Share this article on:

Patterns of Adherence to Raltegravir-Based Regimens and the Risk of Virological Failure Among HIV-Infected Patients: The RALTECAPS Cohort Study

Gras, Guillaume MD*; Schneider, Marie-Paule PharmD, PhD; Cavassini, Matthias MD; Lucht, Frédéric MD§; Loilier, Magalie PharmD, PhD; Verdon, Renaud MD, PhD; Bernard, Louis MD, PhD*; Parienti, Jean-Jacques MD, PhD¶,#

JAIDS Journal of Acquired Immune Deficiency Syndromes: 1 November 2012 - Volume 61 - Issue 3 - p 265–269
doi: 10.1097/QAI.0b013e31826cc592
Rapid Communication

Abstract: Adherence patterns and their influence on virologic outcome are well characterized for protease inhibitor (PI)- and non-nucleoside reverse transcriptase inhibitor (NNRTI)–based regimens. We aimed to determine how patterns of adherence to raltegravir influence the risk of virological failure. We conducted a prospective multicenter cohort following 81 HIV-infected antiretroviral-naive or experienced subjects receiving or starting twice-a-day raltegravir-based antiretroviral therapy. Their adherence patterns were monitored using the Medication Events Monitoring System. During follow-up (188 days, ±77), 12 (15%) of 81 subjects experienced virological failure. Longer treatment interruption [adjusted odds ratio per 24-hour increase: 2.4; 95% confidence interval: 1.2 to 6.9; P < 0.02] and average adherence (odds ratio per 5% increase: 0.68; 95% confidence interval: 0.46 to 1.00, P < 0.05) were both independently associated with virological failure controlling for prior duration of viral suppression. Timely interdose intervals and high levels of adherence to raltegravir are both necessary to control HIV replication.

*Department of Infectious Diseases, Centre Hospitalier Universitaire, Tours, France

Community Pharmacy, Department of Ambulatory Care & Community Medicine, University of Lausanne, Lausanne, Switzerland

Infectious Diseases Service, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland

§Department of Infectious Diseases, Centre Hospitalier Universitaire, Saint-Etienne, France

Department of Pharmacology, Centre Hospitalier Universitaire, Caen, France

Infectious Diseases Service, Centre Hospitalier Universitaire, Caen, France

#Department of Biostatistics and Clinical Research, Centre Hospitalier Universitaire, Caen, France.

Correspondence to: Jean-Jacques Parienti, MD, PhD, Department of Biostatistics and Clinical Research, Centre Hospitalier Universitaire, Avenue de la Côte de Nacre, 14000 Caen, France (e-mail:

Supported by academic research grant from the Caen University Hospital, France.

Presented in part at the 6th International Conference on HIV Treatment and Prevention Adherence, May 22–24, 2011, Miami, Florida and 6th International AIDS Society Conference on HIV Pathogenesis and Prevention, July 17–20, 2011, Rome, Italy.

The authors have no funding or conflicts of interest to disclose.

RALTECAPS Study Group: V. Laplantine, A. Chaillon, F. Bastides, F. Barin, A. Martin, V. Noyon, S. Dargere, A. de la Blanchardière, P. Goubin, P. Feret, A.C. Maelle Detoc, J. Dina, A. Vabret, J.-J. Dutheil, F. Chaillot, D. Alves, O. Bugnon. This study has been supported in part, by the Swiss HIV Cohort Study. The members of the Swiss HIV Cohort Study are J. Barth, M. Battegay, E. Bernasconi, J. Böni, HC. Bucher, C. Burton-Jeangros, A. Calmy, M. Cavassini, C. Cellerai, M. Egger, L. Elzi, J. Fehr, J. Fellay, M. Flepp, P. Francioli (President of the SHCS), H. Furrer (Chairman of the Clinical and Laboratory Committee), CA. Fux, M. Gorgievski, H. Günthard (Chairman of the Scientific Board), D. Haerry (deputy of “Positive Council”), B. Hasse, HH. Hirsch, B. Hirschel, I. Hösli, C. Kahlert, L. Kaiser, O. Keiser, C. Kind, T. Klimkait, H. Kovari, B. Ledergerber, G. Martinetti, B. Martinez de Tejada, K. Metzner, N. Müller, D. Nadal, G. Pantaleo, A. Rauch, S. Regenass, M. Rickenbach (Head of Data Center), C. Rudin (Chairman of the Mother and Child Substudy), P. Schmid, D. Schultze, F. Schöni-Affolter, J. Schüpbach, R. Speck, P. Taffé, P. Tarr, A. Telenti, A. Trkola, P. Vernazza, R. Weber, and S. Yerly.

