In the developed world, progression to AIDS and AIDS-related mortality has fallen dramatically since the mid-1990s, predominantly as a result of highly active antiretroviral therapy (HAART) [1–3]. In resource limited countries where the burden of HIV/AIDS is far greater, access to antiretroviral therapy (ART) is poor and deaths from AIDS continue to rise . Despite this disparity, recent reports of effective ART programs in resource limited countries have provided optimism for the global HIV/AIDS response . As global ART access improves, several questions arise for resource limited settings. First, can HIV disease progression be retarded to the same degree as achieved in developed countries? Second, are factors that predict response to ART similar across divergent settings? Third, what are appropriate recommendations for initiation of ART in resource limited settings? In resource limited settings the World Health Organization (WHO) recommends initiating treatment in asymptomatic patients with CD4 cell counts ≤200 × 106/l .
Thailand is a middle-income country with a generalized HIV epidemic affecting all sections of the population, and a history of successful interventions to reduce HIV spread [7,8]. The Ministry of Public Health (MOPH) estimates the cumulative number of HIV infections since 1984 to be 1 065 000, that there are currently 635 000 people living with HIV/AIDS and 29 000 new infections occur per year. Thailand was one of the first countries outside the developed world to implement a national ART program. Dual combination ART was introduced in 1995, and since December 2000 the MOPH has recommended HAART as the standard of care .
The HIV Netherlands, Australia, Thailand Research Collaboration (HIV-NAT) was established in 1996 as a collaboration between The National Centre in HIV Epidemiology and Clinical Research (NCHECR), Sydney, Australia, The International Antiviral Therapy Evaluation Center (IATEC), Amsterdam, The Netherlands and The Thai Red Cross AIDS Research Centre, Bangkok, Thailand.
In the light of the recent revision in WHO recommendations for initiation of ART in resource limited settings , and to examine the impact of ART on HIV disease progression patients treated via a clinical research network in such a setting, we conducted a cohort analysis of HIV-NAT patients treated with ART between 1996 and 2002.
Data from all adult patients in HIV-NAT-initiated studies were included in this cohort analysis. Details of the trial protocols are given in Table 1. Patients who were ARV-naive at study entry were all initially treated with non-HAART regimens (HIV-NAT study series 001, 002 and 003 from 1996 to 1998, Table 1). Those who were ARV-experienced were treated with HAART (HIV-NAT studies 005) or with efavirenz and ritonavir boosted indinavir (HIV-NAT 009) from 1999 to 2002. To ensure continuity of ART for HIV-NAT patients, a series of rollover protocols was used. As an example, patients initially enrolled in 001 would be rolled over into 001.1, then 001.2 and so on (Table 1). At the completion of study series 001–009, all patients were enrolled into multicentre trials of HAART [10,11], or to a long-term HIV-NAT study of HAART for those who have exhausted ‘rollover’ protocol options.
Patients were reviewed at least every 3 months, and study visits included a history and physical examination, assessment for HIV disease progression and ARV toxicity, and blood collection for biochemical, haematological, immunological and virological assessments. The physician/study nurse provided adherence counselling at each visit, after a pill count of returned drugs. All patients received opportunistic infection (OI) prophylaxis according to Thai National Guidelines . The studies were approved by the Ethics Committees of participating sites, and by the Ethics Committee of the Ministry of Public Health for studies involving drugs not licensed in Thailand; all trial participants provided written informed consent.
T-lymphocyte subsets were measured using standard flow cytometry. Quantification of HIV-1 RNA in plasma in the 001 series was by the Amplicor Monitor PCR assay (Roche Diagnostics). HIV-1 RNA estimation in all other trials was conducted using the branched DNA assay (Chiron Corporation Quantiplex bDNA assay, Chiron Diagnostics, Emeryville, California, USA). Initially, the lower limit of detection (LLD) was 500 and 400 copies/ml for both assays respectively, but ultra-sensitive tests with LLD <50 copies/ml were phased in during 1998.
The primary endpoint for the cohort analysis was HIV disease progression measured as time to a combined endpoint (‘event’) of first AIDS-defining disease, additional AIDS defining illnesses during the trial [patients who were Centers for Disease Control and Prevention (CDC) category C at study entry] or death from any cause, as first events. The CDC 1993 revised classification system for HIV infection  was used for clinical stage classifications.
