To describe the long term outcome of patients who interrupted highly active antiretroviral therapy (HAART) once, identify the variables associated with earlier need to re-start HAART, and the response when therapy was resumed. A retrospective observational cohort of 66 adult patients with HIV-1 infection who interrupted HAART with a CD4+cell count ≥350 cells/μL and undetectable viral load (VL) was performed. The pre-established CD4+ cell count for restarting therapy was 300cells/μL. Cox regression was used to analyse the variables associated with earlier HAART reinitiation.The median follow-up was 209 weeks . Rates of HIV-related or possible HIV-related events were 0.37  and 1.49 per 100 patient-years, respectively. Two patients died after re-starting therapy and having reached undetectable VL. Three patients suffered a sexually transmitted disease while off therapy. Fifty patients (76%) resumed therapy after a median of 97 weeks . Age, a nadir of CD4+ <250 cells/μL, and a mean VL during interruption of >10,000 copies/ml were independent predictors for earlier re-start. The intention-to-treat success rate of the first HAART resumed regimen was 85.4%. There were no differences by regimen used, nor between regimens that were the same as or different from the one that had been interrupted.Our data suggest highly active antiretroviral therapy may be interrupted in selected patients because in these patients, when the HAART is restarted, the viral and clinical response may be achieved. High active antiretroviral therapy (HAART) has completely changed the prognosis of HIV infection . AlthougBeyond pre-established strategies, interruption of HAART are more frequent that desired due to different reasons , such asThe objectives of this study were to describe the very long term outcome of patients who interrupted HAART for ≥3 months while being in good immunologic condition and with undetectable VL, to evaluate the variables associated with an earlier need to re-start therapy, and to analyse the virologic and immune response to the reintroduction of HAART.A retrospective cohort analysis was performed. Patients were considered eligible if included in Hospital Universitario Virgen Macarena cohort of adult patients (age >18 years) patients with chronic HIV-1 infection, and had interrupted the antiretroviral drugs for at least 12 weeks between January 2001 and December 2004. Only patients who interrupted a stable HAART regimen while clinically stable, with undetectable VL (<50 copies/mL) and stable CD4+ cell count ≥350/μl for the last 6 months were included.Patients were visited at least every 12 weeks. In all routine visits, a clinical examination and routine blood tests including a CD4+ cell count and RNA HIV-1 viral load testing  were performed. Decisions about restarting HAART were discussed in every visit with the patients; the following criteria were pre-established as indications to reinitiate therapy: CD4+ cell count < 300 cells/μL (the attending physicians had previously agreed to consider this CD4+ count threshold for resuming therapy), pregnancy, acute retroviral syndrome, thrombocytopenia, and any other B or C event . Data weThe study was approved by the Ethic Committee from Hospital Universitario Virgen Macarena. The need to obtain written consent informs was waived because of the retrospective nature of the study.From all patients, the following variables were collected: age, gender, ethnics, risk behaviours, number of years since the diagnosis of HIV infection, toxic habits, HCV and HBV coinfection, HIV category before interruption, CD4+ cell nadir and in the interruption, number and duration of HAART regimens, previous virologic failures, and previous available resistance-related mutations.The main endpoints of the study were the occurrence of HIV-related or possibly related events and death from any cause. The following events were considered HIV-related: acute retroviral syndrome, all B events according to 1993 CDC classification , and allSecondary end-points were: time until HAART was restarted and response to HAART after interruption. The latter was defined as failure in an intention to treat analysis if any of the following circumstances occurred: undetectable VL (<50 copies/ml) had not been reached in the visit when the HAART regimen was changed or at the end of follow-up , the HAATime until reinitiation of HAART was studied using Kaplan-Meier curves. The crude association between exposure to the different variables and the time until restart of therapy was studied by the log-rank test. Multivariate analysis was peformed by Cox regression. Analysis were performed with SPSS 15.0.Neisseria gonorrhoeae, one pyelonephritis due to Escherichia coli, and one hypertensive crisis. There were no other cardiovascular events. If all potentially HIV-related events