Received May 25, 2012

Accepted July 31, 2012

Treatment interruptions are an important component of medication-taking behavior in HIV-infected patients receiving antiretroviral (ARV) therapy.1 Although the potency of the available ARV medications has increased over the years, adherence and persistence remain the keys to treatment success.2 Most of the current understanding about the impact of adherence patterns on virological failure has come from studies of protease inhibitor, non–nucleoside reverse transcriptase inhibitor (NNRTI), and boosted PI-based regimens.3–9 Raltegravir is the first approved drug in the integrase inhibitor class of the ARV armamentarium. Raltegravir is a potent and safe drug, but a single mutation can lead to resistance.10 Such a low genetic barrier to resistance can have significant effects on treatment: a treatment interruption of 15 days was associated with a 50% probability of virological failure among HIV-infected subjects on an NNRTI-based regimen.11

In this study, we identified patterns of adherence to raltegravir that are associated with virological failure and examined the impact of adherence on the development of ARV resistance. Our specific objective was to predict the risk of virological failure as a function of the duration of treatment interruption. We hypothesized that short treatment interruptions with raltegravir would be associated with virological failure.

Back to Top | Article Outline


Study Design and Population

We conducted a multicenter prospective observational cohort study of HIV-infected patients treated with a raltegravir-based regimen, the RALTECAPS study [RALTEgravir monitored by Medication Event Monitoring System (MEMS) CAPS]. We selected patients receiving or starting twice-a-day raltegravir-based regimens between April 2010 and February 2011. Adherence to raltegravir was monitored in several outpatient clinics: 3 in France (the Caen, Tours, and Saint Etienne University Hospitals) and one in Switzerland (the Lausanne University Hospital). ARV-naive or treatment-experienced patients starting raltegravir-based regimens were eligible. All included subjects had a genotypic sensitivity score of 3 or more, including raltegravir. The genotypic sensitivity score represents the total number of ARV drugs in the regimen to which a patient's HIV was susceptible (score, 1), possibly susceptible (score, 0.5), or resistant (score, 0), according to version 16 of the ANRS algorithm (

The Institutional Review Board of the University of Caen, France (which covers all French sites) and the Committee on Human Subjects Research of the University of Lausanne, Switzerland approved all study procedures, and the patients provided written informed consent.

Back to Top | Article Outline

Endpoint Definition and Data Collection

Patients were sampled at baseline and every 3 months. Virological failure was defined as 2 consecutive plasma HIV RNA levels >40 copies/mL or one HIV RNA level >400 copies/mL during the 6-month follow-up period for subjects with undetectable HIV RNA at baseline, and HIV RNA level >40 copies/mL at 6 months for subjects with detectable HIV RNA at baseline. Adherence was prospectively measured using MEMS (Aardex) CAPS. In addition, MEMS use was confirmed by measuring the raltegravir plasma levels, and unmonitored periods or pocket doses were monitored by self-report questionnaires in all centers. Because we were interested in assessing dose timing for a rather long period, self-report adherence questionnaires were not performed. Steady-state plasma levels, collected at least 3 months after starting raltegravir, were measured in a central laboratory at the end of the study for the French centers. MEMS caps measure patterns of missed doses with a time and date record of pill bottle opening behavior. Based on our previous work,11 we characterized patterns of missed doses using several a priori measures: (1) the average percent dose adherence; (2) the number of treatment interruptions lasting >48 hours; and (3) the duration of the longest treatment interruption (in days). Persistence was defined as remaining on raltegravir at the end of the follow-up period. Other data, including duration of viral suppression before enrollment, the use of other ARV treatments, CD4 cell count, and viral load, were collected prospectively. Genotypic resistance testing was performed in cases of virological failure. We chose version 16 of the ANRS algorithm to define raltegravir resistance as the presence of one major mutation (T66K, E92Q, F121Y, G140A/S, Y143C/G/H/R/S, Q148E/G/H/K/R, V151 L, N155H/S/T, and E157Q) or at least 3 minor mutations (V72I, L74 M, T97A, V151I, G163R, and S230R). Emergence of resistance to raltegravir was investigated by genotyping the integrase coding sequence of the virus after the development of virological failure. Consensus amino acid sequences for the integrase gene were compared with sequences obtained before study entry.

