Clinico-immunological outcomes and patient survival
The median time of follow-up was 23.8 months (IQR 22.8–24.0). Among the 416 patients included, 53 patients (12.7%) had died, seven (1.7%) were lost to follow-up, six (1.4%) were transferred and 350 (84.1%) still remained on HAART.
Estimates of survival given by the Kaplan–Meier method were 0.87 at 12 months [95% confidence interval (CI) 0.83–0.90] and 0.85 at 24 months (95% CI 0.81–0.88) when death events were taken into account exclusively (Fig. 2). Two-thirds of the deaths (35/53, 66.0%) occurred within the first 6 months after HAART initiation (median time to death 3.6 months, IQR 1.7–9.8).
Among patients with available CD4 cell counts at baseline (n = 416), the median CD4 cell gain was found to be +101 cells/μl (IQR 62–137) at 6 months (n = 344), +154 cells/μl (IQR 95–217) at 12 months (n = 337) and +233 cells/μl (IQR 156–332) at 24 months (n = 346; Table 2). Alhough they presented with a very low baseline CD4 cell count, 259 out of 346 patients (74.9%) had a CD4 cell count greater than 200 cells/μl at 24 months.
Cross-sectional virological survey of patients continuing HAART
Among the 350 patients still on treatment after 24 ± 2 months at the date of analysis, four could not be included (three refusals, one out of time) and 346 were assessed virologicaly (Fig. 1). Among them, 86 (24.8%) were on stavudine–lamivudine–efavirenz, 74 (21.4%) were on zidovudine–lamivudine–efavirenz, 115 (33.2%) were on stavudine–lamivudine–nevirapine, 50 (14.4%) were on zidovudine–lamivudine–nevirapine and 10 (2.9%) were on other alternative first-line regimen. Only 11 patients (3.2%) were on a protease inhibitor-based regimen (lopinavir/ritonavir).
Viral loads were below 40 copies/ml for 276 out of 346 patients (79.8%), were below 400 copies/ml for 306 (88.4%) and below 1000 copies/ml for 315 (91.0%). Among the 40 patients with a viral load above 400 copies/ml (11.6%), only 15 (4.3%) were above 30 000 copies/ml, corresponding to major virological failure (Table 3).
Overall, in intention-to-treat analysis of the whole M24 cohort (n = 416) taking into account both deaths, loss to follow-up and missing data (Fig. 1) as treatment failures, 66.3% (95% CI 61.5–70.8) and 73.6% (95% CI 69.0–77.7) of treatment successes were observed when considering viral loads as failures when above 40 copies/ml or 400 copies/ml, respectively.
HIV reverse transcriptase genotyping was performed for the 40 patients with viral loads above 400 copies/ml. Among interpretable sequences (n = 39), 10 (25.6%) did not show any drug-resistance-associated mutations, 23 (59.0%) presented with nucleoside reverse transcriptase inhibitor (NRTI) resistance mutations, and 28 (71.8%) presented with non-nucleoside reverse transcriptase inhibitor (NNRTI) resistance mutations. The most frequent NRTI mutation was the lamivudine-induced M184V mutation (23/39), the stavudine-induced T215Y/F mutation (9/39), the T69D/N (7/39) and the D67N (7/39) mutations. Among NNRTI mutations, K103N (15/39), Y181C/I (8/39) and G190A/S (8/39) were most frequently observed (Fig. 3). According to ANRS algorithms , 12 patients with a viral load greater than 400 copies/ml were resistant to both zidovudine, lamivudine, stavudine and nevirapine/efavirenz.
