Although genotypic resistance testing (GRT) is recommended to guide combination antiretroviral therapy (cART), funding and/or facilities to perform GRT may not be available in low to middle income countries. Since treatment history (TH) impacts response to subsequent therapy, we investigated a set of statistical learning models to optimise cART in the absence of GRT information.The EuResist database was used to extract 8-week and 24-week treatment change episodes (TCE) with GRT and additional clinical, demographic and TH information. Random Forest (RF) classification was used to predict 8- and 24-week success, defined as undetectable HIV-1 RNA, comparing nested models including (i) GRT+TH and (ii) TH without GRT, using multiple cross-validation and area under the receiver operating characteristic curve (AUC). Virological success was achieved in 68.2% and 68.0% of TCE at 8- and 24-weeks , respectively. RF (i) and (ii) showed comparable performances, with an average (st.dev.) AUC 0.77 (0.031) vs. 0.757 (0.035) at 8-weeks, 0.834 (0.027) vs. 0.821 (0.025) at 24-weeks. Sensitivity analyses, carried out on a data subset that included antiretroviral regimens commonly used in low to middle income countries, confirmed our findings. Training on subtype B and validation on non-B isolates resulted in a decline of performance for models (i) and (ii).Treatment history-based RF prediction models are comparable to GRT-based for classification of virological outcome. These results may be relevant for therapy optimisation in areas where availability of GRT is limited. Further investigations are required in order to account for different demographics, subtypes and different therapy switching strategies. Current HIV-1 guidelines recommend genotypic resistance testing (GRT) both before starting antiretroviral therapy (ART) and at treatment failure. However, appropriate funding and/or facilities to perform GRT may not be available in low to middle income countries (LMIC), leaving physicians with switching therapy based solely on the patient's clinical background. Coupled with virological and immunological measurements, treatment history (TH) is one of the most crucial factors to play a role in the response to a new treatment regimen. In fact, current (2010) recommendations of the International AIDS Society-USA Panel In LMIC there is still need for cheap viral load tests to identify early viral failures and limit the emergence of resistance It has been shown that drug costs are a major factor impairing optimal HIV-1 drug sequencing Given the need for low-cost viral load assays, evaluations of alternative technologies have been carried out The virological benefit of genotype-guided treatment decisions has been convincingly demonstrated in the last decade In this work we aimed at investigating a set of statistical learning models – called random forests - for optimisation of antiretroviral therapy in the absence of GRT information, using the treatment history as a surrogate predictor. Furthermore, we compare the performance of TH-based to those of GRT-based models.This study uses anonymous retrospective data from European merged study cohorts and biological material was not employed in any step of the analysis. All the single data providers had previously obtained patients' informed consent for the execution of retrospective studies and their inclusion in merged cohorts, accomplishing national and international ethical issues. The study design was eventually approved by a global scientific committee and by local scientific committees of each single data provider.http://www.euresist.org/) was used to extract patients' treatment change episodes (TCE), previously introduced by the HIV Resistance Response Database Initiative The EuResist retrospective database (Each SD was defined as a patient's new regimen (TCE), either a first-line or a subsequent line of therapy, coupled with a follow-up HIV-RNA measured at 8 weeks (ranging from 4 to 12) and at 24 weeks (ranging from 20 to 28) of unmodified therapy.TCE included antiretroviral compounds approved by the Food and Drug Administration and by the European Medicine Agency: the nucleoside/nucleotide reverse transcriptase inhibitors (NRTI) lamivudine, abacavir, zidovudine, stavudine, zalcitabine, didanosine, emtricitabine and tenofovir disoproxil fumarate; the non-NRTIs (NNRTI) efavirenz, nevirapine and etravirine; the protease inhibitors (PI) amprenavir/fosamprenavir, atazanavir, indinavir, lopinavir, nelfnavir, full-dose ritonavir, boosting-dose ritonavir, saquinavir, tipranavir and darunavir; the fusion inhibitor enfuvirtide. Each drug was represented by a binary variable encoding its presence/absence in a TCE. No restrictions concerning the number of drugs included in a regimen were applied. Both suboptimal treatment regimens made of <3 drugs or salvage regimes with >4 drugs were included. It was previously shown that the inclusion of suboptimal TCE can improve data-driven model performance http://www.hiv.lanl.gov/).Each TCE was provided with a contemporary HIV-1 genotype (spanning protease and reverse transcriptase genes) or the closest within 90 days before the TCE start date. The viral genotype was encoded as binary vector of mutations, insertions and deletions with respect to the HIV-1 consensus B reference, and the viral subtype was determined by a BLAST search on the latest subtype reference set provided by the Los Alamos repository (The closest (e.g. baseline) HIV-RNA and CD4+/CD4% cell count measurements previous to the TCE start date  were collected. Patient's demographic information was also retrieved, including age, gender, mode of HIV transmission , and country of origin.We associated to each TCE the previous patient's drug exposures, codifying a binary variable for each compound if that drug was experienced for more than 12 months. Three additional binary variables were defined summarising the exposures to the NRTI/NNRTI/PI classes, and a numerical variable representing the regimen line (any drug change in a combination therapy for any reason).10 from the baseline HIV-RNA. At 24-weeks, the virological success was defined as the achievement of an undetectable viral load. Because of the inclusion of many viral load data obtained with non-ultrasensitive assays, a 500 cp/ml threshold was applied for the definition of undetectable HIV-RNA.We defined a virological success at 8-weeks, as the achievement of an undetectable HIV-RNA or a decrease of at least 2 LogAdditional details for the study design, the SD definition and data extraction procedures and constraints have been discussed previously http://www.virolab.org/), comprising the cohorts of Belgium (Katholieke Universiteit Leuven) and Spain (fundaciò irsiCaixa). These two sets, containing treatment history but not mandatorily baseline GRT, were named SD8H and SD24H, respectively.Two other data sets were extracted using the same notion of SD, except for relaxing the need of a baseline GRT, and including additional data from the Virolab study group (Random Forests (RF) were used to predict 8- and 24-week outcomes and to assess variable importance In our study, the tree number and number of candidate variables at each split of RF were optimised preliminarily with a bootstrap approach. The area under the receiver operating characteristic curve (AUC) The following nested models were defined: (i) full set of input covariates (including GRT and TH); (ii) full set of input covariates, excluding GRT information, but keeping TH; (iii) full set of input covariates, excluding TH, but keeping GRT; (iv) current cART, baseline HIV-1 RNA and CD4; (v) current cART.We also designed a set of sensitivity analyses as follows: in order to investigate the potential bias derived from the fact that most of the TCE present in the data base have been decided after a GRT, we excluded from SD8H/SD24H all TCEs for which a baseline GRT was available, and we compared models with/without baseline HIV-RNA load as a covariate. From this reduced data set, we selected first- and second-line regimens commonly available in LMIC  and repeated model evaluations. Finally, SD8G was split into two sets according to the viral subtype of each associated GRT, grouping B and non-B subtypes. RF models were trained on the subtype B dataset and tested against the non-B.R, open-source software for statistical computing Weka, a data-mining suite The analyses were carried out using From the EuResist data base, we collected 2,831 and 2,579 standard datum instances for the 8-week (SD8G) and 24-week (SD24G) outcome, respectively. Virological success was reported in 68.2% and 68.0% of cases, respectively. Suboptimal therapies , had a prevalence of 6.2% and 5.5% in the 8-week and 24-week data set. The majority of cART contained lamivudine (55.9%), any PI/r , tenofovir (34.8%), zidovudine (31.7%), didanosine (28.4%), stavudine (24.0%), efavirenz (17.5%), abacavir (16.0%), and nevirapine (10.6%). Darunavir, tipranavir, etravirine, enfuvirtide, raltegravir, and maraviroc were not present in the data set. Additional details on cART distribution are available in Relaxing the need for a baseline GRT, the data sets SD8H and SD24H were extracted . Seventy-one percent of patients reached virological success at 8-weeks and 67% of patients reached virological success at 24-weeks. Differences in proportions of SD8H and SD24H with SD8G and SD24G success rates yielded p<0.0001 and p = 0.13, respectively. The percentage of suboptimal therapies was 8%. The majority of cART contained lamivudine (63%), zidovudine (33%), tenofovir (28%), stavudine (27%), any PI/r , didanosine (21%), abacavir (19%), and efavirenz (11%). There was a low percentage of darunavir (1.3%) and tipranavir (1.3%) containing cART.We also compared AUC of models (i), (ii), and (v) by stratifying for the therapy line . Detailed results are shown in When executing CV on SD8H and SD24H, only RF models (ii), (iv) and (v) were testable (since GRT information was not present). Average (st.dev.) AUC values for model (ii) were 0.799 (0.011) at 8-weeks, and 0.832 (0.009) at 24 weeks. Model (iv) had average (st.dev.) AUC of 0.742 (0.012) at 8-weeks, and 0.752 (0.012) at 24 weeks. Model (v) had an average AUC of 0.684 (0.014) and 0.714 (0.013) at 8- and 24-weeks, respectively. Differences in AUC between (ii) and (iv) were significant both at 8-weeks and at 24-weeks (p<0.0001). As expected, the difference was even larger between model (ii) and (v) (p<0.0001 both at 8- and 24-weeks).Since all the extracted data sets may be biased due to the fact that in Europe the prevalence of genotype-guided TCE is high, we excluded from SD8H and SD24H all the TCE for which there was a baseline GRT available . This is meant to be an approximation to the set of non-genotype-guided therapies, although some baseline GRT may have been available but not inserted in the data base. In a sub-analysis, then, we deleted the HIV-RNA variable. The proportion of successes was 71% and 65% at 8- and 24-weeks, respectively. For the 8-weeks outcome, average (st.dev.) AUC for models (ii), (iv), and (v) were 0.809 (0.015), 0.753 (0.015), and 0.685 (0.017). For the 24-weeks outcome, average (st.dev.) AUC for model (ii), (iv), and (v) were 0.839 (0.013), 0.765 (0.014), and 0.709 (0.018). Model (ii) significantly outperformed both models (iv) and (v) at all time points (p<0.0001). When removing the HIV-RNA variable, for 8-weeks outcome the average (st.dev.) AUC for models (ii) and (iv) were 0.758 (0.015) and 0.654 (0.016), p<0.0001; for 24-weeks outcome, the average (st.dev.) AUC for models (ii) and (iv) were 0.807 (0.015) and 0.670 (0.014), p<0.0001. Notably, model (ii) without baseline HIV-RNA outperformed model (iv) with baseline HIV-RNA at 24-weeks (p<0.0001), but not at 8-weeks (p = 0.4387).Models (ii), (iv) and (v) were also tested on SD8H and SD24H excluding not only GRT-guided TCE, but also restricting to first- (NRTI/NNRTI) and second-line (NRTI/NNRTI/PI) regimens  available in LMIC. Percentage of virological successes at 8- and 24-weeks were 79% and 75%. For the 8-weeks outcome, model (ii) had an average (st.dev) AUC of 0.76 (0.03), significantly higher (p<0.0001) than model (iv) and (v), that yielded AUC of 0.71 (0.03) and 0.70 (0.03), respectively. The same held for the 24-weeks outcome, where model (ii) had an average (st.dev) AUC of 0.81 (0.02), significantly higher (p<0.0001) than model (iv) and (v), with AUC of 0.75 (0.03) and 0.74 (0.03), respectively.As a final evaluation, a special training/test data split was used for SD8G. The split was based on the subtype of the virus. Precisely, TCEs were grouped into subtype B or non-B, resulting in n = 2,353 and 478 instances, respectively. RF model (i) and (ii) were trained and 10-fold cross-validated on the B subtype set, and then tested against the non-B set. The proportion of successes at 8-weeks was 67% for B and 73% for non-B subtypes. In a single 10-fold CV run, model (i) yielded an average AUC of 0.765, model (ii) of 0.757, and model (iii) of 0.753. On the test set (i.e. non-B subtypes), AUC was 0.721 for model (i), 0.721 for model (ii), and 0.653 for model (iii).AUC plots for the sensitivity analyses, along with variable importance evaluation on the SD8H data set, are available as supplementary material .Following the demonstration of a major role for HIV-1 drug resistance mutations in response to antiretroviral therapy, GRT has been the standard decision tool to face HIV-1 drug resistance in clinical practice. The caregiver can use one or multiple systems developed to translate HIV-1 genotype into clinically relevant information. Commonly used genotype interpretation systems do not integrate additional patient information as an input to complement HIV-1 genotype in the effort to predict response to cART. A few experimental systems derived from statistical learning have recently been described which integrate patient and virus information to build an effective antiretroviral regimen The expanding cART coverage of LMIC poses new challenges for optimising treatment due to limited availability of second-line and later regimens and frequent lack of facilities or funding to support GRT. Our analysis suggests that availability of basic patient data supplemented with simple categorical indicators of past treatment can provide short- and medium-term response prediction as accurately as in the presence of HIV-1 genotype information. This finding is not surprising since the virus genotype is actually shaped by anti-HIV-1 compounds and can thus summarize the patient TH. It is interesting to note that the performance of the GRT-based model, which did not include any information on TH, decreased, although not significantly, at 24-weeks, compared to that of TH-containing models. This may be a consequence of the fact that bulk genotyping does not capture minority variants that have been selected by previous exposures We showed that when the HIV-RNA covariate is deleted the AUC performance decrease, which is an argument in favour of the use of viral load monitoring for the optimization of cART in LMIC. This reconciles with another recent study from Revell et al. In a sub-analysis, we showed that the accuracy of the prediction models decreased when training on HIV-1 subtype B data and validating on non-B subtype data. While non-B grouping is an artificial approach not corresponding to any biological entity, differences in response to certain cART regimens with specific subtypes may occur and must be considered in the development of treatment decision tools.A limitation of our study is that the datasets used do not correspond to a typical scenario of LMIC, since models were applied on data composed by patients cared in Europe. However, we performed sensitivity analyses excluding GRT-guided TCE, and including only first- and second-line regimens with drugs available in LMIC. But a more detailed investigation is advisable since the outcome distributions varied significantly across different datasets. Although the compounds considered in these analyses can be accessible in LMIC, drug combinations might differ.In addition, in LMIC additional factors like co-morbidity with other diseases, cost of treatment, distance to treatment centres, interruptions of stocks for drugs during certain periods, and stigma can play an important role in treatment outcome. This information needs to be integrated in a system specifically designed for LMIC, provided that co-operation efforts for data collection are appropriately set up.Another critical point is the fact that therapy switches in EuResist are mainly driven by virological monitoring, and not by clinical/immunological criteria, that are commonly used in LMIC. Finally, in this study a 500-copy threshold in the definition of undetectable viral load was used. This was due to the inclusion of HIV-1 RNA data derived from old generation laboratory assays: although this is a strong limitation for a model designed for high-income countries, where the virological reduction below 50 cp/ml would be the necessary outcome, in LMIC the end-point might be revised by considering the HIV-RNA only where testing is available and CD4/clinical monitoring otherwise.Despite these limitations, prediction of response to treatment based on TH rather than on GRT appears to be an appealing strategy providing a possibility to help clinicians with data-driven systems in the absence of HIV-1 genotype information. We realize that the here described model is only applicable when therapy failures are mainly judged from viral load monitoring, and when further lines of treatment are available, which is currently not the case in LMIC. However, the concept of the model warrants optimism towards the development of more appropriate models for LMIC. Thus, further development along these line are warranted, along with a coordinated effort to collect HIV-1 treatment related data from the areas that could maximally benefit from it.Material S1(0.03 MB DOC)Click here for additional data file.Figure S1Variable importance evaluation by RF model (ii) on SD8H: mean decrease in Gini index.(0.17 MB TIF)Click here for additional data file.Figure S2ROC plots of a single 10-fold CV run for RF models on SD8H and SD24H excluding instances with an available baseline GRT  and with/without baseline HIV-RNA load as a covariate.(0.25 MB TIF)Click here for additional data file.


Patients with HIV and hepatitis C virus (HCV) coinfection are at increased risk of developing hepatic steatosis. The aims of this study were to assess the prevalence of steatosis in a cohort with HIV-HCV coinfection, and to determine an association, if any, between steatosis, antiretroviral therapy (ART), and advanced hepatic fibrosis.HIV-HCV coinfected patients were retrospectively identified from the HIV clinic. ART was classified as none, nucleoside reverse transcriptase inhibitors (NRTIs) only, highly active antiretroviral therapy (HAART) only, and sequential therapy . Fibrosis stage and necroinflammation grade were assessed by the modified HAI (Ishak) scoring method. Steatosis was graded as 0–3.Sixty patients were identified. The overall prevalence of hepatic steatosis was 58%. Those that received HAART only had a lower prevalence of steatosis (41%) compared to those on NRTIs only (70%) or sequential therapy (82%). Independent predictors of hepatic steatosis were absence of HAART only therapy, OR 2.9, p = 0.09, and presence of cirrhosis, OR 4.6, p = 0.044. Forty five percent of the patients had advanced fibrosis (fibrosis stage ≥ 3). NI grade , and steatosis grade , were independent predictors of advanced fibrosis.Hepatic steatosis is associated with more advanced hepatic fibrosis in the HIV-HCV coinfected population. HAART only therapy  appears to be associated with a lower prevalence of hepatic steatosis. This may be one of the mechanisms by which HAART could attenuate hepatic fibrosis in such a cohort. Highly active antiretroviral therapy (HAART) has significantly improved survival in patients with human immunodeficiency virus (HIV) infection . IncreasPatients with HIV are also at increased risk of developing hepatic steatosis due to multiple factors including antiretroviral therapy (ART), obesity, hyperglycemia, lipodystrophy, and coinfection with HCV ,7-11. In1. Use of ART is associated with hepatic steatosis2. Hepatic steatosis is an independent predictor of advanced hepatic fibrosis3. Hepatic steatosis is associated with fibrosis progression in serial liver biopsiesThe study period was from 1990–2005. Patients with HIV-HCV coinfection were identified from the Pathology and HIV database after which, their charts and computerised chemical pathology and histology databases reviewed. To be included in the study the patients had to be1. HCV antibody and or HCV PCR  positive2. HIV antibody positive3. Have had a liver biopsy.The indications for a liver biopsy in most patients were abnormal liver tests. All the liver biopsies had been reviewed by RG who was blinded to the clinical information. The fibrosis stage (0–6) and necroinflammatory (NI) grade (0–18) were assessed by the modified HAI (Ishak) scoring system . SteatosHCV disease duration and fibrosis progression were calculated as before . LipoatrAnti-HCV antibody testing, HCV RNA and HIV RNA quantification and HCV genotyping were performed in the hospital virology laboratory using standard commercial kits .Alcohol abuse was defined as either consumption of > 3 units of alcohol/day (approximately 40 gms/day), and/or a written note in patient records stating that there was a history of alcohol excess. Details of ART were also recorded to assess duration of therapy prior to the liver biopsy. ART was classified as .1. None2. NRTIs only3. HAART only. Those whose HIV was diagnosed in or after 1996 and were therefore treated with HAART at onset. For the purpose of this study HAART was defined as use of any two nucleoside reverse transcriptase inhibitors (NRTIs) with any protease inhibitor (PI) or non nucleoside reverse transcriptase inhibitors (NNRTI)4. Sequential therapy: Those whose HIV was diagnosed prior to 1996; hence were initially treated with NRTIs and then converted to HAART after 1996.Ethnicity was classified (according to the HIV database) into White, Other European and Others.The following patients were excluded1. Presence of liver disease of another etiology, e.g. active hepatitis B infection, autoimmune liver disease, genetic hemochromatosis, and cholestatic liver disease2. Medical records/clinical data not availableData are presented as mean ± SD except the HIV and HCV viral load (VL) which are presented as median (range). Differences between continuous variables were compared using the Mann Whitney (U) Test and between categorical variables by the Chi Square and Fischer's Exact Test. Binary logistic regression analysis was performed to assess independent predictors of hepatic steatosis and fibrosis stage ≥ 3. The statistical software programme used was SPSS version 14 From 1990–2005 there were sixty-six patients with HIV who had been coded as having had a liver biopsy for HCV. Six were excluded, and therefore 60 patients were found suitable