Switching HIV Treatment in Adults Based on CD4 Count Versus Viral Load Monitoring: A Randomized, Non-Inferiority Trial in Thailand
Background:
Viral load (VL) is recommended for monitoring the response to highly active antiretroviral therapy (HAART) but is not routinely available in most low- and middle-income countries. The purpose of the study was to determine whether a CD4-based monitoring and switching strategy would provide a similar clinical outcome compared to the standard VL-based strategy in Thailand.
Methods and Findings:
The Programs for HIV Prevention and Treatment (PHPT-3) non-inferiority randomized clinical trial compared a treatment switching strategy based on CD4-only (CD4) monitoring versus viral-load (VL). Consenting participants were antiretroviral-naïve HIV-infected adults (CD4 count 50–250/mm3) initiating non-nucleotide reverse transcriptase inhibitor (NNRTI)-based therapy. Randomization, stratified by site (21 public hospitals), was performed centrally after enrollment. Clinicians were unaware of the VL values of patients randomized to the CD4 arm. Participants switched to second-line combination with confirmed CD4 decline >30% from peak (within 200 cells from baseline) in the CD4 arm, or confirmed VL >400 copies/ml in the VL arm. Primary endpoint was clinical failure at 3 years, defined as death, new AIDS-defining event, or CD4 <50 cells/mm3. The 3-year Kaplan-Meier cumulative risks of clinical failure were compared for non-inferiority with a margin of 7.4%. In the intent to treat analysis, data were censored at the date of death or at last visit. The secondary endpoints were difference in future-drug-option (FDO) score, a measure of resistance profiles, virologic and immunologic responses, and the safety and tolerance of HAART. 716 participants were randomized, 356 to VL monitoring and 360 to CD4 monitoring. At 3 years, 319 participants (90%) in VL and 326 (91%) in CD4 were alive and on follow-up. The cumulative risk of clinical failure was 8.0% (95% CI 5.6–11.4) in VL versus 7.4% (5.1–10.7) in CD4, and the upper-limit of the one-sided 95% CI of the difference was 3.4%, meeting the pre-determined non-inferiority criterion. Probability of switch for study criteria was 5.2% (3.2–8.4) in VL versus 7.5% (5.0–11.1) in CD4 (p = 0.097). Median time from treatment initiation to switch was 11.7 months (7.7–19.4) in VL and 24.7 months (15.9–35.0) in CD4 (p = 0.001). The median duration of viremia >400 copies/ml at switch was 7.2 months (5.8–8.0) in VL versus 15.8 months (8.5–20.4) in CD4 (p = 0.002). FDO scores were not significantly different at time of switch. No adverse events related to the monitoring strategy were reported.
Conclusions:
The 3-year rates of clinical failure and loss of treatment options did not differ between strategies although the longer-term consequences of CD4 monitoring would need to be investigated. These results provide reassurance to treatment programs currently based on CD4 monitoring as VL measurement becomes more affordable and feasible in resource-limited settings.
Trial registration:
ClinicalTrials.gov NCT00162682
Please see later in the article for the Editors' Summary
Vyšlo v časopise:
Switching HIV Treatment in Adults Based on CD4 Count Versus Viral Load Monitoring: A Randomized, Non-Inferiority Trial in Thailand. PLoS Med 10(8): e32767. doi:10.1371/journal.pmed.1001494
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pmed.1001494
Souhrn
Background:
Viral load (VL) is recommended for monitoring the response to highly active antiretroviral therapy (HAART) but is not routinely available in most low- and middle-income countries. The purpose of the study was to determine whether a CD4-based monitoring and switching strategy would provide a similar clinical outcome compared to the standard VL-based strategy in Thailand.