The median concentration of efavirenz for 169 patients was 2946 ng/ml (range < 50–31 559 ng/ml), with seven patients (4.1%) having efavirenz levels either undetectable (n = 4) or less than 1000 ng/ml (n = 3) and 47 (27.8%) having levels greater than 4000 ng/ml. Median trough nevirapine concentrations in 165 patients were 5643 ng/ml (range < 25–16 971 ng/ml), with two patients undetectable (1.2%), six patients (3.6%) with nevirapine levels less than 3000 ng/ml and 21 (12.7%) with levels greater than 8000 ng/ml. For the 11 patients on lopinavir/ritonavir at the time of the study, the median trough plasma concentration of lopinavir was 4962 ng/ml (range 2215–8797 ng/ml) with only one patient less than 3000 ng/ml.
Among the 15 patients with low NNRTI trough or 12-h concentrations at month 24, seven had a viral load greater than 1000 copies/ml (47%). Among them, three displayed a wild-type HIV strain with neither NRTI nor NNRTI-induced RT mutations and had a viral load greater than 30 000 copies/ml. On the other hand, when considering patients with high trough plasma antiretroviral concentrations, none among the 21 patients who had high nevirapine concentrations and three out of 47 patients (6.4%) with high efavirenz levels were found to have a virological failure.
Side effects of patients from the M24 cohort
As a result of drug intolerance, nine patients from the M24 cohort (2.2%) stopped zidovudine (eight for anaemia, and one for anaemia and neutropenia), 131 (31.5%) stopped stavudine (50 for neuropathy, 71 for lipodystrophy, nine for both neuropathy and lipodystrophy, and one for mitochondrial toxicity), and four stopped HAART because of mitochondrial toxicity. Considering NNRTI, three patients (0.7%) stopped efavirenz (one for neuropsychiatric disorders and two for gynaecomasty), and 13 (3.1%) stopped nevirapine (one for hepatitis, eight for hypersensibility and three for severe cutaneous rash). The median duration before stopping the drug was 93 days (IQR 63–140) for zidovudine, 13.9 months (IQR 11.8–16.7) for stavudine, 22 days (IQR 12–28) for nevirapine and between 10 and 674 days for efavirenz (n = 4).
Factors associated with a viral load greater than 1000 copies/ml at 24 months
In order to identify patients at risk of having a detectable viral load, we analysed the association of distinct factors present either at baseline, at 12 or at 24 months with a viral load greater than 1000 copies/ml at 24 months. The aim here was more to identify the group of patients potentially needing a decision to switch their treatment than describing factors associated with virological failures. For this reason, and because of the well-known existence of virological blips, we chose 1000 copies/ml as a reasonable threshold. Higher viral load thresholds could not be tested because of too small sample sizes.
Using univariate logistic regression analysis, the following factors were found to be associated with viral loads greater than 1000 copies/ml at 24 months: age greater than 35 years (P = 0.008), previous antiretroviral exposure (P = 0.004), CD4 cell count less than 200 cells/μl (P = 0.001) or between 200 and 350 cells/μl (P = 0.041), an opportunistic infection between months 9 and 24 (P = 0.041), low plasma antiretroviral concentrations at month 24 (P < 0.001) and an insufficient percentage CD4 cell gain at month 24 (P < 0.001). On the other hand, hospitalization between months 9 and 24, antiretroviral drug stops, weight loss between months 12 and 24, BMI at month 24 or being on HAART second line at month 24 were not statistically associated with a viral load greater than 1000 copies/ml (Table 4). Other factors such as sex (P = 0.7) or being a smoker (P = 0.57) were also not associated. Finally, no criteria present at month 12 among the following were found to be predictive of a viral load greater than 1000 copies/ml at month 24: CD4 cell count (P = 0.68), CD4 cell gain (P = 0.7), percentage CD4 cell gain (P = 0.14), weight gain between months 0 and 12 (P = 0.16) or between months 6 and 12 (P = 0.1).