All statistical analyses were done with SAS, Version 8 (SAS Institute Inc. Cary, North Carolina, USA). Progression analyses were ‘intention to continue treatment’, ignoring subsequent changes to treatment regimens, treatment interruptions and terminations. At progression to first occurrence of combined endpoint, persons were censored from further follow-up. Initially we assessed time to disease progression using baseline laboratory values, demographic and treatment characteristics of patients in the cohort who had begun trial ART. Based on previous studies [13–15], nine candidate prognostic variables were included in the modelling: baseline CD4 cell count, baseline viral load, age, sex, clinical stage, transmission mode, treatment regimen used in the first 6-month period, whether the patient was ART naive or experienced, and whether protease inhibitors were used in the ART regimen at any time.
Since Chene et al.  demonstrated that immunological and virological responses to initial treatment regimen more accurately predicted disease progression, we conducted a second analysis to explore this phenomenon. Patients who were lost to follow-up during before their 6-month visit were excluded from this analysis. Disease progression in this analysis was defined as ‘events’ occurring after the first 6 months of therapy. In addition to the candidate prognostic variables modelled in analysis 1, we also modelled CD4 cell count and log10 viral load after 6 months of therapy, and changes in CD4 cell count and viral load from baseline values.
No data points were missing for any patient in any analysis described in this paper. The Kaplan–Meier method was used to estimate time to event. For patients free of events, follow-up was censored on the date of the most recent visit when the patient was known to be alive without clinical progression, or on 1 December 2002. We initially used the log-rank test to assess whether prognostic variables were independently affecting survival. Variables with a P < 0.25 were included in the final multivariate model. The Cox proportional hazards model was used for model selection and validation, and all assumptions for Cox proportional hazards modelling were tested and met. Univariate screening was performed on all prognostic candidate variables and compared to the log-rank test scores. Thereafter, we used a stepwise backwards selection procedure. Continuous covariates were initially modelled as continuous and then categorical. We subsequently assessed whether factors excluded in the univariate analysis might be significant in the presence of other factors by forward modelling these factors into the reduced model.
Four hundred and seventeen patients were included in the analysis of studies occurring between 1996 and 2002. Of these, 371 (89%) patients acquired HIV through heterosexual transmission, 40 (9.5%) through homosexual/bisexual transmission, and six (1.5%) through other transmission routes. In the baseline analysis, the mean age of subjects was 32 years [median 31 years, interquartile range (IQR), 27–36 years]. Median duration of patient follow up was 62.3 (IQR, 38.8–69.8; mean 49.7; range, 0–73.8) months. Dropout rates at 12, 24 and 36 months were 10%, 17% and 21% respectively. Two hundred and ninety-five (71%) patients were ART naive at trial entry (trial series HIV-NAT 001, 002 and 003) and initially treated with non-HAART regimens. For these patients, the median baseline CD4 cell count was 316 (IQR, 230–412) × 106/l and median baseline viral load was 24 820 (IQR, 4920–84 770) copies/ml. Patients who were ART experienced at trial entry (HIV-NAT 005 and 009) were treated with HAART (25%) and non-HAART regimens (4%). For these patients, median baseline CD4 cell count was 135 (IQR, 29–270) × 106/l and median baseline viral load was 12 529 (IQR, 2807–51 897) copies/ml.
Characteristics at 6 months
The ‘6-month’ visit was scheduled to occur at 24 weeks; the median time at which clinic visits occurred was 5.53 (IQR, 5.53–5.66) months. After excluding patients with follow-up times or events that occurred before 6 months, the reduced risk set comprised 397 patients. The median age of patients in this analysis was 31 (IQR, 27–36) years. The median CD4 cell count at baseline for ART-naive patients was 318 (IQR, 238–413) × 106/l, and median viral load was 23 790 (IQR, 4863–82 775) copies/ml. The median baseline CD4 cell count for ART-experienced patients in the 6-month analysis was 110 (IQR, 27–264) × 106/l and median viral load was 14 188 (IQR, 3304–51 897) copies/ml.
