South Africa
Lawn, SD; Myer, L; Bangani, N; Vogt, M; Wood, R
Bmc Infectious Diseases, 7(): -.
ARTN 41
CrossRef
AIDS
Short-term clinical disease progression in HIV-1-positive patients taking combination antiretroviral therapy: the EuroSIDA risk-score
Mocroft, A; Ledergerber, B; Zilmer, K; Krik, O; Hirschel, B; Viard, JP; Reissh, P; Francioli, P; Lazzarini, A; Machalai, L; Phillips, AN
AIDS, 21(): 1867-1875.

Hiv Clinical Trials
CD4+T-lymphocytes natura decrease m HAART-Naive HIV-infected adults in Abidjan
Duvignac, J; Anglaret, X; Kpozehouen, A; Inwoley, A; Seyler, C; Toure, S; Gourvellec, G; Messou, E; Gabillard, D; Thiebaut, R
Hiv Clinical Trials, 9(1): 26-35.
10.1310/hct0901-26
CrossRef
Hiv Medicine
Immunosuppression among HIV-1-positive patients attending for care: experience from two large HIV centres in the United Kingdom
Harte, D; Dosekun, O; Sethi, G; Chadborn, T; de Ruiter, A; Copas, A; Edwards, SG; Miller, RF
Hiv Medicine, 11(2): 114-120.
10.1111/j.1468-1293.2009.00753.x
CrossRef
Antiviral Therapy
Definition and epidemiology of late presentation in Europe
Johnson, M; Sabin, C; Girardi, E
Antiviral Therapy, 15(): 3-8.
10.3851/IMP1522
CrossRef
Lancet
The Trivacan study - Reply
Danel, C; Moh, R; Sorho, S; Gabillard, D; Anglaret, X
Lancet, 368(): 916.

Hiv Medicine
British HIV Association (BHIVA) guidelines for the treatment of HIV-infected adults with antiretroviral therapy (2006)
Gazzard, B
Hiv Medicine, 7(8): 487-503.

Periodontology 2000
Epidemiology, pathogenesis, and management of human immunodeficiency virus infection in patients with periodontal disease
Yin, MT; Dobkin, JF; Grbic, JT
Periodontology 2000, 44(): 55-81.

Internist
Chronic HIV infection
Vogel, M; Rockstroh, JK
Internist, 48(5): 519-524.
10.1007/s00108-007-1845-6
CrossRef
Journal of the Formosan Medical Association
Gender difference in the clinical and Behavioral characteristics of human immunodeficiency virus-infected injection drug users in Taiwan
Cheng, SH; Chiang, SC; Hsieh, YL; Chang, YY; Liu, YR; Chu, FY
Journal of the Formosan Medical Association, 106(6): 467-474.

Hiv Medicine
HIV in the UK 1980-2006: Reconstruction using a model of HIV infection and the effect of antiretroviral therapy
Phillips, AN; Sabin, C; Pillay, D; Lundgren, JD
Hiv Medicine, 8(8): 536-546.

New England Journal of Medicine
Effect of Early versus Deferred Antiretroviral Therapy for HIV on Survival
Kitahata, MM; Gange, SJ; Abraham, AG; Merriman, B; Saag, MS; Justice, AC; Hogg, RS; Deeks, SG; Eron, JJ; Brooks, JT; Rourke, SB; Gill, MJ; Bosch, RJ; Martin, JN; Klein, MB; Jacobson, LP; Rodriguez, B; Sterling, TR; Kirk, GD; Napravnik, S; Rachlis, AR; Calzavara, LM; Horberg, MA; Silverberg, MJ; Gebo, KA; Goedert, JJ; Benson, CA; Collier, AC; Van Rompaey, SE; Crane, HM; McKaig, RG; Lau, B; Freeman, AM; Moore, RD
New England Journal of Medicine, 360(): 1815-1826.
10.1056/NEJMoa0807252
CrossRef
Infections in Medicine
Controversies in the treatment of HIV-1 infection
Kinloch-de Loes, S
Infections in Medicine, 24(8): 335-338.

AIDS Reviews
Treatment of heavily antiretroviral-experienced HIV-infected patients
Van Lunzen, J
AIDS Reviews, 9(4): 246-253.