In the multivariate logistic regression model, evident interactions between CD4 cell level at month 24 and percentage CD4 cell gain between months 12 and 24 (M12–M24%CD4 gain) were taken into account and a new variable combining both CD4 cell count and percentage CD4 cell gain was introduced in the model (Table 4). Finally, three independent factors at month 24 remain positively associated with a viral load greater than 1000 copies/ml (Table 4): previous antiretroviral exposure [odds ratio (OR) 12.6, 95% CI 2.6–62.8, P = 0.002], low plasma antiretroviral concentrations at month 24 (OR 11.1, 95% CI 2.6–48.4, P = 0.001), and having both a CD4 cell count less than 350 cells/μl and a M12–M24%CD4 gain of +23% or less (OR 10.6, 95% CI 2.2–51.7, P = 0.003).
ROC curve analysis was used to define this cut-off of M12–M24% CD4 gain giving the highest sensitivity and specificity for the detection of viral loads greater than 1000 copies/ml. The percentage CD4 cell gain cut-off at 23% gave optimal sensitivity (66.7%, 95% CI 46.0–83.5) and specificity (73.4%, 95% CI 68.0–78.3).
When combining such criteria to improve the detection of patients with viral loads greater than 1000 copies/ml at 24 months, we found that the following association was optimal: previous exposure to ART, or CD4 cell count at month 24 less than 350 cells/μl and a M12–M24%CD4 gain of +23% or less. Using such combined criteria greatly improved the sensitivity (74%, 95% CI 53.7–88.9) with a similar specificity (75%, 95% CI 69.3–79.4). The positive and negative predictive values were 20.6% (95% CI 13.1–30.0) and 97% (95% CI 93.9–98.8), respectively.
The present analysis of 416 patients performed 24 months after HAART initiation in Cambodia found 75% of treatment successes (patients still followed with a viral load < 400 copies/ml) in an intention-to-treat analysis and a 0.85 probability of survival at 24 months. Such favourable outcomes are similar or even better than those reported so far in western cohorts [24,25], and are remarkable given the extreme severity of the disease at HAART initiation (see Table 1) in such a resource-limited setting.
Intensive therapeutic education, counselling and psychosocial support might explain the very low rate of lost-to-follow-up patients observed. Such a result was obtained given an important investment in the recruitment and training of counsellors. Human resources remain a key factor in resource-limited settings. As already reported by other studies in similar settings [6,26–31], most of the deaths occurred during the first 6 months after HAART initiation. Advanced clinical stage and low CD4 cell counts might explain such an increased early mortality. Difficulties in managing opportunistic infections as a result of late presentation or limited diagnostic and therapeutic tools as well as malnutrition or immune reconstitution syndrome might also have played a role in worsening the prognosis of such patients [31–34]. Noteworthy is the fact that 20.4% of the patients analysed had either nevirapine or efavirenz concentrations above the therapeutic range without apparent impairment of their tolerance to the antiretroviral regimen. The reasons for such high concentrations are still unclear and do not seem to be related to the low weight of Asian patients compared with western patients [35,36].
Progressive immune restoration was observed over time (Table 2) similar to that reported in similar settings [6,26–28,37] or even in high-income countries for similar patients [26,38]. It is, however, noteworthy that after 2 years a quarter of the patients remained below 200 CD4 cells/μl probably partly because of late HAART initiation with very low baseline CD4 cell counts. It has been reported that patients with poor immune reconstitution at 6 months (CD4 cell count < 100 cells/μl) have a high risk of an AIDS-defining event or progression to death at 5 years . As in our case, the long-term outcomes of patients still below 200 CD4 cells/μl at 24 months remain largely unknown. Further studies are clearly warranted in resource-limited settings in order to determine the long-term outcomes of these patients and understand the factors governing such poor immune restoration.