In the first 6-month period, non-HAART regimens used to treat the 295 ARV-naive patients were didanosine only (3%), stavudine and didanosine (15%), zidovudine and lamivudine (13%), zidovudine and zalcitabine (27%), zidovudine, lamivudine and didanosine (13%). Regimens used to treat 122 ART-experienced patients were lamivudine, zidovudine and indinavir (13%), lamivudine, zidovudine, ritonavir and indinavir (12%), efavirenz, ritonavir and indinavir (4%). After 6 months of therapy, the median CD4 cell count for patients who were ART-naive at trial entry had increased from 316 to 386 (IQR, 311–474) × 106/l, and median viral load was 500 (IQR, 400–3560) copies/ml. The 6-month median CD4 cell count for patients who were ART-experienced at trial entry had increased from 135 to 184 (IQR, 98–380) × 106/l, and median viral load was 50 copies/ml (range 50–177 181) copies/ml.
During 1677 person years of follow up there were 29 events. There were 11 deaths (five as first events; three were not AIDS related), and 24 patients developed at least one AIDS defining illness; 311 (75%) patients were censored as of 1 December 2002. Rate of progression to AIDS and death were 1.4 [95% confidence interval (CI), 1.1–2.4] and 0.7 (95% CI, 0.3–1.3) per 100 person years respectively. For those patients in this cohort who progressed to the combined study endpoint, median time to event was 13.9 (IQR, 6.0–22.3) months from trial entry. Tuberculosis (TB) was the most common AIDS defining illness that defined disease progression in our study: 14 of 24 (58%) patients who progressed to a new AIDS illness; 3 of 6 (50%) deaths resulting from opportunistic infections. The median CD4 cell count at the time of TB occurrence was 189 (IQR, 140–395; range, 17–513) × 106/l. The median CD4 cell count at development of AIDS defining illnesses other than TB (three oropharyngeal candidiasis, two Mycobacterium avium complex, two cytomegalovirus retinitis, and one each of Pneumocystis carinii pneumonia, HIV wasting syndrome and cerebral toxoplasmosis) was 38 (IQR, 19.5–65.5; range, 3–212) × 106/l.
Kaplan–Meier plots of the probability of an event for baseline CD4 cell count and viral load are shown in Fig. 1. In the multivariate model, baseline CD4 cell count (P = 0.005), baseline HIV viral load (P = 0.015) and transmission group (P = 0.04) were the only independent predictors of disease progression and death (Table 2). After controlling for these significant variables, patients with a baseline CD4 cell count <200 × 106/l had a relative hazard of disease progression 3.7 times greater than those with a CD4 cell count ≥350 × 106/l. The relative hazard of disease progression for patients with CD4 cell counts between 200 × 106 and 349 × 106/l was equivalent to those with baseline CD4 cell counts ≥350 × 106/l. Compared to patients with a log10 viral load less than 4 log10, patients with baseline viral load greater than 4 log10 or 5 log10, had a relative hazard of disease progression of 1.6 and 2.8 times respectively.
In the multivariate model of factors at 6 months associated with disease progression, CD4 cell count at 6 months (P = 0.0001), HIV viral load at 6 months (P < 0.0001) and transmission group (P = 0.049) were independently associated with disease progression. Change in CD4 cell count or viral load from baseline to 6 months were not significant in multivariate analyses (P = 0.32 and P = 0.62 respectively). Baseline CD4 count and viral load which were strongly associated with disease progression in our baseline analysis were no longer significant in the presence of 6-month CD4 cell count and viral load (Table 3).
In the 6-month multivariate model, patients with a 6-month CD4 cell count <200 × 106/l had a relative hazard of disease progression or death 5.2 times that of patients with a CD4 count ≥350 × 106/l. For patients with a CD4 cell count between 200 × 106 and 349 × 106/l the hazard of disease progression was not significantly different to those with a CD4 count >350 × 106/l. The relative hazard of progression for a patient with a 6-month viral load ≥4 log10 was 4.95 times that of a patient with a 6-month log10 viral load <2.7. The hazard of disease progression with a viral load between 2.7 and 3.99 log10 was not significantly different from when viral load was <2.7 log10.
In a cohort of Thai patients with HIV enrolled in randomized clinical trials of ART through HIV-NAT, both progression to a new AIDS defining illness and mortality were low (1.4 and 0.7 per 100 person years respectively) over a follow-up period of more than 5 years. Factors associated with HIV disease progression included baseline CD4 cell count and HIV viral load, and ART response as measured by CD4 cell count and HIV viral load 6 months following commencement of trial ART.