for the study. Of these sixty patients 52 (87%) had abnormal transaminases. All were HCV antibody positive, and all but one was HCV RNA  positive. Eight patients had undergone more than one biopsy, of whom seven had had two and one three biopsies.Table Some degree of hepatic steatosis was present in 58% of the patients, with the majority having grade 1 steatosis Fig . The useThere was no difference in durations of different forms of ART in those with and without steatosis Table . The oveTable Duration of stavudine (44.9 ± 21.5 vs. 24.8 ± 20.6) and didanosine (34.0 + 22.1 vs. 11.7 ± 10.4) were longer in those with fibrosis stage ≥ 3, though the differences did not achieve statistical significance . Factors that were significantly associated with fibrosis stage ≥ 3 on univariate analysis (p ≤ 0.1)  were then entered into a step wise logistic regression model. The following were independently associated with advanced fibrosis: grade of necroinflammation , and grade of hepatic steatosis (OR 3.6 (95% CI 1.0–12.7), p = 0.045). There were 15 patients who had both a necroinflammatory grade ≥ 4 (mean grade in the whole group) and steatosis. Prevalence of cirrhosis in this group was 40%, compared to 13% in those without any of these two factors (p = 0.02). Fig Of the eight patients with more than one biopsy, the mean interval between the biopsies was 52 ± 17.9 months. Five had fibrosis progression ≥ one stage of whom 4 (80%) showed evidence of hepatic steatosis in the index biopsy.This study has shown that 58% of HIV-HCV coinfected patients (of whom 72% were on ART) have evidence of hepatic steatosis on liver biopsy, and this is consistent with earlier data -11. ThosThe lowest prevalence of hepatic steatosis was observed in the HAART only group. Furthermore, the duration between HIV diagnosis and HAART initiation was longer for those with steatosis (47.4 vs. 40.4 months). This suggests that receiving HAART early, may in some way be protective against development of subsequent hepatic steatosis. Prior authors have also found a lower prevalence of hepatic steatosis with certain antiretroviral drugs (NNRTI) .So how do we explain this negative association between HAART and hepatic steatosis? It may be partly related to the fact that NNRTIs  do not result in insulin resistance and may also be associated with elevations in high density lipoproteins (HDL) . SecondlSteatosis has been independently associated with advanced fibrosis in HCV monoinfected patients ,23 thougThe effects of HAART on liver fibrosis in HIV-HCV coinfected patients remain controversial. Recent data however, do indicate that HAART may attenuate hepatic fibrosis in this cohort especially if patients receive HAART  at onset, and if therapy is associated with successful viral suppression ,24. DataWe however accept the limitations of this study, including its retrospective design, and the small sample size of the coinfected cohort. In addition liver biopsies were performed in only carefully selected patients. Though this clearly resulted in a selection bias, such a bias is true for most studies in this area, and in our opinion is unavoidable.In conclusion despite a low prevalence of obesity and DM, 58% of HIV-HCV coinfected patients have evidence of hepatic steatosis histologically. Absence of HAART only therapy was independently associated with steatosis in this study. The presence of hepatic steatosis was also associated with more advanced hepatic fibrosis in the coinfected cohort. The comparatively lower prevalence of hepatic steatosis seen in those that receive HAART only  may contribute to the attenuated hepatic fibrosis that has been observed with this therapy ,22.The authors declare that they have no competing interests.SV participated in the study design and was responsible for data collection, statistical analysis and drafting of the manuscript. RG performed the histological analysis and contributed to drafting of the manuscript. JM conceived of the study and contributed to drafting of the manuscript. All authors read and approved the final manuscript.


Hyperbilirubinemia is a common side effect of the antiretroviral agent atazanavir but is generally reversible upon discontinuation of treatment. We used therapeutic drug monitoring to investigate the occurrence of hyperbilirubinemia in a 49-year-old Hispanic man infected with HIV, following an overdose of ritonavir in ritonavir-boosted atazanavir therapy.trough and an initial rise in bilirubin plasma levels. Bilirubin levels later decreased, probably as a consequence of enzyme induction, while atazanavir plasma concentrations remained elevated.A 49-year-old Hispanic man with HIV who had received several highly active antiretroviral therapy regimens over a number of years including atazanavir-containing regimens, was diagnosed with hyperbilirubinemia. An inappropriate doubling of ritonavir boosting resulted in a high atazanavir CThis article describes an occurrence of hyperbilirubinemia in a man infected with HIV and supports the importance of therapeutic drug monitoring in investigations of hyperbilirubinemia among patients receiving antiretroviral agents. That the patient tolerated exceptionally high atazanavir levels further strengthens the tolerability profile of this drug. However, hyperbilirubinemia is generally reversible on discontinuation of treatment . Hyperbi (ATV/r) .In patients receiving antiretroviral therapy, therapeutic drug monitoring (TDM) may be a useful tool for: (i) assessing the interindividual variability in plasma drug concentrations, (ii) investigating the relationship between drug concentrations and either the anti-HIV effect or drug-related toxicities, (iii) documenting the occurrence of possible drug-drug interactions, and (iv) optimizing therapy in individual patients . We used3 and an HIV-RNA plasma viral load (HIV-RNA pVL) of more than 600,000 copies/ml.We present the case of a 49-year-old Hispanic man with HIV. The patient was diagnosed HIV positive in 1984, the infection most likely resulting from homosexual contact. He was also hepatitis A and hepatitis B positive, was hepatitis C negative and had negative syphilis serology. His total and fractionated bilirubin levels were normal and there was no history of Gilbert's disease in his medical record. In 2001, he suffered an episode of bacterial pneumonia and in 2002 he reached his CD4 count nadir, with 24 cells/mmIn 2003, he was diagnosed and treated for an oral Kaposi's sarcoma. As a flight attendant, he had a frenetic lifestyle and was treated for HIV infection in different countries. As a result, his medication history included several HAART combinations: (1) tenofovir disoproxil + didanosine + efavirenz (TDF + ddI + EFV); (2) tenofovir disoproxil + zidovudine + lopinavir/ritonavir (TDF + AZT + LPV/r); (3) stavudine + didanosine + lopinavir/ritonavir (d4T+ ddI + LPV/r); (4) stavudine + didanosine + efavirenz (d4T+ ddI + EFV); and (5) didanosine 250 mg once daily + tenofovir disoproxil + efavirenz (ddI 250 + TDF + EFV). The reasons for switching from one regimen to another between 2002 and January 2005 included virological failure, diarrhea and the development of lipodystrophy.3 and his viral load was approximately 8000 copies/ml. Genotype testing showed a 190Q mutation related to EFV and a 65R mutation related to TDF. In the protease domain, only a 63P polymorphism was evident.When we first evaluated the patient in January 2005, his CD4 level was 208 cells/mm3. At this time, his total bilirubin concentration was 1.40 mg/dl . A few weeks later, the patient returned to his home country in South America and was examined again in March 2006, during which his viro-immunological control was close to that obtained in Italy, six months previously.A new treatment regimen was started of ddI (400 mg once daily), d4T (40 mg twice daily), and ritonavir-boosted saquinavir (SQV/r), 1000 mg/100 mg twice daily. In September 2005, his HIV-RNA pVL was undetectable and CD4 cell count had risen to 232 cells/mmIn May 2006, he returned to our center complaining of colicky abdominal pain and diarrhea with up to eight discharges of loose stools per day. Scleral icterus was noted on physical examination. After excluding drug toxicities, viral hepatitis and alcohol abuse, it became evident that he had been switched from SQV/r to ATV/r while in his native country. Laboratory data showed grade 3 hyperbilirubinemia  while his HIV-RNA pVL and CD4 count remained basically unchanged. His trough ATV plasma concentration was measured by high performance liquid chromatography (HPLC) and found to be 5.2 μg/ml, around ten times the therapeutic level of 0.15-0.85 μg/ml [trough (5.4 μg/ml)  was noted but transaminase levels were within the normal range, as were gamma-GT and alkaline phosphatase. He was discharged from the emergency room with his gastrointestinal symptoms alleviated. In June 2006, the patient returned to our clinic still showing mild scleral icterus but was otherwise asymptomatic. At this stage he presented a note from his medical practitioner from South America that stated he had been taking 300 mg of ATV boosted by 200 mg of ritonavir (RTV). Hence, an incorrect RTV dosage had been taken since May 2006. A further HPLC test in June 2006 confirmed a high level of ATV C) Figure . Interestrough level was below detection limit (0.1 μg/ml), despite his adherence to the therapy. Thus, both the total bilirubin and the ATV Ctrough decreased after reduction of the RTV boosting dosage. At his last clinic visit in August 2006, the patient's total bilirubin concentration was normal, but the ATV Ctrough was below the therapeutic range. The patient did not return to our clinic and was therefore lost to follow-up.In August 2006, the patient's jaundice disappeared, his serum total bilirubin concentration was markedly reduced, and his CD4 cell count was unchanged, though levels of HIV-RNA pVL had increased to approximately 200 copies/ml. His ATV CThis case report highlights several points of interest. The inappropriate doubling of RTV boosting resulted in a high ATV trough and an initial rise in the bilirubin plasma level. Bilirubin levels later decreased, probably as a consequence of enzyme induction, while ATV plasma concentrations remained elevated. An "adaptation" in bilirubin levels during ATV-based therapy has been reported indirectly at the 96-week follow-up in the BMS-045 study (120 patients treated with ATV/r), where the incidence of grade 3-4 bilirubin elevation was almost five times higher in the first year of treatment than in the second year  [This article describes an occurrence of hyperbilirubinemia in a man infected with HIV and supports the importance of therapeutic drug monitoring in investigation of hyperbilirubinemia in people receiving antiretroviral agents. That this patient tolerated exceptionally high ATV levels further strengthens the tolerability profile of this drug.trough: plasma trough concentrations; HAART: highly active antiretroviral therapy; gamma-GT: gamma-glutamyl transpeptidase; HIV-RNA pVL: HIV-RNA plasma viral load; HPLC: high performance liquid chromatography; TDM: therapeutic drug monitoring; UGT: uridine diphosphate glucuronosyltransferaseATV: atazanavir; ATV/r: ritonavir-boosted ATV; CWritten, informed consent was obtained from the patient for publication of this case report and accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.The authors declare that they have no competing interests.All authors contributed equally to care of the patient. RM wrote the manuscript. All authors reviewed and approved the final manuscript.