Back to Top | Article Outline

Statistical Analysis

Continuous variables were summarized as the mean values, median values, SDs, and interquartile ranges (IQRs) depending on their distributions. Dichotomous data were summarized as proportions. Quantitative variables were compared between groups with and without virological failure using the exact Wilcoxon rank sum nonparametric test. Qualitative variables were compared using Fisher exact test. The effect of each adherence pattern on the probability of virological failure was estimated by calculating odds ratios (ORs) and their 95% confidence intervals (CIs) using univariate logistic regression models. We assessed the independence of predictors in the multivariate analysis adjusting for baseline risk factors. We conducted forward, backward, and stepwise model selections among all 4 potential adherence patterns, with a likelihood ratio test limit of P < 0.10 to enter and remain in the model. All these 3 automated variable selection methods led to a similar model. Analyses were performed using PowerView, version 2.3.3 (Aardex Group, Sion, Switzerland) and SAS, version 9.1 (SAS Institute, Cary, NC). All reported P values are 2-sided, and a P value of < 0.05 was considered to be statistically significant.

Back to Top | Article Outline


A total of 81 patients were included during the study period. Table 1 shows the baseline characteristics of the overall cohort. The mean age was 47.8 years, and 72% of the patients were male. Ten (12%) subjects were naive, and the majority of the patients (53%) received tenofovir plus emtricitabine in combination with raltegravir. Sixty (74%) patients had a plasma HIV RNA <40 copies/mL at study entry. The median baseline CD4 cell count was 468 (IQR, 289–658), and the mean baseline plasma HIV RNA level at study entry was 2.0 log10 (SD, 1.1). During a median follow-up of 188 days, representing 30,900 MEMS events (overall median average adherence: 97.4%), 12 of 81 (15%) subjects experienced virological failure. All subjects continued on raltegravir throughout the follow-up period.

Table 1 also presents baseline characteristics by virological outcome. Longer duration of HIV RNA suppression before inclusion in the study was significantly associated with subsequent viral suppression. Other baseline characteristics, such as demographics and treatment factors, were not significantly associated with our primary endpoint.

All adherence measurements were significantly associated with virological failure in the univariate analysis, as shown in Table 1. Raltegravir plasma levels were not significantly associated with virological failure. In the multivariate analysis, the duration of treatment interruption [adjusted OR (aOR) per 24-hour increase: 2.4, 95% CI: (1.2 to 6.9), P < 0.02] and average adherence (aOR per 5% increase 0.68, 95% CI: 0.46 to 1.00, P < 0.05) were both independently associated with virological failure when controlling for the prior duration of viral suppression (aOR per 12 additional months 0.37, 95% CI: 0.12 to 1.09, P = 0.07).

Figure 1 depicts the sigmoid logistic curve of the empirically estimated risk of virological control as a function of the longest raltegravir treatment interruption. The probability of virological failure was 50% after 7 days of consecutive raltegravir interruption.

Most patients presented with low-level virological failure with a median viral load of 2.81 log copies/mL (IQR, 1.94–3.08). Samples for 10 of the 12 patients reaching our primary endpoint of virological failure were subjected to nucleic acid amplification and sequencing. Six minor mutations (V72I) were detected in the HIV-1 integrase gene, but those mutations had already been detected before raltegravir treatment. Two new major (Q148H/G140S and Q148R) mutations were found: the former was found in a naive patient starting emtricitabine–tenofovir plus raltegravir, and the latter was found in an experienced patient receiving etravirine, boosted-darunavir, maraviroc plus raltegravir.

Back to Top | Article Outline


In this cohort of HIV-infected patients, a high level of adherence to twice-a-day raltegravir was necessary to achieve or maintain virological control. Moreover, short-term raltegravir interruptions were associated with virological failure. This study is the first to identify adherence patterns associated with virological failure in a prospective cohort of unselected HIV-infected patients treated with raltegravir-based regimens. Overall, our results contrast with those we observed previously with NNRTIs and boosted PIs. NNRTIs were more susceptible to treatment interruptions,11 whereas boosted PIs were more susceptible to low average adherence.12 The level of forgiveness of raltegravir-based regimens seems low for both nonadherence patterns.