Both baseline CD4 cell count and HIV viral load were found to be highly predictive of HIV disease progression in the pre-HAART period , and studies conducted since the introduction of HAART have shown similar associations [13,15,17]. The particular CD4 cell count cut-off at which risk of HIV disease progression is increased has remained an issue of considerable interest, as studies have been inconclusive. Given that HIV disease progression was similar and extremely low among patients with baseline CD4 cell counts of 200 × 106–350 × 106/l and ≥350 × 106/l, our study would suggest that ART could generally be delayed until the CD4 cell count is approaching 200 × 106/l.
Consistent with the findings of Chene et al. in their ART naive cohort , the strongest predictors of disease progression and death were CD4 cell count and viral load after 6 months of therapy. In our 6-month analysis, the hazard ratio (HR) of disease progression for patients with log viral load between 2.7 and 3.99 was less than for those with log viral load <2.7, although this difference was not significant, with a wide confidence interval. The apparent equivalence of these two strata, together with the highly significant p for trend when this variable was modelled as continuous, highlights the poorer prognosis of patients with very high six-month viral loads.
Developed country cohorts of patients on HAART have reported death rates ranging from 0.9 to 2.8 per 100 person years, and progression to a new AIDS event rate between 2.6 and 4.8 per 100 person years [3,13,18] (Table 4). In the HIV-NAT cohort, progression to new AIDS illnesses and mortality were 1.4 and 0.7 per 100 person years respectively, which is comparable to the developed country cohorts. A number of differences in baseline characteristics may account for the favourable progression rates amongst our patients compared to the larger mostly European-based cohorts. These latter cohorts were comprised predominantly of males, between 21% and 30% had clinical AIDS at the start of the study period, and the majority of the cohorts had acquired HIV through homosexual contact or by being injecting drug users (IDU). IDU are known to progress at a more rapid rate than other transmission groups [13,19]. In contrast, our cohort had approximately equal numbers of men and women, 8% of whom had clinical AIDS at trial entry, and 89% of participants had acquired HIV through heterosexual transmission. Of interest, the patients in our study comprising IDU and homosexual/bisexual men had a higher rate of progression than the heterosexual transmission group, although this finding was of borderline significance in both baseline and 6-month models, and given the higher proportion of patients in the heterosexual transmission group, should be interpreted with caution.
The most common HIV disease progression event and underlying cause for death was TB, consistent with resource limited setting studies in other parts of Asia and Africa [20–22]. In our cohort, TB developed over a broad range of CD4 cell counts, consistent with other studies in Thailand [23–25]. This highlights the need for a high degree of clinical suspicion of TB in the setting of suggestive symptoms, irrespective of underlying degree of immune deficiency.
Progression events may be related to immune reconstitution syndrome. This syndrome most commonly occurs in the first 3 months after starting ART, especially when the baseline CD4 cell count is <50 × 106/l. In our study, four events (oropharyngeal candidiasis, TB, toxoplasmosis and M. avium complex infection) occurred within 3 months of initiating ART, but only the patient with M. avium complex had a CD4 cell count <50 × 106/l. The extremely low incidence of other AIDS-related opportunistic infections reflects the success of trial ART regimens in terms of CD4 cell count recovery and maintenance, and the success of OI prophylaxis regimens used for patients with lower CD4 counts.
Implications for resource limited countries
Our findings in terms of the importance of baseline CD4 cell count and HIV viral load, and CD4 cell count and HIV viral load after 6 months of ART in predicting HIV disease progression have particular relevance for resource limited settings. Current WHO guidelines advocate starting treatment in asymptomatic patients when the CD4 cell count is at or below 200 × 106/l, a recommendation supported by our study . In addition, in our study, baseline CD4 cell count <200 × 106/l and baseline viral load >100 000 copies/ml were both independently associated with a higher progression to AIDS or death, and both were significant in multivariate analyses after controlling for the other. This supports ART initiation in patients with CD4 cell count >200 × 106/l and HIV viral load >100 000 copies/ml. Since HIV viral load testing remains at prohibitive costs for resource-limited settings, further research is required for cheaper alternative testing methods.
Several limitations in our study should be considered. First, the impact of ART on HIV disease progression in the HIV-NAT clinical trial population may not be representative of other more resource limited settings, even within Thailand. HIV clinic populations in resource-limited settings often have a higher proportion of patients with very advanced HIV disease. Further large-scale studies are required in settings with more limited health care infrastructure for ART delivery and monitoring. Second, the study is unable to address which particular ART regimens may be most appropriate for resource limited settings. Third, although our data suggest that ART can generally be delayed until the CD4 cell count is approaching 200 × 106/l, findings from randomized strategy studies that are currently underway are required for more conclusive recommendations.