Combination antiretroviral treatment (cART) has been very successful, especially among selected patients in clinical trials. The aim of this study was to describe outcomes of cART on the population level in a large national cohort.Characteristics of participants of the Swiss HIV Cohort Study on stable cART at two semiannual visits in 2007 were analyzed with respect to era of treatment initiation, number of previous virologically failed regimens and self reported adherence. Starting ART in the mono/dual era before HIV-1 RNA assays became available was counted as one failed regimen. Logistic regression was used to identify risk factors for virological failure between the two consecutive visits.Of 4541 patients 31.2% and 68.8% had initiated therapy in the mono/dual and cART era, respectively, and been on treatment for a median of 11.7 vs. 5.7 years. At visit 1 in 2007, the mean number of previous failed regimens was 3.2 vs. 0.5 and the viral load was undetectable (<50 copies/ml) in 84.6% vs. 89.1% of the participants, respectively. Adjusted odds ratios of a detectable viral load at visit 2 for participants from the mono/dual era with a history of 2 and 3, 4, >4 previous failures compared to 1 were 0.9 (95% CI 0.4–1.7), 0.8 (0.4–1.6), 1.6 (0.8–3.2), 3.3 (1.7–6.6) respectively, and 2.3 (1.1–4.8) for >2 missed cART doses during the last month, compared to perfect adherence. From the cART era, odds ratios with a history of 1, 2 and >2 previous failures compared to none were 1.8 (95% CI 1.3–2.5), 2.8 (1.7–4.5) and 7.8 (4.5–13.5), respectively, and 2.8 (1.6–4.8) for >2 missed cART doses during the last month, compared to perfect adherence.A higher number of previous virologically failed regimens, and imperfect adherence to therapy were independent predictors of imminent virological failure. Combination antiretroviral therapy (cART) has dramatically reduced morbidity and mortality of HIV-infected persons with access to care. Nevertheless, therapeutic failure still remains substantial, in particular due to late initiation, interruption or refusal of cART, incomplete adherence to therapy, medication toxicities, antiretroviral drug resistance, hepatitis virus co-infections, consumption of alcohol, illicit drug use, or depression.The potency of ART regimens has continuously improved but virological outcome is still not optimal. A large pan-European collaboration recently published on responses to cART across age groups and observed the best virological outcomes for older patients with up to 80% having reached viral suppression to <50 copies/ml by 3 years after initiating cART The aims of this study were to analyze determinants of virological failure in all HIV-infected persons on cART prospectively followed in a large national cohort study during 2007. Further, we wanted to describe the frequency of treatment modifications and discontinuations, as well as the clinical course. We were especially interested in the history of previous treatment failures and adherence as predictors for imminent virological failure.We selected participants of the Swiss HIV Cohort Study (SHCS) who were enrolled prior to 2007, were on uninterrupted cART for ≥3 months at their first cohort visit in 2007 (visit 1); and had one additional semiannual follow-up visit prior to June 30, 2008 (visit 2). Patients were categorized into two groups according to the era of antiretroviral treatment initiation, i.e. mono/dual drug therapy vs. cART era. We excluded patients who started with drug combinations not clearly attributable to mono/dual drug regimens or cART, unavailable CD4 cell counts, HIV-1 RNA or adherence data within 6 months prior to visit 1 or at visit 2.Patients were assigned to the mono/dual drug treatment era if their initial regimen consisted of ≤2 nucleoside reverse-transcriptase inhibitors (NRTI), or three NRTI's without abacavir prior to 1999. Patients starting with ≥3 drugs including a protease inhibitor (PI) or a non-nucleoside reverse-transcriptase inhibitor (NNRTI) or abacavir in addition to two other NRTI were assigned to the cART era.Previous regimens were defined as virologically failing from the date of the first available HIV-1 RNA record onward if ≥2 consecutive HIV-1 RNA measurements were >400 copies/ml, or ≥1 measurement was >1000 copies/ml, while the patient was on the same regimen for ≥3 months. In accordance with a UK-CHIC study Self-reported adherence was classified according to the number of missed doses within four weeks prior to a cohort visit  as described previously Hepatitis B virus (HBV) infection was considered active if HBs antigen, HBe antigen or HBV DNA were positive. Hepatitis C virus (HCV) infection was considered active if anti-HCV antibodies and HCV RNA were positive; and inactive if HCV serology was positive and HCV RNA negative.Virological failure was defined as having a HIV-1 RNA ≥50 copies/ml at visit 2. Treatment discontinuation was defined as ≥15 days off cART between the two visits. Treatment modification was recorded if at least one drug of a regimen was modified between the two visits. Treatment interruptions lasting less than 15 days were considered as treatment modifications. We also considered new AIDS-defining clinical events or death occurring between the two visits.The SHCS is a prospective cohort study, established in 1988, with semi-annual follow-up visits at university hospitals, collaborating clinics or private physicians' practices We decided to perform separate analyses for patients who initiated treatment in the mono/dual drug combination era and for patients who started with cART because of the potential survivor bias in the former group. Furthermore, preliminary analyses showed pronounced interactions between era of starting ART and the impact of previously failed regimens as well as the impact of suboptimal adherence. First, we performed descriptive analyses of the proportion of patients with HIV-1 RNA below and above 50 copies/ml and with CD4 cell counts below and above 200/µl at visit 1, and analyzed the association between these markers and the number of previous virologically failed regimens. Second, we determined the virological status at visit 2, and the proportion of patients with treatment discontinuation, treatment modification or clinical progression to AIDS or death between visit 1 and 2. Third, we used uni- and multivariable logistic regression to analyze predictors for virological failure at visit 2. Covariables in these models included gender, age , non-white ethnicity, mode of HIV transmission, HCV co-infections, HIV-1 RNA , CD4 cell counts (nadir and at visit 1), adherence to therapy, total duration of antiretroviral therapy (5 year strata) and number of previous failed regimens. To assess whether the exclusion of patients starting with non-standard ART affected our conclusions, we performed a sensitivity analysis in which these patients were combined with patients from the cART era. We used Stata 10.0 .The patient selection process is depicted in Reflecting the changing epidemiology of HIV in Switzerland, patients who started with mono/dual treatments were more frequently of white ethnicity, infected via needle sharing and thus with active HCV co-infection, had higher CD4 cell counts at enrolment in the SHCS, but lower nadir CD4 cell counts thereafter. At visit 1, these patients had more advanced HIV disease , had experienced more virologically failed regimens in the past (average 3.2 vs. 0.5), and the proportion with HIV-1 RNA <50 copies/ml was lower (84.6% vs. 89.1%). Irrespective of the era of treatment initiation, higher numbers of previous failed regimens were strongly associated with higher proportions of detectable HIV-1 RNA at visit 1  and of CD4 cell counts <200 cells/µl (p<0.001) at visit 1 .Among all patients without treatment discontinuations between the two visits, 179/1370 (13.1%) from the mono/dual and 271/3039 (8.9%) from the cART era had HIV-1 RNA ≥50 copies/ml at visit 2 (p<0.001). Results from uni- and multivariable logistic regression analyses for both eras are shown in The number of previously failed regimens remained a significant predictor of virological outcome at visit 2 when limiting the analysis to patients who had undetectable HIV-1 RNA levels at visit 1 with Odds Ratios of 2.33 (1.18–4.62) for ≥5 previously failed regimens among patients who started in the mono/dual era and 5.13 (2.62–10.0) for ≥3 previously failed regimens among patients who started in the cART era.Results from a sensitivity analysis in which we included patients who started with non-standard ART to the cART group were virtually identical.During the six months separating the two visits, 49/1419 (3.5%) patients from the mono/dual era and 83/3122 (2.7%) patients from the cART era discontinued treatment for 15 days or longer (P = 0.14). Reasons for discontinuation were patient's wish  or physician's decision (16.3% vs. 8.4%), drug toxicity (4.1% vs. 8.4%), and others (10.2% vs. 20.5%). Treatment modifications over the two consecutive visits were observed among 358/1419 (25.2%) patients from the mono/dual era and 768/3122 (24.6%) from the cART era (P = 0.65). 23/1419 (1.6%) patients from the mono/dual era intensified treatment by adding a drug; the other 335 (23.6%) stopped one or more drugs without completely discontinuing treatment. Similarly, 47/3122 (1.5%) patients from the cART era intensified treatment and 721/3122 (23.1%) stopped taking at least one of the drugs. Reported reasons for stopping a drug were virological, immunological, or clinical failure , metabolic disorders (6.6% vs. 7.1%), gastro-intestinal disorders (2.4% vs. 2.8%), other toxicities (9.9% vs. 8.7%), or other reasons such as patient's wish or physician's decision (72.1% vs. 76.7%).New clinical AIDS events between visit 1 and visit 2 occurred in 1 patient from the mono/dual era (non-Hodgkin lymphoma) and 4 patients from the cART era . Not included in the above analyses are the 33 patients who died before visit 2 . Of thesWe studied the impact of patients' treatment history, previous virological failures and adherence at two semiannual visits in 2007/08 among a cohort of 4541 participants on stable cART. At visit 1 the percentage of patients with viral loads <50 copies/ml was high with 84.6% of patients who started ART in the mono or dual therapy era and 89.1% among those who started with cART directly. Nevertheless, between 5 and 7% of these successfully treated patients experienced virological failure until visit 2 after six months. In the analysis of predictors for virological failure at visit 2 we found that the main independent risk factors were the number of previous failed regimens, suboptimal adherence to therapy and never having achieved an undetectable viral load.We observed that approximately 3% of patients discontinued treatment for 2 weeks or longer and 25% modified treatment between the two semiannual visits. A recent study investigating treatment switches and interruptions in the SHCS showed that changes are frequent: up to 48% of the patients change treatment within 12 months after treatment initiation. Intolerance is the main reason for treatment switches, whereas discontinuation is equally explained by both intolerance and patient's wish The proportion of 85–90% of treated patients in routine clinical care having undetectable viral loads is similar to what has been shown for randomized clinical trials of treatment naive patients Few long-term studies have directly compared patients from the mono/dual treatment and the cART era. The distinct group of patients, who started treatment already in the pre-cART era and survived, has now been on cART for more than 10 years. Most of these patients were only on partially suppressive treatments before cART The association between the history of treatment failures and later viral break-through has been shown in previous studies Although the assessment of adherence is not uniform and subject to methodological bias, the relationship between poor adherence and virological failure is not disputed The design of the present study implied that patients had to survive until 2007, therefore selecting patients with good prognostic markers who initiated ART in the era of mono/dual therapies. In fact, 2688/5769 (47%) patients who initiated ART with a mono/dual regimen in the SHCS died before 2007, vs. only 349/5191 (6.7%) patients who started therapy with cART. Our study reflects the current situation of the present heterogeneous patient population in a country with universal access to care. Thus, extrapolations to other settings, especially in developing countries need to be done with caution. An additional limitation by design is the short observation period of 6 months which precludes the analysis of events that require a longer time to occur. On the other hand, this relatively short follow-up period was deemed to represent the typical clinical situation of routine patient care.Although antiretroviral treatment is very successful, lack of continued viral suppression on stable cART is still relatively frequent in today's practice. Major information on risks for virological failure is the accurate patient's history, including the history of mono/dual drug therapy, the number of previous regimens with virological failure, and adherence to therapy. The former factors are associated with archived resistance mutations and mandate a careful selection of drugs in case of a treatment change, even if the current resistance testing - if at all possible - may not reveal all accumulated resistance mutations. Maintenance of good adherence to therapy is key of patient care and long-term suppression of viral replication, especially with the promising new drugs and drug classes currently entering routine care.