were counted including those occurring after HAART was resumed, the rate of events would be 1.64 events per 100 patients-year (follow up included the time after therapy resume).We included 66 patients. Their features are summarised in Table At the end of the study period, 50 patients (76%) had restarted HAART. The median duration of interruption was 97 weeks range, 17–267). The probability of remaining without HAART is shown in Figure 7–267. ThIn the analysis of response to restarted HAART, we excluded 2 patients whose follow up at the end of study period was <12 weeks; thus, 48 patients were evaluated. Among them, the median follow-up after HAART restarted was 101 weeks . The median CD4+ cell count when HAART was restarted was 266 , and the median VL was 51.400 copies/ml . The probability of remaining with detectable HIV-1 viral load (>50 copies/ml) after reinitiating antiretroviral therapy is shown in Figure The main reason for restarting HAART was the decrease in the CD4+ cell count in 43 patients (88%), HIV-related or possibly related events in 3 (6%), patient’s request in 2 (4%), and pregnancy in 1 (2%). As explained above, there were no B o C events after HAART was restarted, and 2 patients died (Table All patients received 2 nucleoside or nucleotide reverse transcriptase inhibitors (NRTI) plus another drug: a non-nucleoside reverse transcriptase inhibitor (NNRTI), 25 patients; a boosted protease inhibitor (PI), 17 patients; or another drug, 4 patients. In an ITT analysis, the success rate were 84%, 88.2% and 100%; the differences between groups were not statistically significant .The initial regimen was based on the same groups of drugs than the one that was interrupted in 25 patients (52%). Among them, 16 received an initial regimen based on NNRTI; 14 reached undetectable VL, one was changed to PI because of intolerance, and the other was changed to PI because of virologic failure . Eight received an initial regimen based on PI; 7 reached undetectable VL and the other was changed because of intolerance. The other patient was treated with 3 NRTI and reached undetectable VL.The initial regimen was based on a different group of drugs than the one that was interrupted in 23 patients. Among them, the initial regimen after interruption was based in a NNRTI in 9 patients; 7 of them reached undetectable VL, one was changed because of intolerance, and the other needed to be changed to a PI-based regimen because of virologic failure; genotypic test could not be performed. Nine received an initial regimen based on PI; 8 reached undetectable VL, and one failed; mutations found were 62V, 75I, 118I, and 184I (reverse transcriptase gene) and 77I (protease gene). This patient had a low adherence and was still in virologic failure at the end of follow-up. The 4 patients with regimens base on other drugs reached undetectable VL.In an ITT analysis, we found no difference between patients who restarted HAART with a regimen based on the same families of drugs that had been interrupted and those who restarted with a different regimen .The evolution of CD4+ cells is shown in Figure Our study show that long term prognosis of patients who interrupted HAART once for more than 12 weeks, while being in a stable situation, and with the intention to restart HAART once CD4+ cells were ≤300 per μL was good, although some considerations need to be done.The hypothesis for investigating HAART interruptions as a strategy included: the potential effect of viral rebound in stimulating the immunologic response against HIV, and the potential reduction in HAART-related adverse events, in the fatigue of patients, and in the costs of therapy. Structured treatment interruptions, with fixed cycles on and off therapy, have not demonstrated a comparable short-term safety in comparison with continuous treatment . On the Two patients in our cohort died. Both had interrupted HAART on their own, and both reached undetectable VL after restarting therapy. However, one developed a Hodgkin’s lymphoma, and the other had a decompensation of HCV-related liver cirrhosis. Since non-aids-defining malignancies and progression of HCV-related liver fibrosis are conditions in which HAART may have a preventive role , this should be considered in the case of an interruption. Also, the fact that the CD4+ cell count level at the interruption was not reached again after therapy was resumed, is another aspect to be considered.The interruptions analysed in our study occurred in a specific context in the history of HAART: the previous hypothesis that HIV infection could be cured was proved false; the late side effects of some drugs were emerging; and the alternative regimens were scarce. Obviously, the context today is different. However, based on the long term follow-up of our series, and since still most present