In response to the enormous burden of HIV disease, and the lack of ART access in resource poor settings, the WHO recently launched the Treat 3 million by 2005 (‘3 × 5’) initiative . This initiative acknowledges the important role of technical, operational and human capacity building within countries so that ART can be delivered within less developed country settings, and health outcomes can be improved. HIV-NAT provides a model for achieving this goal. In addition to favourable individual HIV disease outcomes, HIV-NAT clinical trials have played a crucial role in capacity building for Thailand and the region. Capacity has been developed in both clinical HIV/AIDS management and clinical trial conduct. Clinical trial nurses have been trained in ART adherence monitoring, extensive laboratory capacity has been developed, and HIV-NAT has been involved in major education and training initiatives. In the context of locally relevant clinical research, HIV-NAT has produced HIV clinical outcomes comparable to developed country settings and provides optimism for the global HIV/AIDS response.
The authors acknowledge all the HIV-NAT patients, as well as the physicians, research nurses, laboratory and administrative personnel who contributed to patient care. We thank Theshinee Chuenyam for helpful discussions in the preparation of this manuscript.
Sponsorship: Funding sources for HIV-NAT trials included in this cohort analysis were Roche (Thailand), Roche Diagnostics, Bristol Myers Squibb, GlaxoSmithKline and Merck. HIV-NAT was responsible for the conduct of all studies, including data collection and management. HIV-NAT, NCHECR and IATEC were responsible for the design of the studies, analysis and interpretation of data, as well as preparation and review of manuscripts. Funding sources provided antiretroviral therapy used in the studies, in addition to funding for laboratory monitoring and salaries of research staff. Our funding sources had no role in the collection, analysis or interpretation of the data for this study.
1 Palella FJ, Delaney KM, Moorman AC, Loveless MO, Fuhrer J, Satten GA, et al
. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. N Engl J Med 1998; 338:853–860.
2 Law MG, Li Y, McDonald AM, Cooper DA, Kaldor JM. Estimating the population impact in Australia of improved antiretroviral treatment for HIV infection. AIDS 2000; 14:197–201.
3 Mocroft A, Ledergerber B, Katlama C, Kirk O, Reiss P, d’Arminio Monforte A, et al
. Decline in the AIDS and death rates in the EuroSIDA study: an observational study. Lancet 2003; 362:22–29.
4 Garnett GP, Bartley L, Grassly NC, Anderson RM. Antiretroviral therapy to treat and prevent HIV/AIDS in resource-poor settings. Nat Med 2002; 8:651–654.
5 Farmer P, Leandre F, Mukherjee JS, Claude M, Nevil P, Smith-Fawzi MC, et al
. Community-based approaches to HIV treatment in resource-poor settings. Lancet 2001; 358:404–409.
8 Ainsworth M, Beyrer C, Soucat A. AIDS and public policy: the lessons and challenges of “success” in Thailand. Health Policy 2003; 64:13–37.
9 Thai Ministry of Public Health. National Guidelines for the Clinical Management of HIV Infection in Children and Adults, 6th Edn
. Nonthaburi: Ministry of Public Health, Thailand; 2000.
10 Emery S, Abrams DI, Cooper DA, Darbyshire JH, Lane HC, Lundgren JD, et al
. The evaluation of subcutaneous proleukin (interleukin-2) in a randomized international trial: rationale, design, and methods of ESPRIT. Control Clin Trials 2002; 23:198–220.
11 Ananworanich J, Nuesch R, Le Braz M, Chetchotisakd P, Vibhagool A, Wicharuk S, et al
. Failures of 1 week on, 1 week off antiretroviral therapies in a randomized trial. AIDS 2003; 17:F33–F37.
12 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
13 Egger M, May M, Chene G, Phillips AN, Ledergerber B, Dabis F, et al
. Prognosis of HIV-1-infected patients starting highly active antiretroviral therapy: a collaborative analysis of prospective studies. Lancet 2002; 360:119–129.
14 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.
15 Mellors JW, Munoz 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.
16 Chene G, Sterne JA, May M, Costagliola D, Ledergerber B, Phillips AN, et al
. Prognostic importance of initial response in HIV-1 infected patients starting potent antiretroviral therapy: analysis of prospective studies. Lancet 2003; 362:679–686.