To assess the adverse effects of antiretroviral therapy (HAART) and its adherence in HIV-infected patients, in remote and tribal area with restricted resources.This was a prospective, observational study carried out at Department of Medicine, Government Medical College, Jagdalpur. A set of questions were asked and adverse drug reactions (ADRs) were recorded for every patient.79 HIV positive patients were analyzed. Among them, 68 (86%) had at least one ADR. The mean ADR per patient was 1.64 (±1.09). The most common ADR in our study was peripheral neuropathy (20.83%), followed by skin rashes (15.83%). Twenty-one patients (26.58%) had severe (grade-3 and grade-4) ADRs. Female patients had more ADRs (45.71%) than males (11.36%); severe ADRs had a statistically significant positive correlation with sex and CD4 cell count of the patients.In spite of high ADRs, HAART is the only answer to HIV/AIDS; thus, management requires a highly precise balance between benefits of durable HIV suppression and the risks of drug toxicity to achieve the therapeutic goals, with conventional drugs or with newer less toxic agents. Universal access to antiretroviral therapy (ART) is the aim of World Health Organization and UNAIDS. In June This was a prospective, observational study conducted at the Department of Medicine of Government Medical College and associated Maharani Hospital, Jagdalpur Chhattisgarh, with an assistance from the Department of Pharmacology after approval from Institutional Human Research Ethics committee and written consent of patients. The study was conducted from December 2006 to November 2008, in a cohort of 79 patients. All adult HIV patients on HAART therapy of either sex were included. Patients taking antitubercular treatment (ATT) and other drugs with a potential for hepatic or renal toxicity or interactions with HAART drugs and those with a history of alcohol abuse and noncompliance were excluded from the study.A baseline interview was taken before initiating the therapy followed by further interrogation at first, fourth, and seventh month for the development of ADRs. Information on adverse reactions and data related to health care utilization variables were collected from the medical charts as well as through self-reporting by patients. An adverse reaction to ART was defined as any undesirable effect or symptom registered in the medical charts by the treating physician, that occurred up to 1 year of the first ART prescription.Baseline laboratory investigations such as hemoglobin (Hb), total and differential leukocyte counts, erythrocyte sedimentation rate, urine analysis, serum venereal disease research laboratory (VDRL) test, serum hepatitis B surface antigen (HBsAg), Mantoux test (MT), and fine needle aspiration cytology (FNAC) of lymph nodes were carried out in each patient to rule out any opportunistic infection or specific contraindications to any drug. The chest X-ray and ultrasonography of the abdomen were done to determine the focus of tuberculosis. Patients were also subjected to liver function tests (LFTs); renal function tests (RFTs), lipid profile, and blood sugar analysis. Clinical examination along with appropriate lab investigations, histopathology, and radiodiagnosis were documented in the adverse reactions reported by the treating physician.P value < 0.05 was considered to be statistically significant.Three types of HAART regimens were used: A: Stavudine, lamivudine, and nevirapine (63%); B: Lamivudine, zidovudine, and nevirapine (30.37%) and C: Stavudine, lamivudine, and efavirenz (6.32%). Allotment of HAART regimens was based on physician's judgment. Data were analyzed using the chi-square test for establishing correlation between severe ADR and different variables. A A total of 137 HIV patients were enrolled. Among them, 15 expired, 16 patients stopped the therapy midway, 9 were lost to follow up, and 18 patients were on antitubercular treatment (ATT) simultaneously and so were excluded from the study. Of the 79 patients included in the study, 44 were males and 35 were females. The study group had a mean age of 31.16 ± 8.39 years.At least one ADR was seen in 68 (86%) patients. The mean ADR per patient was 1.64 (±1.09). The most common ADR observed was peripheral neuropathy (20.83%) and second most common was skin rash (15.83%) . A totalThe incidence of severe adverse reactions was 32.55% in patients less than 35 years of age, as compared to only 19.44% in those more than that. This was statistically insignificantly .3. Sixteen of these patients (48.48%) had severe side effects and in 46 patients who had CD4 count of >200 cells/mm3, only 5 (10.86%) had severe side effects which is statistically highly significant. The co-relation between different regimens and severity of ADRs reported were also as statistically insignificant.Among 79 patients, 33 had a CD4 count <200 cells/mmet al. (2008) reported ADRs in 71.1% of cases, and the most common ADR reported was cutaneous manifestation.[et al. (2002) has also shown peripheral neuropathy, anemia, and nail hyperpigmentation as the most common side effects. In our study, lipodystrophy was observed in 20.04% of cases, while in other studies it was seen in 14.5% of the cases.[9et al. (2002) reported the incidence of lipodystrophy to be 46.5%.[In the present study, 79 patients on HAART regimens were observed over a period of 2 years. Adverse effects were seen in 86% of cases. Sharma e cases.9 Saint-Mabe 46.5%.et al. (2008) reported ADRs in 94% of cases, the most common being a sexual disorder (40%),[A study by Malangu er (40%), but in oThe correlation between female sex and severe ADR was found to be statistically highly significant. It is not clearly known why sex difference exists in adverse reactions to antiretroviral drugs. Factors cited include differences in weight and body mass index, hormonal changes unique to females, and the effect of these changes on drug metabolism. Other po9 cells/L.[ADR to ART may depend on the baseline CD4+ cell count at initiation of therapy. A study by Center for Disease Control and Prevention on HIV outpatients suggested that some complications were more frequent and severe when therapy is started at lower CD4+ cell counts. Thus, patients may actually experience fewer side effects over a 10-20 year period of drug exposure if they start therapy 18-24 months earlier than if they delay therapy until the CD4+ cell count decreases to less than 0.200 × 10 cells/L.The measurement of viral loads and CD4 cell counts and the standard of care for assessing efficacy and response to HAART are not possible everywhere, particularly in resource-restricted places. Also, as eradication of the disease is currently not possible, significant problems related to compliance and long-term toxicity can be anticipated with decade-long therapies. Patient compliance can be improved with proper education and counseling regarding the disease process and inherent but innocuous side effects of HAART. More research is needed to develop low-cost investigations and algorithms for prediction of adverse effects of existing regimen, along with generation of more efficacious and less toxic drugs.