The virological results of the M24 cohort revealed that among 346 patients, 40 (11.6%) presented with a detectable viral load (> 400 copies/ml), and only 15 (4.3%) were above 30 000 copies/ml. Overall, among patients with detectable viral loads, 25.6% did not have any ART-induced RT HIV mutations and three had low NNRTI trough plasma concentrations corresponding to non-adherent patients. The mutations observed for the other patients were those expected under the first-line regimen used, and the great majority of patients were already resistant to nevirapine/efavirenz (93%) or lamivudine/emtricitabine (79%) (Fig. 3). Overall, these observations are similar to other first-line cohort studies in resource-limited settings describing HIV-1 RT mutation patterns [7,27,40]. This stresses the importance of keeping available several distinct second-line molecules in such settings, and calls upon the international community to make them really and rapidly affordable. In this study, genotyping analysis allowed the detection of non-adherent patients and the selection of appropriate second-line regimens when necessary. Empiric second-line regimens given according to the type of first-line treatments will have to be evaluated through HIV genotyping pilot studies.
In any case, the decision of when to switch remains critical in order to avoid the accumulation of HIV mutations and preserve the efficacy of the limited number of second-line molecules available. In resource-poor settings where access to viral load is largely limited, better identifying groups of patients at risk of treatment failure might help design accurate viral load monitoring strategies. For operational reasons and because of the existence of potential virological blips, we have chosen 1000 copies/ml as a reasonable threshold in order to identify the group of patients potentially needing to switch to a second-line regimen. Logistic regression analysis revealed that previous ART exposure or CD4 cell counts still less than 350 cells/μl with a CD4% gain of less than +23% between months 12 and 24 were two factors strongly associated with viral loads greater than 1000 copies/ml at 24 months. It is noteworthy that no other characteristics of the patients either at baseline, and specifically the CD4 cell count, or at 12 months of follow-up were found to be associated with viral loads greater than 1000 copies/ml at 24 months. A test using both criteria to identify patients with viral loads greater than 1000 copies/ml gave a 74% sensitivity, a 74% specificity and a 21% positive-predictive value. Using more stringent criteria closer to those recommended by WHO (a decrease in CD4 cell gain > 30% between months 12 and 24) gave a lower sensitivity (26%, 95% CI 11–46) with a higher specificity (98%, 95% CI 96–99) and only a 54% (95% CI 25–81) positive-predictive value. Therefore, if a decision to switch was made only according to such stringent criteria, half of the patients would have been switched inappropriately, and most patients with viral loads greater than 1000 copies/ml would have been missed. These observations clearly emphasize that deciding to switch solely on the basis of immunological criteria is not an acceptable option and might even be costly to the programme. Using less stringent criteria allows the identification of a larger group of patients including most patients with a viral load greater than 1000 copies/ml and for whom viral load tests could be proposed in order to help appropriate decision making. Such a strategy clearly strengthens the need to improve access to affordable viral load testing in resource-limited countries .
Data from all patients on HAART for more than 24 months (N = 1036) in this MSF/MoH HIV programme indicate that almost 25.9% (n = 218) would have fulfilled the criteria defined above and would have been proposed a viral load. Among them, approximately 50 viral loads greater than 1000 copies/ml would have been detected, whereas other patients would have had undetectable viral loads. In settings where viral load access is limited, such a strategy identifying patients at risk of failure could be useful to restrict the number of viral loads proposed. Our analysis is still preliminary, however, and needs to be strengthened by further studies on larger numbers of patients. In addition, it appears that the criteria for suspecting failure might vary greatly according to the duration of follow-up. Studies analysing success/failure criteria at distinct timepoints after HAART initiation are on the way in other MSF programmes.
A model to monitor the virological efficacy of HAART in resource-limited settings has recently been proposed by Colebunders et al. . This interesting proposal, mainly based on patients' clinical history, remains to be validated and will have to take into account that a large number of patients still have CD4 cell count less than 200 cells/μl with WHO stage III or IV manifestations even after 2 years of follow-up. Cost-effectiveness analyses would also be important to compare the ‘selective viral load’ approach with a ‘systematic viral load’ approach (proposing a viral load once a year for all patients, for example) in settings where viral load access and second-line regimen availability are limited. As a result of the still high cost of second-line regimens and the complete lack of a third line in resource-poor settings, further studies are clearly warranted to determine the appropriate timepoint for switching to second line, not too early (being aware of poor adherence) but not too late (precluding the accumulation of HIV mutations, which impairs the choice among the limited number of second-line molecules). The efficacy of second-line regimens chosen without genotyping information is currently being assessed in Cambodia.