MEMS caps have been frequently used in research studies to measure adherence. However, adherence to medications can be underestimated if only MEMS devices are used. The best approach is most likely to combine several measurements.13 The use of self-report questionnaires and plasma drug dosage to validate proper MEMS use in our study increased the internal validity, although such intensive therapeutic drug monitoring is difficult to replicate outside the research setting for cost reasons.

In the sentinel study by Paterson et al,3 an acceptable level of adherence to therapy in HIV infection was defined as consumption of more than 95% of the prescribed doses, but treatment interruption patterns were not analyzed. This breakpoint required for unboosted PIs has been widely challenged by ARV drugs with longer half-lives, such as NNRTIs6,14 and, to a lesser extent, some boosted PIs.15 It was hypothesized that more reliable virological control with NNRTI therapy at modest levels of adherence may be the result of either improved potency or the extended half-life of NNRTIs.6 As expected, decreased average adherence to raltegravir was also an independent risk factor for virological failure. Gardner et al16 investigated factors associated with virological outcome in a cohort of 39 individuals receiving boosted PIs and raltegravir. Consistent with our current and previous findings,12 based on boosted PIs (OR: 3.3 per 10% increase in MEMS adherence; P < 0.001), the odds of virological failure increased as raltegravir adherence decreased (OR: 1.9 per 10% decrease in pharmacy refill adherence; P = 0.03).16 However, the methods used to measure adherence could not capture the pattern of adherence.

High average levels of adherence were not sufficient. Indeed, short treatment interruptions were independently associated with virological failure when controlling for the average level of adherence. The risk of virological failure was 50% after a 7-day interruption of raltegravir; in comparison, a 15-day period of interruption of NNRTIs is associated with similar risk of virological failure. Of note, raltegravir has been proven to be noninferior to efavirenz in a large randomized controlled trial.17 Therefore, lower potency cannot explain these differences in virological outcomes between raltegravir and efavirenz. Raltegravir not only has a shorter plasma half-life compared to efavirenz but it also has extremely high inter-individual variability.18 Consequently, even short-term raltegravir interruptions could lead to ineffective intracellular concentrations in some patients, even with high levels of average adherence. The association of low plasma levels with virological failure on once-daily raltegravir regimens has been demonstrated in the QDMRK trial.19 In our study, we could not demonstrate that patients with virological failure had lower plasma drug levels. However, plasma levels were not systematically measured at the time of virological failure. Moreover, we did not collect important information such food intake, which affects drug levels.20 Drug levels are subject to short-term changes in adherence and do not necessarily reflect past decrease in adherence.

The risk of major resistance after virological failure was low: 2 (16%) of 12. Although raltegravir has a low genetic barrier to resistance, the risk of developing genotypic mutations with partial adherence also depends on the replicative cost for the mutated HIV strain,21 that is, viral fitness. In addition, more than half of the subjects received a nucleoside backbone containing emtricitabine–tenofovir. The long intracellular half-life of the other drugs combined with raltegravir in these regimens may have helped to limit differential exposure, functional raltegravir monotherapy, and, therefore, resistance.22 As was shown by Bangsberg et al23 for unboosted PIs, genotypic resistance to raltegravir may occur at higher levels of average adherence to allow mutated HIV strains to compete with wild-type strains in the setting of subtherapeutic raltegravir concentrations. In addition, detection of mutations conferring resistance to raltegravir may be difficult at low levels of virological failure,24 such as in our cohort.

There are several limitations to this study. First, the sample size was small. Therefore, some factors may not be significant as a consequence of poor statistical power. Second, other ARV drugs used in combination with raltegravir were not standardized. By including a diverse patient population, we meant to have a cohort that was representative of raltegravir use in clinical practice. Third, only adherence to raltegravir was monitored. However, when patients skip or are off schedule with doses, they generally skip or are off schedule with all the ARV medications taken at that time.25 Finally, the 6-month follow-up period was rather short. However, the effect of adherence on outcome seems to be attenuated as duration of viral control increases.26,27 Therefore, adherence patterns during the first months are critical.