regimens are based on 2 NRTI plus a NNRTI or PI, we think our data may be useful in the decision-making process and advice to patients when an interruption of HAART has happened or is being discussed. The variables associated to a shorter time for restarting HAART  are sound and in agreement with previous investigations -16, and A key question in patients who interrupt HAART is the possibility of HIV transmission . In our One potential worry after an interruption is whether the probability of reaching again virologic control is compromised. The vast majority of our patients succeeded in controlling VIH replication, although undetectable VL was reached somehow late in many patients. Even though the number of patients is low, we did not find differences between NNRTI- and PI-based regimens, or if the regimen was similar to the one that was interrupted or not. Interruptions of NNRTI-based regimens has been associated to selection of mutations conferring resistant to these drugs in previous studies, although this may be more frequent in the case of multiple interruption ,19 In ouOur study has several limitations that should be considered. First, it was performed in only one center. Second, metabolic parameters, inflammation markers or cardiovascular risks were not assessed, therefore potential implications of the HAART interruption related to these items could not be analysed. And third, we could not compare the data with a control group of matched patients who continued therapy.Our data suggest that restarting HAART when a single interruption occurs is not an emergency and that it is safe in most patients to wait while reinforcing the important of a correct adherence and the alternatives to potential adverse effects of HAART. The risk of non-aids-defining events, worse evolution of coinfections, and transmission of the HIV should be specifically discussed.Two authors of this article, Carmen Machado and Virginia Palomo were recipients of grants from Andalusian Public Foundation for the management of Research in Health of Seville (FISEVI). Five authors, J. Galvez, A. Domínguez, F. Fernández-Cuenca, M A. Muniain and J. Rodríguez Baño received funding for research from Ministry of Science and Innovation, Institute Carlos III of Health - co-financed by European Development Regional Fund "A way to achieve Europe" ERDF, Spanish Network for the Research in Infectious Diseases (REIPI RD06/0008). Anyway, the sources of funding have not had an involvement in the design of the study, the collection of data, the analysis or interpretation of these, in the drafting of the manuscript neither in the decision to send it for publication.FFC carried out the RNA HIV-1 viral load test and resistance testing. CM and VP participated in the data recollection from clinical histories and the inclusion in database MJRV, JGA, ADC, MAM and JRB selected the patients, attended the routine visits, the clinical examination and advised them of any possible higher risk of HIV transmission to sexual partners during the interruption . JRB, MAM and CM participated in the design of the study and performed the statistical analysis. JRB and MJR conceived of the study, and participated in its design and coordination. All authors helped to draft the manuscript, read and approved the final manuscript.


Hepatitis B e Antigen (HBeAg)-negative chronic hepatitis B (CHB) patients have an active liver disease with a high risk of progression to decompensated cirrhosis and hepatocellular carcinoma. The management strategy for HBeAg-negative CHB patients treated with nucleos(t)ide analogues (NUCs) is a topic of concern. To observe the outcomes for this population after NUCs withdrawal, HBeAg-negative CHB patients with loss of hepatitis B surface antigen (HBsAg) or sustained undetectable HBV DNA levels who had discontinued NUCs therapy were included in the study.A total of 66 patients (2 patients with HBsAg loss and 64 patients with sustained undetectable HBV DNA levels) were examined. HBV DNA levels and alanine aminotransferase (ALT) levels were monitored regularly after discontinuation of NUCs therapy. Relapse was defined as HBV DNA levels >2,000 IU/mL while off therapy in at least two determinations more than 4 weeks apart.The time to achieve undetectable HBV DNA levels was 14 weeks (interquartile range (IQR): 12–24 weeks). The time until consolidation therapy was 144 weeks (IQR: 96–168 weeks). No relapses occurred in either of the HBsAg loss patients. Among the 64 patients with undetectable HBV DNA levels, 19 (29.7%) patients demonstrated evidence of relapse. All the relapses occurred within 96 weeks after discontinuation. The median duration of relapse was 36 weeks (IQR: 12–48 weeks). Elevation of HBV DNA and ALT levels over baseline was only observed in 10% of the relapse patients. There were no significant