The authors thank Dr Jean-Marc Reynes who was the co-supervisor of N.Ly.'s PhD thesis. They would also like to thank all the people involved in the study and in the implementation of the programme at the KSF Hospital including the MoH of Cambodia, the personnel of the KSF Hospital, the MSF staff and MSF headquarters. They would also like to thank the patients and their families for their participation in the study.
Contributors: D. Laureillard, L. Ferradini, S. Balkan and J.F. Delfraissy contributed to the study concept and design. L. Ferradini was the study coordinator. N. Prak, C. Ngeth, M. Fernandez and G. Puertas actively collected data in the field. C. Rouzioux and N. Ly performed the virological evaluation. A.M. Taburet performed the pharmacological analysis. L. Ferradini and L. Pinoges performed the statistical analysis. D. Laureillard, C. Quillet, L. Ferradini, and J.F. Delfraissy led the writing of the paper, and all investigators participated in its final writing and editing.
The results were presented in part at the International AIDS Society Conference, Rio de Janeiro, Brazil, in July 2005.
Sponsorship: The study was funded by Sidaction (grant no. 617-001-00/A015-2) and Médecins Sans Frontiéres.
Conflicts of interest: None.
1. Laurent C, Diakhate N, Gueye NF, Toure MA, Sow PS, Faye MA, et al
. The Senegalese government's highly active antiretroviral therapy
initiative: an 18-month follow-up study. AIDS 2002; 16:1363–1370.
2. Orrell C, Bangsberg DR, Badri M, Wood R. Adherence is not a barrier to successful antiretroviral therapy
in South Africa. AIDS 2003; 17:1369–1375.
3. Tassie JM, Szumilin E, Calmy A, Goemaere E. Highly active antiretroviral therapy
in resource-poor settings: the experience of Medecins Sans Frontieres. AIDS 2003; 17:1995–1997.
4. Kumarasamy N, Solomon S, Chaguturu SK, Mahajan AP, Flanigan TP, Balakrishnan P, et al
. The safety, tolerability and effectiveness of generic antiretroviral drug regimens for HIV
-infected patients in south India. AIDS 2003; 17:2267–2269.
5. Weidle PJ, Malamba S, Mwebaze R, Sozi C, Rukundo G, Downing R, et al
. Assessment of a pilot antiretroviral drug therapy programme in Uganda: patients' response, survival, and drug resistance. Lancet 2002; 360:34–40.
6. Coetzee D, Hildebrand K, Boulle A, Maartens G, Louis F, Labatala V, et al
after two years of providing antiretroviral treatment in Khayelitsha, South Africa. AIDS 2004; 18:887–895.
7. Bourgeois A, Laurent C, Mougnutou R, Nkoue N, Lactuock B, Ciaffi L, et al
. Field assessment of generic antiretroviral drugs: a prospective cohort study in Cameroon. Antivir Ther 2005; 10:335–341.
8. Zhou J, Kumarasamy N, Ditangco R, Kamarulzaman A, Lee CK, Li PC, et al
. The TREAT Asia HIV
Observational Database: baseline and retrospective data. J Acquir Immune Defic Syndr 2005; 38:174–179.
9. Carrieri MP, Raffi F, Lewden C, Sobel A, Michelet C, Cailleton V, et al
. Impact of early versus late adherence to highly active antiretroviral therapy
on immuno-virological response: a 3-year follow-up study. Antivir Ther 2003; 8:585–594.