While the relationship between average adherence to HIV potent antiretroviral therapy is well defined, the relationship between patterns of adherence within adherence strata has not been investigated. We examined medication event monitoring system (MEMS) defined adherence patterns and their relation to subsequent virologic rebound.We selected subjects with at least 3-months of previous virologic suppression on a non-nucleoside reverse transcriptase inhibitor (NNRTI)-based regimen from two prospective cohorts in France and North America. We assessed the risk of virologic rebound, defined as HIV RNA of >400 copies/mL according to several MEMS adherence measurements.Seventy two subjects were studied, five of them experienced virologic rebound. Subjects with and without virologic rebound had similar baseline characteristics including treatment durations, regimen (efavirenz vs nevirapine), and dosing schedule. Each 10% increase in average adherence decreased the risk of virologic rebound  , P<0.002). Each additional consecutive day off therapy for the longest treatment interruption  and each additional treatment interruption for more than 2 days  increased the risk of virologic rebound. In those with low-to-moderate adherence (i.e. <80%), treatment interruption duration , but not average adherence  was significantly associated with virologic rebound.Sustained treatment interruption may pose a greater risk of virologic rebound on NNRTI therapy than the same number of interspersed missed doses at low-to-moderate adherence. Adherence to HIV antiretroviral therapy is the strongest predictor of virologic suppressionin vivo, we hypothesized that once viral suppression was achieved, a sustained treatment interruption rather than frequent missed doses would be associated with virologic failure.Because not all patients with moderate adherence are suppressed, we asked if all missed doses are the same. Specifically, we hypothesized that patterns of adherence in addition to average adherence may be an important determinant of incomplete viral suppression. We focused on NNRTI based regimens because these drugs are the cornerstone for most first line regimens world-wide. Because the NNRTIs are potent and have a very long-half life To test this hypothesis, we analyzed prospectively collected data from NNRTI treated individuals who achieved an initial virologic response during adherence monitoring with electronic medication monitors. Electronic medication monitors measure patterns of missed doses with a time-date record of pill bottle opening behaviour. The objective of this study was to identify adherence patterns predictive of virologic rebound on NNRTI-based antiretroviral therapy. We examined the temporal association between average adherence, treatment interruptions and subsequent viral rebound in all subjects, but in particular, subjects with <80% adherence because they are at highest risk for virologic reboundWe conducted a case-control study of NNRTI treated patients nested in two prospective observational cohorts: the Posology of Nevirapine (POSOVIR) Study and the Research in Access to Care (REACH) Cohort. POSOVIR is a randomized study of once versus twice daily nevirapine at 4 teaching medical institutions in FranceAdherence was prospectively measured using the Medication Event Monitoring System (MEMS) 6 caps . Individuals with no MEMS events for >15 days immediately prior to virologic rebound were excluded in order to exclude rebound due to simple treatment discontinuation. Percent dose adherence was defined as MEMS events/prescribed number of doses×100. We characterized patterns of missed doses by several a priori measures: (1) number of days without a dose, defined as drug discontinuation for more than 24 hours and less than 48 hours; (2) number of treatment interruptions lasting ≥48 hours, and (3) the duration of the longest treatment interruption (in days).t-test. The effect of adherence on the probability of virologic rebound was estimated by calculating the exact odds ratios (ORs) and their 95% confidence intervals using univariate exact conditional logistic models, to account for the small sample size with sparse dataContinuous variables were summarized as means, medians, standard deviations and ranges. Dichotomous data were summarized as proportions. Rates were compared by Fisher exact test and continuous variable were compared by We conducted a sensitivity analysis limited to subjects with <80% adherence in order: (1) to create a closer balance in the distribution of adherence between the groups with and without virologic rebound, since all the subjects in the former group had <80% adherence; and (2) to test whether adherence patterns still predicted viral rebound at low-to-moderate adherence. Quantitative variables were compared between groups with and without virologic rebound by exact Wilcoxon two-sample non-parametric tests because of the uncertainty of whether the small sample met assumptions of a normal distribution.P values are 2-sided, and P<.05 was considered significant.Analyses were performed using PowerView 2.3.3  and SAS version 9.1 . All reported Seventy-two participants met eligibility criteria. By design, all subjects had a HIV RNA less than 400 copies/ml during at least 3 months of NNRTI-based antiretroviral therapy. Five subjects experienced virologic rebound. Other antiretroviral agents included zidovudine plus lamivudine in 33 (46%), tenofovir plus lamivudine or emtricitabine in 18 (25%), abacavir plus lamivudine in 5 (7%), other nucleosides without protease inhibitors in 11 (15%) and ritonavir-boosted protease Inhibitors in 5 (7%). The mean (SD) duration of MEMS monitoring in days among subjects with and without virologic rebound were 85 (6) and 88 (6) days, respectively. Other baseline characteristics were similar between groups .All adherence measurements significantly predicted virologic rebound, except the frequency of short-term interruptions (24 to 48 hours), as shown in In the sensitivity analysis limited to subjects with low-to-moderate (<80%) adherence, the mean difference in longest interruption between those with and without viral rebound was 10.1 days  and statistically significant (p<0.02); whereas, the mean difference in adherence between those with and without viral rebound was 2.8%  and was not statistically significant (p = 0.65). . There wThese data suggest that sustained and repeated NNRTI treatment interruptions are associated with viral rebound. Specifically, sustained treatment interruptions more closely predicted viral rebound than interspersed missed doses in patients with low-to-moderate adherence, which represented 24% of our sample. Average adherence and the potential duration of treatment interruptions are, of course, not independent; 100% adherence precludes an interruption in treatment. As adherence rates decline, however, different patterns of missed doses are possible, as shown in Our finding derived from “real life” treatment interruptions in socio-economically and ethnically diverse patients is consistent with both observational and experimental studies of treatment interruptions. For example, Oyugi et al. found that MEMS defined treatment interruptions with a mean of 11.5 days were associated with NNRTI resistance in UgandaThere are several limitations to our study. The number of events (five) is small, mainly because MEMS technology is not routinely used and because the risk of virologic rebound subsequent to viral suppression on NNRTI-based antiretroviral therapy is lowGross et al. found that patients treated with efavirenz-based regimens had suboptimal adherence up to 90 days prior to viral reboundIn summary, near perfect adherence leading to sustained virologic suppression remains the goal of HIV therapy. However, patients with moderate adherence to NNRTIs-based regimens can still achieve virologic control. For these patients with incomplete adherence, missing doses over a continuous and sustained interval may pose more risk for virologic rebound than interspersed missed doses. Limiting sustained NNRTI treatment interruptions may improve durable virologic suppression, especially in patients with incomplete adherence.


The impact of highly active antiretroviral therapy (HAART) on health-related quality of life has been widely researched in the developed world, but there are few data from sub-Saharan Africa, where the vast majority of HIV-infected individuals live. This study examined health-related quality of life among HIV-positive individuals initiating HAART in Cape Town, South Africa, and explored the impact of HAART-related drug toxicities on quality of life.Health-related quality of life was assessed using a standardised questionnaire, the Medical Outcomes Survey Short Form 36. Physical health summary scores and mental health summary scores were compared pre-HAART and at regular intervals during the first 48 weeks of HAART. The relationships between socio-demographic, baseline and on-treatment variables and decline in health-related quality of life, as well as the impact of drug toxicities on quality of life, were assessed in unadjusted bivariate and adjusted multivariate analyses.Two hundred and ninety-five patients were enrolled into the study. There was a significant increase in health-related quality of life during the first 48 weeks on HAART. The median physical health summary score increased from 45 to 53 units (p < 0.001) and median mental health summary score increased from 45 to 50 units (p < 0.001).The bulk of this increase occurred during the first 16 weeks. Overall, 23% of participants experienced a decline in their physical health summary score, while 34% showed a decline in the mental health summary score. Average drops in median physical and mental health summary scores were 8.4 units (SD 9.31) and 9.9 (SD 11.4) units respectively. Participants with drug toxicity had lower physical health summary scores than participants without drug toxicity at all time points. However, only three participants with toxicity (27%) reported an actual decline in health-related quality of life by week 48. Drug toxicities had little impact on mental health summary scores.These results confirm the health-related quality of life benefits of HAART. While the majority of patients experienced a significant improvement in health-related quality of life on HAART, up to a third of patients reported declines in this quality of life. This was largely related to better baseline clinical state. HAART-related drug toxicities did not have a significant impact on health-related quality of life during the first year of HAART, which supports the ongoing use of the current national first-line regimen. By December 2006, an estimated 39.5 million people worldwide were living with HIV and a further 2.9 million people had died due to AIDS. The bulk of infections  occurred in sub-Saharan Africa, where 24.7 million people were reported to be HIV infected . In SoutPrior to 2004, people infected with HIV in South Africa who were unable to access life-saving antiretroviral (ARV) therapy progressed to AIDS and died of their disease. The rollout of highly active antiretroviral therapy (HAART) through national and provincial programmes has dramatically altered this experience.By late 2008, an estimated 549 700 HIV-positive individuals were receiving HAART in South Africa . With inThere is a sizeable body of research on the impact of HAART on health-related quality of life (HRQoL) in the developed world. Most recent cohort studies in the USA and Europe have shown no significant change in HRQoL within the first two years of HAART -11, althIn contrast, the 2NN study, which compared the efficacy and safety of three non-nucleoside reverse transcriptase inhibitor (NNRTI)-containing regimens  showed an overall improvement in HRQoL over 48 weeks .To date, only four studies have addressed the impact of HAART on HRQoL in developing countries -19. WoutOnly the studies by Stangl et al  and JelsInternationally, there is concern about the impact of HAART-related toxicity on HRQoL. In fact, it has been suggested that studies that consider only mortality outcomes ignore treatment-related morbidity and may actually overestimate the benefits of HAART .These international concerns are echoed in South Africa and other developing countries, where national first-line regimens tend to be NNRTI-based and incorporate drugs such as stavudine, which has been shown to be the reason for up to 75% of drug switches for toxicity within the first three years of first-line HAART . In SoutMore data about the impact of HAART and