Hiv Medicine
European AIDS Clinical Society (EACS) guidelines for the clinical management and treatment of HIV-infected adults
Clumeck, N; Pozniak, A; Raffi, F
Hiv Medicine, 9(2): 65-71.
10.1111/j.1468-1293.2007.00533.x
CrossRef
Lancet
Outcomes from monitoring of patients on antiretroviral therapy in resource-limited settings with viral load, CD4 cell count, or clinical observation alone: a computer simulation model
Phillips, AN; Pillay, D; Miners, AH; Bennett, DE; Gilks, CF; Lundgren, JD
Lancet, 371(): 1443-1451.

Hiv Medicine
British HIV Association guidelines for the treatment of HIV-1-infected adults with antiretroviral therapy 2008
Gazzard, BG
Hiv Medicine, 9(8): 563-608.
10.1111/j.1468-1293.2008.00636.x
CrossRef
AIDS Reviews
CD4+ Guided Antiretroviral Treatment Interruption in HIV Infection: A Meta-Analysis
Seminari, E; De Silvestri, A; Boschi, A; Tinelli, C
AIDS Reviews, 10(4): 236-244.

AIDS
Late presenters in the era of highly active antiretroviral therapy: uptake of and responses to antiretroviral therapy
Sabin, CA; Smith, CJ; Gumley, H; Murphy, G; Lampe, FC; Phillips, AN; Prinz, B; Youle, M; Johnson, MA
AIDS, 18(): 2145-2151.

Hiv Clinical Trials
Comparison of prognostic importance of latest CD4+cell count and HIV RNA levels in patients with advanced HIV infection on highly active antiretroviral therapy
MacArthur, RD; Perez, G; Walmsley, S; Baxter, JD; Mullin, CM; Neaton, JD
Hiv Clinical Trials, 6(3): 127-135.

Journal of Clinical Virology
Fully automated quantification of human immunodeficiency virus (HIV) type 1 RNA in human plasma by the COBAS (R) AmpliPrep/COBAS (R) TaqMan (R) system
Schumacher, W; Frick, E; Kauselmann, M; Maier-Hoyle, V; van der Vliet, R; Babiel, R
Journal of Clinical Virology, 38(4): 304-312.
10.1016/j.jcv.2006.12.022
CrossRef
Hiv Medicine
Factors predicting the time for CD4 T-cell count to return to nadir in the course of CD4-guided therapy interruption in chronic HIV infection
Boschi, A; Tinelli, C; Ortolani, P; Arlotti, M
Hiv Medicine, 9(1): 19-26.

Journal of Infectious Diseases
Activation and Coagulation Biomarkers Are Independent Predictors of the Development of Opportunistic Disease in Patients with HIV Infection
Rodger, AJ; Fox, Z; Lundgren, JD; Kuller, LH; Boesecke, C; Gey, D; Skoutelis, A; Goetz, MB; Phillips, AN
Journal of Infectious Diseases, 200(6): 973-983.
10.1086/605447
CrossRef
AIDS
An updated systematic overview of triple combination therapy in antiretroviral-naive HIV-infected adults
Bartlett, JA; Fath, MJ; DeMasi, R; Hermes, A; Quinn, J; Mondou, E; Rousseau, F
AIDS, 20(): 2051-2064.

Antiviral Therapy
Predictors of clinical progression among HIV-1-positive patients starting HAART with CD4+ T-cell counts >= 200 cells/mm(3)
Lapadula, G; Torti, C; Maggiolo, F; Casari, S; Suter, F; Minoli, L; Pezzoli, C; Di Pietro, M; Migliorino, G; Quiros-Roldan, E; Ladisa, N; Sighinolfi, L; Costarelli, S; Carosi, G
Antiviral Therapy, 12(6): 941-947.

Future Virology
Impact of age on markers of HIV-1 disease
Pirrone, V; Libon, DJ; Sell, C; Lerner, CA; Nonnemacher, MR; Wigdahl, B
Future Virology, 8(1): 81-101.
10.2217/FVL.12.127
CrossRef
Irish Journal of Medical Science
Late presentation of HIV despite earlier opportunities for detection, experience from an Irish Tertiary Referral Institution
O'Shea, D; Ebrahim, M; Egli, A; Redmond, D; McConkey, S
Irish Journal of Medical Science, 182(3): 389-394.
10.1007/s11845-012-0898-2
CrossRef
AIDS
National adult antiretroviral therapy guidelines in South Africa: concordance with 2003 WHO guidelines?
Lawn, SD; Wood, R
AIDS, 21(1): 121-122.
10.1097/QAD.0b013e3280117fa5
PDF (312) | CrossRef
AIDS
Gender and race do not alter early-life determinants of clinical disease progression in HIV-1 vertically infected children
European Collaborative Study,
AIDS, 18(3): 509-516.