differences among the baseline characteristics  or the time until consolidation therapy between relapse and sustained-response patients.NUC discontinuation is feasible after achieving undetectable HBV DNA levels in HBeAg-negative CHB patients. Prolonging the time until consolidation therapy may be a good strategy to decrease the rate of relapse. More than 96 weeks of sustained response is a predictive marker of long-term sustained response. About 350 million people worldwide live with chronic hepatitis B virus (HBV) infection. Approximately 25% of adults with chronic HBV infection since childhood later die from liver cancer or cirrhosis . TherefoFor HBeAg-negative patients, hepatitis B surface antigen (HBsAg) loss is the ideal endpoint . HoweverFew studies have focused on the long-term outcomes after NUCs withdrawal in HBeAg-negative CHB patients. Based on ten years of data on NUCs therapy in HBeAg-negative CHB patients, we designed this retrospective study to observe the long-term outcomes of therapy discontinuation after achieving ideal or satisfactory endpoints.HBeAg-negative CHB patients presenting for treatment at our hospital from 2002–2008, a total of 64 with sustained undetectable HBV DNA levels and 2 patients with HBsAg loss, were included in this study to observe the outcomes of therapy discontinuation. The patients who started out on one antiviral agent, such as lamivudine, and switched to another agent, such as entecavir, because they did not have a good response or developed resistance were classified by the last drug used. Patients were evaluated at least once every 3 months after discontinuation for ALT and HBV DNA levels. Clinical data were collected, monitored, and entered into a database. The outcomes, including sustained response and relapse, were evaluated during long-term follow-up for at least 24 weeks. Data were collected during follow-up appointments through January 1, 2012.This analysis was conducted on anonymized data, collected as part of routine patient care. No additional investigations were performed. Therefore, no prior informed consent from the patients was required. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki as reflected in a priori approval by the ethics committee of Southwest Hospital, which waived the need for informed consent.Criteria for inclusion included: presence of serum HBsAg for at least 6 months; negative for HBeAg; active CHB; and serum HBV DNA levels >2,000 IU/mL, as measured by LightCycler polymerase-chain-reaction (PCR) assay . An active CHB was defined by a single serum alanine aminotransferase (ALT) level more than 2.0 times the upper limit of normal  or at least two determinations 4 weeks apart of ALT levels 1.0–2.0×ULN.Criteria for exclusion included: coexisting serious medical or psychiatric illness; organ or bone marrow transplantation; recent therapy with systemic corticosteroids, immunosuppressants, or chemotherapeutic agents; a serum alpha-fetoprotein level of at least 50 ng/mL; liver disease that was not due to hepatitis B; and seropositivity for human immunodeficiency virus or hepatitis C or D virus.In this study, two endpoints were considered: the ideal and the satisfactory. The ideal endpoint was sustained off-therapy HBsAg loss with or even without anti-HBs seroconversion. The satisfactory endpoint was sustained undetectable HBV DNA levels and biochemical response for at least 96 weeks.Outcomes after discontinuation were either 'sustained response’ or 'virological relapse’. Sustained response was defined as a sustained off-therapy virological and biochemical response. Virological relapse was defined as off-therapy HBV DNA levels >2,000 IU/mL in at least two determinations more than 4 weeks apart.Routine biochemical tests were performed using automated techniques. HBsAg, antibody to HBsAg, HBeAg, antibody to HBeAg, and antibody to hepatitis B core antigen were detected by ELISA  or electrochemiluminescence . Antibodies to hepatitis C virus, hepatitis D virus, and human immunodeficiency virus were detected by routine, commercially available enzyme immunoassays . Serum HBV DNA levels was quantified with a commercially available PCR assay . Alpha-fetoprotein levels were detected by routine, commercially available colorimetric assay (KHB). The HBV genotypes were determined using a hepatitis B virus gene type PCR fluorescence kit .P values were two-sided, with a level of significance of 0.05. The statistical software used for this analysis was SPSS 18.0.Continuous variables were expressed as mean ± standard deviation (SD) for normal distributions or median and interquartile range (IQR) for abnormal distributions. Pearson’s chi-square tests, Kruskal-Wallis tests, Student’s t tests, ANOVAs, the Kaplan-Meier method, and log-rank tests were carried out as appropriate. All tests for significance