Methods and Findings:
The Programs for HIV Prevention and Treatment (PHPT-3) non-inferiority randomized clinical trial compared a treatment switching strategy based on CD4-only (CD4) monitoring versus viral-load (VL). Consenting participants were antiretroviral-naïve HIV-infected adults (CD4 count 50–250/mm3) initiating non-nucleotide reverse transcriptase inhibitor (NNRTI)-based therapy. Randomization, stratified by site (21 public hospitals), was performed centrally after enrollment. Clinicians were unaware of the VL values of patients randomized to the CD4 arm. Participants switched to second-line combination with confirmed CD4 decline >30% from peak (within 200 cells from baseline) in the CD4 arm, or confirmed VL >400 copies/ml in the VL arm. Primary endpoint was clinical failure at 3 years, defined as death, new AIDS-defining event, or CD4 <50 cells/mm3. The 3-year Kaplan-Meier cumulative risks of clinical failure were compared for non-inferiority with a margin of 7.4%. In the intent to treat analysis, data were censored at the date of death or at last visit. The secondary endpoints were difference in future-drug-option (FDO) score, a measure of resistance profiles, virologic and immunologic responses, and the safety and tolerance of HAART. 716 participants were randomized, 356 to VL monitoring and 360 to CD4 monitoring. At 3 years, 319 participants (90%) in VL and 326 (91%) in CD4 were alive and on follow-up. The cumulative risk of clinical failure was 8.0% (95% CI 5.6–11.4) in VL versus 7.4% (5.1–10.7) in CD4, and the upper-limit of the one-sided 95% CI of the difference was 3.4%, meeting the pre-determined non-inferiority criterion. Probability of switch for study criteria was 5.2% (3.2–8.4) in VL versus 7.5% (5.0–11.1) in CD4 (p = 0.097). Median time from treatment initiation to switch was 11.7 months (7.7–19.4) in VL and 24.7 months (15.9–35.0) in CD4 (p = 0.001). The median duration of viremia >400 copies/ml at switch was 7.2 months (5.8–8.0) in VL versus 15.8 months (8.5–20.4) in CD4 (p = 0.002). FDO scores were not significantly different at time of switch. No adverse events related to the monitoring strategy were reported.
Conclusions:
The 3-year rates of clinical failure and loss of treatment options did not differ between strategies although the longer-term consequences of CD4 monitoring would need to be investigated. These results provide reassurance to treatment programs currently based on CD4 monitoring as VL measurement becomes more affordable and feasible in resource-limited settings.
Trial registration:
ClinicalTrials.gov NCT00162682
Please see later in the article for the Editors' Summary
Zdroje
1. EggerM, MayM, CheneG, PhillipsAN, LedergerberB, et al. (2002) Prognosis of HIV-1-infected patients starting highly active antiretroviral therapy: a collaborative analysis of prospective studies. Lancet 360: 119–129.
2. ThompsonMA, AbergJA, CahnP, MontanerJS, RizzardiniG, et al. (2010) Antiretroviral treatment of adult HIV infection: 2010 recommendations of the International AIDS Society-USA panel. JAMA 304: 321–333.
3. ART-CC (2008) Life expectancy of individuals on combination antiretroviral therapy in high-income countries: a collaborative analysis of 14 cohort studies. Lancet 372: 293–299.
4. Panel on Antiretroviral Guidelines for Adults and Adolescents (2011). Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents. Washington (D.C.): Department of Health and Human Services. October 14, 2011. pp. 1–167.
5. World Health Organization (2013). Consolidated guidelines on the use of antiretroviral drugs for treating and preventing HIV infection. Recommendations for a public health approach. June 2013. Geneva: WHO.
6. KoenigSP, KuritzkesDR, HirschMS, LeandreF, MukherjeeJS, et al. (2006) Monitoring HIV treatment in developing countries. BMJ 332: 602–604.
7. KentDM, McGrathD, IoannidisJP, BennishML (2003) Suitable monitoring approaches to antiretroviral therapy in resource-poor settings: setting the research agenda. Clin Infect Dis 37: S13–24.
8. KumarasamyN, FlaniganTP, MahajanAP, CarpenterCC, MayerKH, et al. (2002) Monitoring HIV treatment in the developing world. Lancet Infect Dis 2: 656–657.
9. LaurentC, KouanfackC, Laborde-BalenG, AghokengAF, MbouguaJB, et al. (2011) Monitoring of HIV viral loads, CD4 cell counts, and clinical assessments versus clinical monitoring alone for antiretroviral therapy in rural district hospitals in Cameroon (Stratall ANRS 12110/ESTHER): a randomised non-inferiority trial. Lancet Infect Dis 11: 825–833.