PDF (111)
AIDS
Late diagnosis in the HAART era: proposed common definitions and associations with mortality
The UK Collaborative HIV Cohort (UK CHIC) Steering Committee,
AIDS, 24(5): 723-727.
10.1097/QAD.0b013e328333fa0f
PDF (280) | CrossRef
AIDS
No time to wait: how many HIV-infected homosexual men are diagnosed late and consequently die? (England and Wales, 1993–2002)
Chadborn, TR; Baster, K; Delpech, VC; Sabin, CA; Sinka, K; Rice, BD; Evans, BG
AIDS, 19(5): 513-520.

PDF (106)
AIDS
Predictive value of absolute CD4 cell count for disease progression in untreated HIV-1-infected children
HIV Paediatric Prognostic Markers Collaborative Study,
AIDS, 20(9): 1289-1294.
10.1097/01.aids.0000232237.20792.68
PDF (152) | CrossRef
AIDS
No evidence of a change in HIV-1 virulence since 1996 in France
Troude, P; Chaix, M; Tran, L; Deveau, C; Seng, R; Delfraissy, J; Rouzioux, C; Goujard, C; Meyer, L; for the ANRS Primo cohort,
AIDS, 23(10): 1261-1267.
10.1097/QAD.0b013e32832b51ef
PDF (110) | CrossRef
AIDS
Impact of small reductions in plasma HIV RNA levels on the risk of heterosexual transmission and disease progression
Modjarrad, K; Chamot, E; Vermund, SH
AIDS, 22(16): 2179-2185.
10.1097/QAD.0b013e328312c756
PDF (127) | CrossRef
Current Opinion in Infectious Diseases
Should HIV therapy be started at a CD4 cell count above 350 cells/μl in asymptomatic HIV-1-infected patients?
Sabin, CA; Phillips, AN
Current Opinion in Infectious Diseases, 22(2): 191-197.
10.1097/QCO.0b013e328326cd34
PDF (119) | CrossRef
JAIDS Journal of Acquired Immune Deficiency Syndromes
Determinants of HIV Progression and Assessment of the Optimal Time to Initiate Highly Active Antiretroviral Therapy: PISCIS Cohort (Spain)
Jaén, Á; Esteve, A; Miró, JM; Tural, C; Montoliu, A; Ferrer, E; Riera, M; Segura, F; Force, L; Sued, O; Vilaró, J; Garcia, I; Masabeu, A; Altès, J; Clotet, B; Podzamczer, D; Murillas, J; Navarro, G; Gatell, JM; Casabona, J; the PISCIS Study Group,
JAIDS Journal of Acquired Immune Deficiency Syndromes, 47(2): 212-220.
10.1097/QAI.0b013e31815ee282
PDF (415) | CrossRef
JAIDS Journal of Acquired Immune Deficiency Syndromes
Patients' Perceptions of Highly Active Antiretroviral Therapy in Relation to Treatment Uptake and Adherence: The Utility of the Necessity-Concerns Framework
Horne, R; Cooper, V; Gellaitry, G; Date, HL; Fisher, M
JAIDS Journal of Acquired Immune Deficiency Syndromes, 45(3): 334-341.
10.1097/QAI.0b013e31806910e3
PDF (122) | CrossRef
JAIDS Journal of Acquired Immune Deficiency Syndromes
Viral and Host Factors Associated With the HIV-1 Viral Load Setpoint in Adults From Mbeya Region, Tanzania
Saathoff, E; Pritsch, M; Geldmacher, C; Hoffmann, O; Koehler, RN; Maboko, L; Maganga, L; Geis, S; McCutchan, FE; Kijak, GH; Kim, JH; Hoelscher, M; Arroyo, MA; Gerhardt, M; Tovanabutra, S; Robb, ML; Williamson, C; Michael, NL
JAIDS Journal of Acquired Immune Deficiency Syndromes, 54(3): 324-331.
10.1097/QAI.0b013e3181cf30ba
PDF (244) | CrossRef
Back to Top | Article Outline
Keywords:

AIDS risk; CD4 cell count; viral load; therapy initiation; when to start

© 2004 Lippincott Williams & Wilkins, Inc.

Login

Search for Similar Articles
You may search for similar articles that contain these same keywords or you may modify the keyword list to augment your search.