and resulting Baseline demographic and clinical characteristics of the patients are shown in Table The time to achieve undetectable HBV DNA levels was 16 weeks (IQR: 12–24 weeks). The time of consolidation therapy was 144 weeks (IQR: 96–168 weeks). The total time of therapy was 160 weeks (IQR: 130–190 weeks). The time to achieve undetectable HBV DNA levels was 18 weeks (IQR: 12–24 weeks) with lamivudine, 16 weeks (IQR: 12–24 weeks) with adefovir, 8 weeks (IQR: 4–20 weeks) with entecavir, and 36 weeks (24 and 48 weeks) with telbivudine. The time of consolidation therapy was 192 weeks (IQR: 144–240 weeks) with lamivudine, 120 weeks (IQR: 96–144 weeks) with adefovir, 120 weeks (IQR: 96–144 weeks) with entecavir, and 96 weeks (96 and 96 weeks) with telbivudine. The time to achieve HBsAg loss in the two patients was 192 weeks and 288 weeks.Relapse was not observed in either of the two patients with HBsAg loss after 96 and 72 weeks. Therefore, the long-term outcomes after therapy discontinuation in this study focused on the patients with undetectable HBV DNA levels.The time of follow-up in sustained response patients was 72 weeks (48–108 weeks). The proportion of relapse was 29.7% (19 of 64) and of sustained response was 70.3% (45 of 64). The distribution of outcomes after withdrawal of lamivudine, adefovir, entecavir, and telbivudine is shown in Table To analyze the safety of relapse, ALT levels and HBV DNA levels were compared between baseline and the time of relapse. ALT levels higher than baseline at the time of relapse were only seen in 5.3% (1 of 19) of relapse patients. HBV DNA levels over baseline at the time of relapse were only seen in 10.5% (2 of 19) of relapse patients. No fulminant hepatitis was seen in any relapse patient. For patients with relapse, baseline and relapse levels of ALT and HBV DNA, as well as the treatment strategy after relapse, are shown in Table We further analyzed the baseline characteristics and therapy time of all patients to identify common features of the relapse patients. All relapse cases occurred within 96 weeks after therapy discontinuation. The time of relapse was 36 weeks (IQR: 12–48 weeks). No significant differences were noted in sustained response compared with relapse patients for time to undetectable HBV DNA levels, time of consolidation therapy, baseline HBV DNA levels, baseline ALT levels, baseline age, sex, or genotype Table .To analyze the influence of multiple factors, we plotted survival functions between cumulative rate of sustained response and time of sustained response after NUCs discontinuation. The cumulative rate of sustained response was 71.2%. Although this analysis was performed using multiple factors, there were no significant differences in the cumulative rate of sustained response when comparing sex, age group, genotype, or baseline ALT group . Second, the majority of patients used adefovir. The majority of NUCs prescribed in China from 2002–2008 were lamivudine and adefovir. Therefore, the number of patients who were treated with telbivudine and entecavir was very small. Based on the data from HBeAg-positive CHB patients treated with NUCs, the difference in serum immunology response rate among different drugs is very small. This suggests that sustained response depended on individual immunity to HBV rather than the different NUCs used.In conclusion, NUCs discontinuation is feasible after achieving undetectable HBV DNA in HBeAg-negative CHB patients. Prolonging the time of consolidation therapy after achieving undetectable HBV DNA may be a good strategy to decrease the rate of relapse. More than 96 weeks of sustained response is a predictive marker for long-term sustained response.The authors declare that they have no competing interests.DH and WC performed the experiments. XC, GY, ML, SG, PZ, JW, and HH provided patients’ material and clinical data. Wang designed the study. DH and SG analyzed the data and wrote the paper. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2334/13/458/prepub


Most HIV treatment programs in resource-limited settings utilize multiple facilitators of adherence and retention in care but there is little data on the efficacy of these methods. We performed an observational cohort analysis of a treatment program in Kenya to assess which program components promote adherence and retention in HIV care in East Africa.Patients initiating ART at A.I.C. Kijabe Hospital were prospectively enrolled in an observational study. Kijabe has an intensive program to promote adherence and retention in care during the first 6 months of ART that incorporates the following facilitators: home visits by community health workers, community based support groups, pharmacy counseling, and unannounced pill counts by clinicians. The primary endpoint was time to treatment failure, defined as a