The results of this study may have several implications for practice and research. Raltegravir is a well-tolerated and highly effective drug.17,19,28,29 For example, the STARTMRK double-blind randomized study demonstrated a durable virological suppression with raltegravir that was equivalent to efavirenz through 156 weeks of therapy.30 Nevertheless, raltegravir may be highly susceptible to various nonadherence behaviors such as short treatment interruptions and moderate levels of adherence. These patterns are more common in clinical practice31,32 than in clinical research. In addition, double-blind trials30 may not capture the significant benefit of once-a-day versus twice-a-day regimen on adherence (+4.4%; 95% CI: 1.8% to 7.0%; P < 0.001) and virological outcome (+5.7%; 95% CI, 0.7% to 10.8%; P < 0.027) among naive HIV-infected subjects.33 Combinations of raltegravir with other classes and higher forgiveness of common non-adherence patterns may help to achieve sustained virological control among subjects with prior virological failure and resistance.34 New integrase inhibitors with longer half-lives are needed to allow once-a-day dosing and limit the influence of short-term treatment interruptions on virological failure.

Back to Top | Article Outline


1. Genberg BL, Wilson IB, Bangsberg DR, et al.. Patterns of adherence to antiretroviral therapy and impact on HIV-RNA among patients from the MACH14 study. AIDS. 2012;26:1415–1423.
2. Bae JW, Guyer W, Grimm K, et al.. Medication persistence in the treatment of HIV infection: a review of the literature and implications for future clinical care and research. AIDS. 2011;25:279–290.
3. Paterson DL, Swindells S, Mohr J, et al.. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med. 2000;133:21–30.
4. Gross R, Bilker WB, Friedman HM, et al.. Effect of adherence to newly initiated antiretroviral therapy on plasma viral load. AIDS. 2001;15:2109–2117.
5. Nieuwkerk PT, Sprangers MA, Burger DM, et al.. Limited patient adherence to highly active antiretroviral therapy for HIV-1 infection in an observational cohort study. Arch Intern Med. 2001;161:1962–1968.
6. Bangsberg DR. Less than 95% adherence to nonnucleoside reverse-transcriptase inhibitor therapy can lead to viral suppression. Clin Infect Dis. 2006;43:939–941.
7. Maggiolo F, Airoldi M, Kleinloog HD, et al.. Effect of adherence to HAART on virologic outcome and on the selection of resistance-conferring mutations in NNRTI- or PI-treated patients. HIV Clin Trials. 2007;8:282–292.
8. Martin M, Del Cacho E, Codina C, et al.. Relationship between adherence level, type of the antiretroviral regimen, and plasma HIV type 1 RNA viral load: a prospective cohort study. AIDS Res Hum Retroviruses. 2008;24:1263–1268.
9. Gardner EM, Burman WJ, Steiner JF, et al.. Antiretroviral medication adherence and the development of class-specific antiretroviral resistance. AIDS. 2009;23:1035–1046.
10. Malet I, Delelis O, Valantin MA, et al.. Mutations associated with failure of raltegravir treatment affect integrase sensitivity to the inhibitor in vitro. Antimicrob Agents Chemother. 2008;52:1351–1358.
11. Parienti JJ, Das-Douglas M, Massari V, et al.. Not all missed doses are the same: sustained NNRTI treatment interruptions predict HIV rebound at low-to-moderate adherence levels. PLoS One. 2008;3:e2783.
12. Parienti JJ, Ragland K, Lucht F, et al.. Average adherence to boosted protease inhibitor therapy, rather than the pattern of missed doses, as a predictor of HIV RNA replication. Clin Infect Dis. 2010;50:1192–1197.
13. Liu H, Golin CE, Miller LG, et al.. A comparison study of multiple measures of adherence to HIV protease inhibitors. Ann Intern Med. 2001;134:968–977.
14. Nachega JB, Hislop M, Dowdy DW, et al.. Adherence to nonnucleoside reverse transcriptase inhibitor-based HIV therapy and virologic outcomes. Ann Intern Med. 2007;146:564–573.
15. Nelson M, Girard PM, Demasi R, et al.. Suboptimal adherence to darunavir/ritonavir has minimal effect on efficacy compared with lopinavir/ritonavir in treatment-naive, HIV-infected patients: 96 week ARTEMIS data. J Antimicrob Chemother. 2010;65:1505–1509.