10. MerminJ, EkwaruJP, WereW, DegermanR, BunnellR, et al. (2011) Utility of routine viral load, CD4 cell count, and clinical monitoring among adults with HIV receiving antiretroviral therapy in Uganda: randomised trial. BMJ 343: d6792.
11. MugyenyiP, WalkerAS, HakimJ, MunderiP, GibbDM, et al. (2010) Routine versus clinically driven laboratory monitoring of HIV antiretroviral therapy in Africa (DART): a randomised non-inferiority trial. Lancet 375: 123–131.
12. World Health Organization (2002) Scaling up antiretroviral therapy in resource-limited settings. Guidelines for a public health approach. Geneva: World Health Organization.
13. Division of AIDS table for grading the severity of adult and pediatric adverse events Version 1.0, December, 2004; clarification August 2009. Available: http://rsc.tech-res.com/Document/safetyandpharmacovigilance/Table_for_Grading_Severity_of_Adult_Pediatric_Adverse_Events.pdf. Accessed 8 February 2012.
14. JiangH, DeeksSG, KuritzkesDR, LallemantM, KatzensteinD, et al. (2003) Assessing resistance costs of antiretroviral therapies via measures of future drug options. J Infect Dis 188: 1001–1008.
15. GrabarS, Le MoingV, GoujardC, LeportC, KazatchkineMD, et al. (2000) Clinical outcome of patients with HIV-1 infection according to immunologic and virologic response after 6 months of highly active antiretroviral therapy. Ann Intern Med 133: 401–410.
16. HoggRS, YipB, ChanKJ, WoodE, CraibKJ, et al. (2001) Rates of disease progression by baseline CD4 cell count and viral load after initiating triple-drug therapy. JAMA 286: 2568–2577.
17. JunghansC, LowN, ChanP, WitschiA, VernazzaP, et al. (1999) Uniform risk of clinical progression despite differences in utilization of highly active antiretroviral therapy: Swiss HIV Cohort Study. AIDS 13: 2547–2554.
18. SterlingTR, ChaissonRE, MooreRD (2001) HIV-1 RNA, CD4 T-lymphocytes, and clinical response to highly active antiretroviral therapy. AIDS 15: 2251–2257.
19. Saag M, Westfall A, Luhanga D, Mulenga P, Chi B, et al.. (2012) A cluster randomized trial of routine vs discretionary viral load monitoring among adults starting ART: Zambia. Abstract 87. 19th Conference on Retroviruses and Opportunistic Infections. Seattle (Washington), US.
20. KeiserO, ChiBH, GsponerT, BoulleA, OrrellC, et al. (2011) Outcomes of antiretroviral treatment in programmes with and without routine viral load monitoring in Southern Africa. AIDS 25: 1761–1769.
21. RiddlerSA, JiangH, TenorioA, HuangH, KuritzkesDR, et al. (2007) A randomized study of antiviral medication switch at lower- versus higher-switch thresholds: AIDS Clinical Trials Group Study A5115. Antivir Ther 12: 531–541.
22. TenorioAR, JiangH, ZhengY, BastowB, KuritzkesDR, et al. (2009) Delaying a treatment switch in antiretroviral-treated HIV type 1-infected patients with detectable drug-resistant viremia does not have a profound effect on immune parameters: AIDS Clinical Trials Group Study A5115. AIDS Res Hum Retroviruses 25: 135–139.
23. ZhangS, van SighemA, KesselringA, GrasL, SmitC, et al. (2012) Episodes of HIV viremia and the risk of non-AIDS diseases in patients on suppressive antiretroviral therapy. J Acquir Immune Defic Syndr 60: 265–272.
24. El-SadrWM, LundgrenJD, NeatonJD, GordinF, AbramsD, et al. (2006) CD4+ count-guided interruption of antiretroviral treatment. N Engl J Med 355: 2283–2296.