HAART-related toxicities on HRQoL are required in developing countries to inform programme and policy decisions about HAART roll-out strategies in order to maximise the quality of life of HIV-infected individuals.3 were commenced on first-line ARVs [This cohort study examined the HRQoL reported by HIV-positive individuals pre-HAART and at regular intervals during their first year of receiving HAART at the Hannan Crusaid Treatment Centre between September 2002 and March 2005. As per the national ARV guidelines, adult patients who had World Health Organization (WHO) Stage 4 disease and/or a CD4+ T cell count of <200 cells/mmine ARVs . The majThe Hannan Crusaid Treatment Centre is a community-based ARV clinic that was initiated in September 2002 as a joint venture between the Western Cape Department of Health, Desmond Tutu HIV Foundation and Crusaid, a UK-based non-governmental organization that raises funds to support people living with HIV/AIDS. The clinic was one of the first ARV rollout sites in the Western Cape Province of South Africa. It is situated alongside the primary community health care centre and boasts a multidisciplinary team of medical doctors, clinical nurse practitioners, clinic nurses, Sizophila adherence counsellors and a pharmacist.The clinic followed a programmatic approach to ARV care with a standard first-line and second-line regimen. In keeping with WHO recommendations, the first-line regimen was NNRTI-based and the second was protease inhibitor-based .Adults commenced on the first-line regimen (efavirenz or nevirapine plus stavudine and lamivudine) were reviewed at four, eight and 16 weeks, and thereafter every four months by a medical doctor. At these scheduled visits, patients were assessed clinically, virologically and immunologically. Patients who discontinued their first-line regimen – either due to virological failure or for toxicity reasons – were worked up for the second-line regimen . Nucleoside reverse transcriptase inhibitor (NRTI) substitutions were made within regimen 1 or 2 for NRTI-associated toxicities.At the screening visit, HIV-positive individuals met with the clinic nurse, who completed a demographic information sheet. Blood was drawn for viral load, CD4 cell count and safety blood testing (including a full blood count and liver function tests) at the screening visit and at all subsequent scheduled visits prior to the patient seeing the medical doctor.The adherence counsellors were trained in the administration of the HRQoL instrument. The quality of life questionnaire was administered at each of the following scheduled visits: screening, baseline, week 16, week 32, week 48 and week 64. Although HRQoL data continued to be collected at scheduled visits following week 64, this study focused on quality of life only during the first year of ARVs.HRQoL data were intended to be collected on all patients at all scheduled visits within the first year. This, however, was not always possible. Reasons for incomplete HRQoL data were: death, loss to follow up, transfer out, and patients leaving the clinic without the questionnaire being administered. The analysis only included those patients with HRQoL data available at all time points during the first year on HAART.The University of Cape Town Research Ethics Committee approved all research activities involving antiretroviral service delivery and patient outcomes at the site. Patients signed a research consent form at the screening visit, indicating their willingness to take part in this research study.Health-related quality of life was assessed using a standardised questionnaire, the Medical Outcomes Survey Short Form 36 (MOS-SF36). The instrument uses 36 items to assess eight health concepts: (1) physical functioning; (2) role limitations because of physical health problems; (3) bodily pain; (4) social functioning; (5) general mental health; (6) role limitations because of emotional problems; (7) vitality; and (8) general health perceptions .The MOS-SF36 questionnaire has been widely used in studies of quality of life in HIV-positive patients in both developed and developing countries, and has performed well in all of these settings ,14,25-28Quality of life data were entered using a custom-designed Epi Info™ template to ensure high data quality. On completion of data entry for each questionnaire, scores for the eight health components were automatically generated according to standard scoring algorithms. Data were then transferred into a Microsoft Excel spreadsheet where health component scores were transformed into the physical health summary (PHS) and mental health summary (MHS) scores using standardised factor analysis-based weights.Missing questionnaire items were estimated using a standard scoring algorithm that estimates missing values . Scores Demographic information was collected using standard paperwork. Patients were staged according to WHO clinical criteria by the medical doctor at their screening visit. The Toga Laboratory performed viral load and CD4 cell count testing. Viral load testing made use of the branch DNA hybridisation technique  and CD4+ T cell counts were measured by flow cytometry .Drug toxicities were detected by the medical doctor at both scheduled and unscheduled clinical visits through clinical questioning, examination and safety blood draws . Drug toxicities were defined as any adverse event thought by the clinician to be HAART-related and that required a change in antiretroviral therapy. Drug changes could either be a NRTI substitution or a change from NNRTI to a protease inhibitor.The cohort was initially described using means, medians and proportions, as appropriate. Changes in HRQoL pre-HAART and at week 16, 32 and 48 were compared using the Wilcoxon Rank Sum Test. Crude associations were first examined using Fisher's Exact, Chi Squared and Wilcoxon Rank Sum tests, as appropriate.Negative HRQoL was defined as a decrease in PHS or MHS scores between pre-HAART and week 48. Univariate relationships were then explored between the outcome variables – negative PHS and negative MHS – and each explanatory variable. Multivariate analyses made use of logistic regression models to examine the adjusted association between negative HRQoL and various socio-demographic, baseline and on-treatment explanatory variables as appropriate.Multivariate analysis started with a full model (Model 1) and explanatory variables were removed in a stepwise manner until the final model (Model 2) was selected. All final logistic regression models were checked against model assumptions. Outliers and potentially influential observations were identified and examined to ensure that model results were not being unduly influenced by a small number of non-representative observations. Models were rerun with selected observations excluded.All statistical analyses were performed using Intercooled Stata Version 8.2 . All statistical tests are two-sided at alpha = 0.05.Of the 295 patients with any HRQoL data, 292 (99%) had baseline data, 271 (92%) had week 16 data, 233 (79%) had week 32 data, and 179 (61%) had week 48 data. Complete HRQoL data, obtained pre-treatment and at every scheduled on-treatment visit, were available for 147 patients.Table 3  and median baseline log viral load was 4.9 . Men were older and had more advanced disease than women. The majority of drug toxicities (90.9%) occurred in women with only one drug change made due to toxicity among men. There were no differences in demographic and baseline characteristics between patients with complete HRQoL data (n = 147) and those with incomplete data (n = 148).The median baseline CD4 count was 88 cells/mmThe median scores for the eight health concepts pre-treatment and at regular intervals on-treatment are described in Table The physical health summary and mental health summary scores also showed an improvement in HRQoL over time Figure . There wHowever, not all participants experienced a linear increase in HRQoL. Using a random sample of 15 participants, it was evident that while the bulk of participants experienced a gradual improvement in HRQoL, others experienced a worsening of HRQoL Figure . While tSimilarly, while MHS score increased by an average of 3.3 units (standard deviation: 11.4) between pre-HAART and week 48, 34% of participants experienced a decline in MHS score. The average drop in MHS score among these participants was 9.9 units (standard deviation: 5.92).Baseline log viral load, pre-HAART PHS score, change in CD4 count and change in log viral load were all strongly associated with a negative PHS in the univariate analyses Table .In the multivariate regression model, pre-HAART PHS score and baseline log viral load were the strongest predictors of negative PHS at week 48. Participants with a higher pre-HAART HRQoL score were more likely to report negative PHS , whereas participants with a higher baseline log viral load (>5.0 log) were less likely to report negative PHS than participants with a lower baseline log viral load (≤ 5.0 log) .Age was also predictive of negative PHS. Older participants (above 34 years of age) were more likely to report negative PHS than younger participants . Neither gender nor any of the week 48 variables were associated with negative PHS.Baseline CD4 count, baseline log viral load, pre-HAART MHS score and change in log viral load were all associated with negative MHS in the univariate models (Table 3) were less likely to experience negative MHS than participants with higher baseline CD4 count (>50 cells/mm3) . Participants with a higher baseline log viral load (>5.0 log) were less likely to experience negative MHS than participants with lower baseline log viral load (≤ 5.0 log) . Gender and the week 48 variables were not predictive of negative MHS at 48 weeks.Baseline CD4 count and baseline log viral load remained weakly associated with the outcome. Participants with lower baseline CD4 count (≤ 50 cells/mmEleven participants experienced drug-related toxicities during the first 48 weeks of HAART. Ninety-one percent (n = 10) of these toxicities occurred in women, with 50% (n = 5) of these being due to lactic acidosis. Participants experiencing drug toxicities had similar demographic and baseline characteristics to the overall cohort.Table One patient experienced an efavirenz hypersensitivity reaction between weeks 32 and 48. This was due to the fact that the patient was switched to efavirenz at this time. The bulk (64%) of toxicities occurred during the week 32 to 48 interval and were mostly elevated transaminases and hyperlactataemia related to stavudine use.The 11 participants who experienced drug toxicity during the first 48 weeks of HAART achieved lower PHS scores at all time points than the 281 participants who did not have toxicity. While these differences were not statistically significant pre-HAART and at weeks 16 and 32, they did become significant at week 48. The median PHS score at week 48 was 50 for participants with drug toxicity, compared to 53 for participants without drug toxicity (p = 0.0053) . There was no impact of baseline mental health status on the reporting of toxicities. At weeks 16, 32 and 48, participants with drug toxicity reported higher median MHS scores than those without toxicity. Again, these differences were not statistically significant  of all drug toxicities occurred among participants who reported negative PHS and that these toxicities occurred during the week 32 to 48 treatment interval. No drug toxicities occurred among participants who reported negative MHS.This study reported a significant increase in HRQoL during the first 48 weeks on HAART, with the bulk of this increase occurring during the first 16 weeks on treatment. Improvement in HRQoL occurred across all core domains assessed, as well as the physical health summary and mental health summary.This study therefore supports the findings of Stangl et al  and JelsThere have been few analyses dealing with declines in PHS and MHS scores. In fact, negative HRQoL is often overshadowed by the overwhelming positive impact of HAART on HRQoL, and is therefore not reported. This research showed that although there was a general improvement in HRQoL on HAART, up to a third of participants experienced a decline in HRQoL during the first 48 weeks of HAART. Twenty-three percent of participants reported a drop in PHS score and 34% reported a drop in MHS score.The most significant predictors of negative PHS and MHS were baseline HRQoL score, baseline log viral load and baseline CD4 count. The association between higher baseline HRQoL score and negative HRQoL could have been due to