16. Gardner EM, Melendez AG, Astiz M, et al.. Adherence and preexisting major protease inhibitor resistance mutations are associated with virologic failure of a dual-class antiretroviral regimen with inhibitors of HIV-1 viral protease and integrase. J Int Assoc Physicians AIDS Care (Chic). 2011;11:34–39.
17. Lennox JL, DeJesus E, Lazzarin A, et al.. Safety and efficacy of raltegravir-based versus efavirenz-based combination therapy in treatment-naive patients with HIV-1 infection: a multicentre, double-blind randomised controlled trial. Lancet. 2009;374:796–806.
18. Fayet Mello A, Buclin T, Franc C, et al.. Cell disposition of raltegravir and newer antiretrovirals in HIV-infected patients: high inter-individual variability in raltegravir cellular penetration. J Antimicrob Chemother. 2011;66:1573–1581.
19. Eron JJ Jr, Rockstroh JK, Reynes J, et al.. Raltegravir once daily or twice daily in previously untreated patients with HIV-1: a randomised, active-controlled, phase 3 non-inferiority trial. Lancet Infect Dis. 2011;11:907–915.
20. Brainard DM, Friedman EJ, Jin B, et al.. Effect of low-, moderate-, and high-fat meals on raltegravir pharmacokinetics. J Clin Pharmacol. 2011;51:422–427.
21. Bangsberg DR, Acosta EP, Gupta R, et al.. Adherence-resistance relationships for protease and non-nucleoside reverse transcriptase inhibitors explained by virological fitness. AIDS. 2006;20:223–231.
22. Gardner EM, Sharma S, Peng G, et al.. Differential adherence to combination antiretroviral therapy is associated with virological failure with resistance. AIDS. 2008;22:75–82.
23. Bangsberg DR, Hecht FM, Charlebois ED, et al.. Adherence to protease inhibitors, HIV-1 viral load, and development of drug resistance in an indigent population. AIDS. 2000;14:357–366.
24. Gallien S, Delaugerre C, Charreau I, et al.. Emerging integrase inhibitor resistance mutations in raltegravir-treated HIV-1-infected patients with low-level viremia. AIDS. 2011;25:665–669.
25. Wilson IB, Tchetgen E, Spiegelman D. Patterns of adherence with antiretroviral medications: an examination of between-medication differences. J Acquir Immune Defic Syndr. 2001;28:259–263.
26. Rosenblum M, Deeks SG, van der Laan M, et al.. The risk of virologic failure decreases with duration of HIV suppression, at greater than 50% adherence to antiretroviral therapy. PLoS One. 2009;4:e7196.
27. Lima VD, Bangsberg DR, Harrigan PR, et al.. Risk of viral failure declines with duration of suppression on highly active antiretroviral therapy irrespective of adherence level. J Acquir Immune Defic Syndr. 2010;55:460–465.
28. Martinez E, Larrousse M, Llibre JM, et al.. Substitution of raltegravir for ritonavir-boosted protease inhibitors in HIV-infected patients: the SPIRAL study. AIDS. 2010;24:1697–1707.
29. Vispo E, Barreiro P, Maida I, et al.. Simplification from protease inhibitors to once- or twice-daily raltegravir: the ODIS trial. HIV Clin Trials. 2010;11:197–204.
30. Rockstroh JK, Lennox JL, Dejesus E, et al.. Long-term treatment with raltegravir or efavirenz combined with tenofovir/emtricitabine for treatment-naive human immunodeficiency virus-1-infected patients: 156-week results from STARTMRK. Clin Infect Dis. 2011;53:807–816.
31. Kranzer K, Ford N. Unstructured treatment interruption of antiretroviral therapy in clinical practice: a systematic review. Trop Med Int Health. 2011;16:1297–1313.
32. Ortego C, Huedo-Medina TB, Llorca J, et al.. Adherence to highly active antiretroviral therapy (HAART): a meta-analysis. AIDS Behav. 2011;15:1381–1396.
33. Parienti JJ, Bangsberg DR, Verdon R, et al.. Better adherence with once-daily antiretroviral regimens: a meta-analysis. Clin Infect Dis. 2009;48:484–488.
34. Yazdanpanah Y, Fagard C, Descamps D, et al.. High rate of virologic suppression with raltegravir plus etravirine and darunavir/ritonavir among treatment-experienced patients infected with multidrug-resistant HIV: results of the ANRS 139 TRIO trial. Clin Infect Dis. 2009;49:1441–1449.

adherence; integrase inhibitor; virological failure; antiretroviral therapy

© 2012 Lippincott Williams & Wilkins, Inc.