17 Sterling TR, Chaisson RE, Moore RD. HIV-1 RNA, CD4 T-lymphocytes, and clinical response to highly active antiretroviral therapy. AIDS 2001; 15:2251–2257.
18 Cole SR, Hernan MA, Robins JM, Anastos K, Chmiel J, Detels R, et al
. Effect of highly active antiretroviral therapy on time to acquired immunodeficiency syndrome or death using marginal structural models. Am J Epidemiol 2003; 158:687–694.
19 Perez-Hoyos S, del Amo J, Muga R, del Romero J, Garcia de Olalla P, Guerrero R, et al
. Effectiveness of highly active antiretroviral therapy in Spanish cohorts of HIV seroconverters: differences by transmission category. AIDS 2003; 17:353–359.
20 Harries AD, Hargreaves NJ, Kemp J, Jindani A, Enarson DA, Maher D, et al
. Deaths from tuberculosis in sub-Saharan African countries with a high prevalence of HIV-1. Lancet 2001; 357:1519–1523.
21 Siriarayapon P, Yanai H, Glynn JR, Yanpaisarn S, Uthaivoravit W. The evolving epidemiology of HIV infection and tuberculosis in northern Thailand. J Acquir Immune Defic Syndr 2002; 31:80–89.
22 Hira SK, Dore GJ, Sirisanthana T. Clinical spectrum of HIV/AIDS in the Asia-Pacific region. AIDS 1998; 12(Suppl B):S145–S154.
23 Saenghirunvattana S. CD4+ T counts with a course of antituberculosis therapy in health and HIV-infected patients. J Med Assoc Thai 1996; 79:246–248.
24 Putong NM, Pitisuttithum P, Supanaranond W, Phonrat B, Tansuphasawadikul S, Silachamroon U, et al
. Mycobacertium tuberculosis infection among HIV/AIDS patients in Thailand: Clinical manifestations and outcomes. Southeast Asian J Trop Med Public Health 2002; 33:346–351.
25 Tansuphasawadikul S, Amornkul PN, Tanchanpong C, Limpakarnjanarat K, Kaewkungwal J, Likanonsakul S, et al
. Clinical presentation of hospitalized adult patients with HIV infection and AIDS in Bangkok, Thailand. J Acquir Immune Defic Syndr 1999; 21:326–332.
27 Kroon ED, Ungsedhapand C, Ruxrungtham K, Chuenyam M, Ubolyam S, Newell ME, et al
. A randomized, double-blind trial of half versus standard dose of zidovudine plus zalcitabine in Thai HIV-1-infected patients (study HIV-NAT 001). HIV Netherlands Australia Thailand Research Collaboration. AIDS 2000; 14:1349–1356.
28 Cardiello PG, Samor T, Burger D, Hoetelmans R, Mahanontharit A, Ruxrungtham K, et al
. Pharmacokinetics of lower doses of saquinavir soft-gel caps (800 and 1200 mg twice daily) boosted with itraconazole in HIV-1-positive patients. Antivir Ther 2003; 8:245–249.
29 Ruxrungtham K, Kroon ED, Ungsedhapand C, Teeratakulpisarn S, Ubolyam S, Buranapraditkun S, et al
. A randomized, dose-finding study with didanosine plus stavudine versus didanosine alone in antiviral-naive, HIV-infected Thai patients. AIDS 2000; 14:1375–1382.
30 Ungsedhapand C, Kroon ED, Suwanagool S, Ruxrungtham K, Yimsuan N, Sonjai A, et al
. A randomized, open-label, comparative trial of zidovudine plus lamivudine versus zidovudine plus lamivudine plus didanosine in antiretroviral-naive HIV-1-infected Thai patients. J Acquir Immune Defic Syndr 2001; 27:116–123.
31 Boyd MA, Aarnoutse RE, Ruxrungtham K, Stek M Jr, van Heeswijk RP, Lange JM, et al
. Pharmacokinetics of indinavir/ritonavir (800/100 mg) in combination with efavirenz (600 mg) in HIV-1-infected subjects. J Acquir Immune Defic Syndr 2003; 34:134–139.
Keywords:© 2005 Lippincott Williams & Wilkins, Inc.
CD4 lymphocyte count; viral load; survival analysis; HIV-1; cohort studies; resource-limited setting