25. KullerLH, TracyR, BellosoW, De WitS, DrummondF, et al. (2008) Inflammatory and coagulation biomarkers and mortality in patients with HIV infection. PLoS Med 5: e203 doi:10.1371/journal.pmed.0050203
26. KeiserO, MacPhailP, BoulleA, WoodR, SchechterM, et al. (2009) Accuracy of WHO CD4 cell count criteria for virological failure of antiretroviral therapy. Trop Med Int Health 14: 1220–1225.
27. MeeP, FieldingKL, CharalambousS, ChurchyardGJ, GrantAD (2008) Evaluation of the WHO criteria for antiretroviral treatment failure among adults in South Africa. AIDS 22: 1971–1977.
28. RawizzaHE, ChaplinB, MeloniST, EisenG, RaoT, et al. (2011) Immunologic criteria are poor predictors of virologic outcome: implications for HIV treatment monitoring in resource-limited settings. Clin Infect Dis 53: 1283–1290.
29. WalenskyRP, CiaranelloAL, ParkJE, FreedbergKA (2010) Cost-effectiveness of laboratory monitoring in sub-Saharan Africa: a review of the current literature. Clin Infect Dis 51: 85–92.
30. Medina LaraA, KigoziJ, AmurwonJ, MuchabaiwaL, Nyanzi WakaholiB, et al. (2012) Cost effectiveness analysis of clinically driven versus routine laboratory monitoring of antiretroviral therapy in Uganda and Zimbabwe. PLoS ONE 7: e33672 doi:10.1371/journal.pone.0033672
31. KahnJG, MarseilleE, MooreD, BunnellR, WereW, et al. (2011) CD4 cell count and viral load monitoring in patients undergoing antiretroviral therapy in Uganda: cost effectiveness study. BMJ 343: d6884.
32. BraithwaiteRS, NuciforaKA, YiannoutsosCT, MusickB, KimaiyoS, et al. (2011) Alternative antiretroviral monitoring strategies for HIV-infected patients in east Africa: opportunities to save more lives? J Int AIDS Soc 14: 38.
33. CohenMS, ChenYQ, McCauleyM, GambleT, HosseinipourMC, et al. (2011) Prevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med 365: 493–505.
34. KimmelAD, WeinsteinMC, AnglaretX, GoldieSJ, LosinaE, et al. (2010) Laboratory monitoring to guide switching antiretroviral therapy in resource-limited settings: clinical benefits and cost-effectiveness. J Acquir Immune Defic Syndr 54: 258–268.
35. PhillipsAN, GilksC, LundgrenJD (2009) Cost-effectiveness of strategies for monitoring the response to antiretroviral therapy in resource-limited settings. Arch Intern Med 169: 904; author reply 904–905.
36. Keiser O, Estill J, Egger M (2012) Viral load versus CD4 monitoring - impact on mortality, transmission and cost-effectiveness. MSF-UNITAID Co-Hosted Satellite Event, XIX International AIDS Conference. Washington (District of Columbia).
37. Murtagh M (2011) HIV/AIDS Diagnostic Landscape. UNITAID technical report. Geneva: UNITAID.
Štítky
Interné lekárstvoČlánok vyšiel v časopise
PLOS Medicine
2013 Číslo 8
- Statiny indukovaná myopatie: Jak na diferenciální diagnostiku?
- MUDr. Dana Vondráčková: Hepatopatie sú pri liečbe metamizolom väčším strašiakom ako agranulocytóza
- Vztah mezi statiny a rizikem vzniku nádorových onemocnění − metaanalýza
- Nech brouka žít… Ať žije astma!
- Parazitičtí červi v terapii Crohnovy choroby a dalších zánětlivých autoimunitních onemocnění
Najčítanejšie v tomto čísle
- Switching HIV Treatment in Adults Based on CD4 Count Versus Viral Load Monitoring: A Randomized, Non-Inferiority Trial in Thailand
- Risk of Early-Onset Neonatal Infection with Maternal Infection or Colonization: A Global Systematic Review and Meta-Analysis
- Inclusion of Ethical Issues in Dementia Guidelines: A Thematic Text Analysis
- Country Contextualization of the Mental Health Gap Action Programme Intervention Guide: A Case Study from Nigeria