10. Carrieri P, Cailleton V, Le MV, Spire B, Dellamonica P, Bouvet E, et al
. The dynamic of adherence to highly active antiretroviral therapy
: results from the French National APROCO cohort. J Acquir Immune Defic Syndr 2001; 28:232–239.
11. Gill CJ, Hamer DH, Simon JL, Thea DM, Sabin LL. No room for complacency about adherence to antiretroviral therapy
in sub-Saharan Africa. AIDS 2005; 19:1243–1249.
12. UNAIDS. 2004 Report on the global AIDS epidemic. Available at: http://www.unaids.org
. Accessed: June 2007.
14. Ministry of Health of the Kingdom of Cambodia
. National Center for HIV
/AIDS, Dermatoloty and STDs. Second Quaterly Comprehensive Report 2006. Available at: http://www.nchads.org/Reports/reports.php
. Accessed: June 2007.
16. Pasquier C, Sandres K, Salama G, Puel J, Izopet J. Using RT–PCR and bDNA assays to measure nonclade B HIV
-1 subtype RNA. J Virol Methods 1999; 81(1–2):123–129.
17. Rouet F, Ekouevi DK, Chaix ML, Burgard M, Inwoley A, Tony TD, et al
. Transfer and evaluation of an automated, low-cost real-time reverse transcription–PCR test for diagnosis and monitoring of human immunodeficiency virus type 1 infection in a West African resource-limited setting. J Clin Microbiol 2005; 43:2709–2717.
18. Rouzioux C, Hubert JB, Burgard M, Deveau C, Goujard C, Bary M, et al
. Early levels of HIV
-1 DNA in peripheral blood mononuclear cells are predictive of disease progression independently of HIV
-1 RNA levels and CD4+ T cell counts. J Infect Dis 2005; 192:46–55.
19. Ly N, Recordon-Pinson P, Phoung V, Srey C, Kruy LS, Koum K, et al
. Characterization of mutations in HIV
type 1 isolates from 144 Cambodian recently infected patients and pregnant women naive to antiretroviral drugs. AIDS Res Hum Retroviruses 2005; 21:971–976.
21. Wu EY, Wilkinson JM, Naret DG, Daniels VL, Williams LJ, Khalil DA, et al
. High-performance liquid chromatographic method for the determination of nelfinavir, a novel HIV
-1 protease inhibitor, in human plasma. J Chromatogr B Biomed Sci Appl 1997; 695:373–380.
22. Van Heeswijk RP, Hoetelmans RM, Meenhorst PL, Mulder JW, Beijnen JH. Rapid determination of nevirapine in human plasma by ion-pair reversed-phase high-performance liquid chromatography with ultraviolet detection. J Chromatogr B Biomed Sci Appl 1998; 713:395–399.
23. P. Yeni, et al
. Prise en charge médicale des personnes infectées par le VIH – Rapport 2006. Available at: http://www.sante.gouv.fr
24. Bartlett JA, DeMasi R, Quinn J, Moxham C, Rousseau F. Overview of the effectiveness of triple combination therapy in antiretroviral-naive HIV
-1 infected adults. AIDS 2001; 15:1369–1377.
25. Bartlett JA, Buda JJ, Von SB, Mauskopf JA, Davis EA, Elston R, et al
. Minimizing resistance consequences after virologic failure on initial combination therapy: a systematic overview. J Acquir Immune Defic Syndr 2006; 41:323–331.
26. Dabis F, Balestre E, Braitstein P, Miotti P, Brinkhof WG, Schneider M, et al
. Cohort profile: antiretroviral therapy
in lower income countries (ART-LINC): international collaboration of treatment cohorts. Int J Epidemiol 2005; 34:979–986.
27. Ferradini L, Jeannin A, Pinoges L, Izopet J, Odhiambo D, Mankhambo L, et al
. Scaling up of highly active antiretroviral therapy
in a rural district of Malawi: an effectiveness assessment. Lancet 2006; 367:1335–1342.