the fact that patients with higher baseline scores had less room for improvement and were therefore more likely to regress to the mean.Baseline log viral load was strongly associated with negative PHS. Participants with higher baseline log viral loads were less likely to report negative PHS than participants with lower baseline log viral loads. Similarly, baseline log viral load and baseline CD4 count were associated with negative MHS. Participants with higher baseline log viral loads or lower baseline CD4 counts were less likely to report negative MHS than participants with lower baseline log viral loads or higher baseline CD4 counts.Participants with more advanced disease, characterised by higher baseline viral loads and lower baseline CD4 counts, were less likely to report a decline in HRQoL than those with earlier disease. So it was the relatively well patients entering into the programme who were at greatest risk of experiencing negative HRQoL. These associations could be explained by the negative impact of symptoms on HRQoL -12. PatiAlthough there is great concern about the impact of drug toxicities on HRQoL, few studies have directly assessed this association. In the developed world, symptoms have been shown to impact negatively on both physical and mental HRQoL ,31. HoweIn the developing world, Jelsma et al  concludeFew drug toxicities were recorded during the first 48 weeks on HAART. While participants who experienced drug toxicity had lower PHS scores than participants without a drug toxicity at all time points , only three (27%) participants with toxicity reported an actual decline in physical HRQoL between pre-HAART and week 48.Drug toxicities, especially those related to stavudine use, may have a negative impact on physical HRQoL at the time of the toxicity. They do not, however, reduce overall gains in HRQoL. Drug toxicities had little impact on mental HRQoL.The greatest strength of this study is its longitudinal design. This allowed the assessment of the associations between various socio-demographic and clinical factors and HRQoL, and allowed for inferences to be made about causal relationships.Limitations to the study included possible selection bias and information bias related to the use of the MOS-SF36 instrument. The loss of up to 50% of patients, who had incomplete HRQoL data, from the final analysis may have resulted in a selection bias towards the healthier section of the cohort. However, as there were no significant differences in demographic, baseline and on-treatment characteristics between patients with incomplete and complete HRQoL data, it is unlikely that this was the case. This did impact on the number of patients available for analysis though, and may have limited the ability of the study to detect significant associations.The MOS-SF36 instrument is a generic HRQoL measure and, like all generic instruments, may not be sensitive enough to measure the more specific aspects of HRQoL impacted on by the HIV disease process. This could lead to either an underestimation or overestimation of HRQoL scores and, more important, to a change in HRQoL scores, thereby under-reporting or over-reporting the actual impact of HAART on HRQoL.The MOS-SF36 instrument is also prone to ceiling effects, where substantial numbers of patients get the highest possible score for a domain. This would have made it difficult for the instrument to pick up changes in HRQoL at the upper end of the scale, and may have been a problem with increasing time on treatment. Ceiling effects could have lead to the underestimation of HRQoL gains.This study reported on HRQoL during the first 48 weeks of HAART only. As HAART-related drug toxicities, especially those related to stavudine, increase with length of time on HAART , this stFurthermore, this study only considered drug toxicities that were severe enough to prompt a change in antiretroviral therapy. Less severe toxicities that may also have impacted negatively on HRQoL were not reported on. This could have led to an underestimation of the true negative impact of drug toxicities on HRQoL.This study confirmed the overwhelmingly positive HRQoL benefits of HAART in a community ARV clinic in South Africa. HRQoL improved significantly during the first 48 weeks of HAART, with the bulk of improvement occurring during the first 16 weeks of treatment.Up to a third of patients experienced a decline in HRQoL on HAART. This was largely related to the patient's baseline clinical state. HAART-related drug toxicities did not have a significant impact on HRQoL during the first year of HAART, supporting the ongoing use of the current national first-line regimen.The authors declare that they have no competing interests.JP participated in the design of the study and its coordination, acquired the data, performed the statistical analysis, interpreted the data, and drafted the manuscript. LM assisted with the statistical analysis and interpretation of the data, and helped to draft the manuscript. RW conceived of the study, participated in its design, and critically reviewed the manuscript. All authors have read and approved the final manuscript.


Prognosis of HIV-1 infection dramatically improved during the last decade. Meanwhile, treatment-induced virological success has always been different in adult and children patients.To compare 10 years of follow up in HIV-1 vertically infected children and adult patients.Monocentric retrospective longitudinal analysis of vertically HIV-1-infected children and adult patients followed in the Nice University Hospital between 1999 and 2008. Immunological, virological and antiretroviral treatment data were recorded.95th: 0.52–0.72). Year-to-patient interaction was also significant with a decreasing divergence over time (p: 0.038).Forty children and 1752 adult patients were included. Between 1996 and 2008, the percentage of children receiving HAART increased from 3.2% to 91%. Mean CD4% in the paediatric group remained stable between 29 ± 8.1% in 1998 and 30 ± 9.4% in 2008. Mean adult CD4-cell count significantly increased from 410 in 1998 to 556 cells/mL in 2008. Logistic regression analysis showed that the children-to-adult difference for indetectability (HIV PCR-RNA below 400 copies/mL) was significant (p < 0.0001) with an odds ratio of 0.61 (CINowadays as in adult patients, the control of HIV-1 replication is achieved in nearly eight of 10 children and the percentage of patients with severe immunodeficiency dramatically decreased compared with the mid 1990s. During the mid 1990s, the development of highly active antiretroviral therapy (HAART) resulted in a significant decrease in morbidity and mortality among HIV-infected children . The maiMuch progress has been made and pharmacokinetic studies in younger patients are now available for many drugs ,4. NowadImmunological, virological and antiretroviral treatment data from 40 HIV-1 vertically infected children (25 girls) followed between February 1995 and October 2008 were analyzed. The paediatric database was started prospectively in November 1991. For each consultation the child’s age, antiretroviral treatment, PCR-RNA HIV-1, CD4 and CD8 cell count and percentage were recorded. Children’s data were compared with those of adult patients recorded in the Nadis® database between 1998 and 2008. Nadis® is a computerized medical file for adult patients infected with HIV, HBV or HCV, initially activated in November 2000 in Nice and Toulouse. This database was previously described elsewhere . For allTo ensure cohesion during the 10-year period of follow-up, undetectable HIV-1 viraemia was defined as PCR-RNA below 400 copies per mL. All of the antiretroviral treatments were prescribed in agreement with the successive French recommendations [5). Two nucleoside reverse transcriptase inhibitors (NRTI) and one non-nucleoside reverse transcriptase inhibitor (NNRTI) or two NRTI and one protease inhibitor (PI) were considered as HAART for paediatric treatment. Definition of HAART was the same for adult patients apart from those compounds not yet currently available for children (fusion and integrase inhibitors).All values are furnished as mean ± standard deviation. Statistical analysis was performed with SAS software version 9  using a logistic regression analysis after adjustment for effect . The chi-square test was used for percentage comparisons .Among the 1170 recorded episodes in the paediatric database, 832 were analyzed. Data from patients without treatment were excluded (n = 338). These mainly involved patients on structured treatment interruption or new patients before treatment initiation. Thus, between 1995 and 2008, for each patient, an average of 2.6 blood samples per year was analyzed (1.7 – 4.0). Mean follow up per patient was 88.7 ± 40.2 months (5.7–152.3).As expected, the mean age of the paediatric cohort increased slightly yearly during the observation period . At the During the observation period, mean CD4% in the paediatric group remained stable between 29 ± 8.1% (783 ± 351 CD4 T-cells per mL) in 1998 and 30 ± 9.4% (721 ± 322 CD4 T-cells per mL) in 2008 , Fig. 2.10 (range: 4.0–5.67) in 1996 to 1.92 ± 0.56 log10 (range: 1.60–3.43) in 2008 (p < 0.001)  . Figure Prospective studies on efficacy, tolerability and safety of new antiretroviral drugs remain essential to explore and provide new tools against HIV infection in children. Most of the HAART-based combinations can achieve sustained viral suppression and beneficial clinical and immunological responses. Meanwhile, when considering a given population living with AIDS, cross-sectional analysis of viral suppression showed results far from those obtained in prospective controlled studies –10. IndeUntil now, in most phase I/II trials of new antiretroviral drugs, the percentage of children with undetectable viral load was lower than observed in adults, which is a similar trend to that observed in young outpatients in clinical practice. In 2000, the Italian registry showed a major improvement in the survival of perinatally HIV-infected children as a result of the introduction of combined antiretroviral therapies since 1996. This population-based multicentre observational study was the first paediatric study to provide information on clinical effectiveness at the population level .More recently, the study published in 2004 by the Collaborative HIV Paediatric Study group from UK and Ireland (CHIPS) confirms that there are critical differences in the predictors of immunological and virological response to HAART in previously untreated children and adults. Overall, 60% of their children achieved HIV-1 RNA levels below 400 copies/mL within the first 6 months of treatment, ranging from 48% in the youngest (<2 years old) to 76% in the oldest patients. No calendar year of starting HAART effect was noted . ConversOur data confirm that henceforward, in industrialized countries, as is the case with adult patients, most of the children receiving HAART for HIV infection achieved control of viral replication. We observed that in <3 years between 1999 and 2001, the percentage of HIV PCR-RNA <400 copies per mL increased dramatically in adults and children from 20–30% to nearly 60–70%. Progression among children was delayed compared with the situation observed among adults. There are probably at least two main reasons for this trend: first, the later availability of paediatric drug formulations for the new antiretroviral treatments, second, the need for specific paediatric data for pharmacokinetics. Variation in immunological status during the observation period was less obvious. We mainly observed a marked decrease in the percentage of children with the most severe immunodeficiency (<15% CD4). In both our adult and paediatric cohort, yearly evaluation of immune status showed a trend towards improvement.Our study is quite different from the COHERE  study that addressed treatment outcome by age in 49 921 patients ranging from 4 days to 87 years across 30 European countries. Better virological but poorer immunological responses in older individuals were observed. Meanwhile, as in our study, the chance of experiencing a good virological response was also associated with calendar period  and unknown or unmentioned confounders not registered in our databases  must probably be considered.Morbidity and mortality rates among perinatally HIV-1-infected children continue to decrease over time as recently confirmed in the group from UK and Ireland . MeanwhiHAART is now recommended as standard treatment regimen in both adult and paediatric HIV-infected patients living in industrialized countries ,9. Analy