28. Ivers LC, Kendrick D, Doucette K. Efficacy of antiretroviral therapy
programs in resource-poor settings: a meta-analysis of the published literature. Clin Infect Dis 2005; 41:217–224.
29. Severe P, Leger P, Charles M, Noel F, Bonhomme G, Bois G, et al
. Antiretroviral therapy
in a thousand patients with AIDS in Haiti. N Engl J Med 2005; 353:2325–2334.
30. Lawn SD, Myer L, Orrell C, Bekker LG, Wood R. Early mortality among adults accessing a community-based antiretroviral service in South Africa: implications for programme design. AIDS 2005; 19:2141–2148.
31. Madec Y, Laureillard D, Pinoges L, Fernandez M, Prak N, Ngeth C, et al
. Response to highly active antiretroviral therapy
among severely immuno-compromised HIV
-infected patients in Cambodia
. AIDS 2007; 21:351–359.
32. Lawn SD, Bekker LG, Miller RF. Immune reconstitution disease associated with mycobacterial infections in HIV
-infected individuals receiving antiretrovirals. Lancet Infect Dis 2005; 5:361–373.
33. Colebunders R, John L, Huyst V, Kambugu A, Scano F, Lynent L. Tuberculosis immune reconstitution inflammatory syndrome in countries with limited resources. Int J Tuberc Lung Dis 2006; 10:946–953.
34. Lawn SD, Bekker LG, Myer L, Orrell C, Wood R. Cryptococcocal immune reconstitution disease: a major cause of early mortality in a South African antiretroviral programme. AIDS 2005; 19:2050–2052.
35. Marzolini C, Paus E, Buclin T, Kim RB. Polymorphisms in human MDR1 (P-glycoprotein): recent advances and clinical relevance. Clin Pharmacol Ther 2004; 75:13–33.
36. Gonzalez de RD, Bonora S, Garazzino S, Sciandra M, D'Avolio A, Raiteri R, et al
. Nevirapine plasma exposure affects both durability of viral suppression and selection of nevirapine primary resistance mutations in a clinical setting. Antimicrob Agents Chemother 2005; 49:3966–3969.
37. Calmy A, Pinoges L, Szumilin E, Zachariah R, Ford N, Ferradini L. Generic fixed-dose combination antiretroviral treatment in resource-poor settings: multicentric observational cohort
. AIDS 2006; 20:1163–1169.
38. Le Moing V, Thiebaut R, Chene G, Leport C, Cailleton V, Michelet C, et al
. Predictors of long-term increase in CD4(+) cell counts in human immunodeficiency virus-infected patients receiving a protease inhibitor-containing antiretroviral regimen. J Infect Dis 2002; 185:471–480.
39. Grabar S, Le Moing V, Goujard C, Egger M, Leport C, Kazatchkine MD, et al
. Response to highly active antiretroviral therapy
at 6 months and long-term disease progression in HIV
-1 infection. J Acquir Immune Defic Syndr 2005; 39:284–292.
40. Laurent C, Kouanfack C, Koulla-Shiro S, Nkoue N, Bourgeois A, Calmy A, et al
. Effectiveness and safety of a generic fixed-dose combination of nevirapine, stavudine, and lamivudine in HIV
-1-infected adults in Cameroon: open-label multicentre trial. Lancet 2004; 364:29–34.
41. Calmy A, Ford N, Hirschel B, Reynolds SJ, Lynen L, Goemaere E, et al
viral load monitoring in resource-limited regions: optional or necessary? Clin Infect Dis 2007; 44:128–134.
42. Colebunders R, Moses KR, Laurence J, Shihab HM, Semitala F, Lutwama F, et al
. A new model to monitor the virological efficacy of antiretroviral treatment in resource-poor countries. Lancet Infect Dis 2006; 6:53–59.
Keywords:© 2007 Lippincott Williams & Wilkins, Inc.
antiretroviral therapy; Cambodia; HIV; observational cohort; outcomes