detectable HIV-1 viral load; discontinuation of ART; death; or loss to follow-up. Time to treatment failure for each facilitator was calculated using Kaplan-Meier analysis. The relative effects of the facilitators were determined by the Cox Proportional Hazards Model.301 patients were enrolled. Time to treatment failure was longer in patients participating in support groups , pharmacy counseling , pill counts  and home visits . Better adherence was seen with support groups  and pill counts . Multivariate analysis using the Cox Model found significant reductions in risk of treatment failure associated with pill counts  and support groups .Unannounced pill counts by the clinician and community based support groups were associated with better long term treatment success and with better adherence. The roll out of anti-retroviral therapy in resource-limited setting has resulted in millions of individuals now receiving life saving therapy There is a need to identify effective methods to retain patients in care and promote adherence to HIV regimens In sub-Saharan Africa, a number of evidence based interventions to retain patients in care and to foster adherence to HIV medications have been reported We performed a prospective, observational cohort analysis of a single medical center that utilizes tiered approach retention in care and adherence promotion AIC Kijabe Hospital is a 265 bed hospital located 60 kilometers northwest of Nairobi, Kenya. It follows approximately 6000 patients on anti-retroviral therapy at six locations in Central Kenya. Kenya utilizes a catchment system of care for HIV patients and thus only those who live within a 40 km radius of the hospital are eligible for enrollment into the treatment program. The program utilizes a wide array of personnel to provide care that include consultant physicians, medical officers and clinical officers. Nurses assist in care in the clinics and coordinate community health workers. A community heath program using nurses and community health workers acts as a liaison between the clinics and the patients and manages community-based facilitators of adherence and retention into care. All medications are dispensed by the Kijabe pharmacy by a pharmacist. Medical records are abstracted and entered into an electronic database.The Kijabe HIV program utilizes the highest tiered approach to facilitate adherence and promote retention into care described by Etienne et. al. A schematic of the treatment program and of the study are shown in Patients learn about HIV infection, treatment, the importance of adherence and of programmatic requirements. There are three required classes over 3 weeks, each lasting about one hour.Patients are required to identify a treatment buddy who is typically someone who has already been in treatment to encourage and assist with medication adherence.Patients sign a contract agreeing to participate in both the community program and in the medical care provided by the program.Home Visits. Within one month of initiating ART, community health workers visit patients in their homes and assess barriers to care, patient adherence to medication and overall health status. Patients are referred back to the clinic if acute medical problems are encountered. Repeat visits are scheduled each month as need.Support Groups Meetings. Support group meetings are held monthly in each of the six local communities where there are clinic sites. Meetings are typically held in local churches. Community health workers organize the support group. Patients attend with their treatment buddies. There are open discussions of both problems and successes associated with HIV therapy.Patients must come to the clinic on a monthly basis for the first six months. They undergo a medical evaluation by their care provider.Unannounced Pill Counts. Patient providers count pills to assess their adherence and to provide a context for adherence counseling.Pharmacy Counseling. After the care provider visit, the patient meets with a pharmacist to discuss any difficulties in taking medication and to further address adherence issues discovered during the provider visits.Study participants were recruited from AIC Kijabe Hospital's HIV Program.Eligibility requirements for the study were:HIV positive by Kenyan National AIDS and STI Control Program Criteria.Age greater than 18 years old.Anti-retroviral naïve with the exception of women who had previously received medication as part of prevention of mother to child transmission.Completion of anti-retroviral treatment preparation course.Identification of a Treatment buddy.Signed treatment contract.Providing informed consent.This study was approved by the A.I.C. (African Inland Church) Kijabe Hospital Ethics Board, and the Kenya Medical Research Institute National Ethics Committee and the University of Texas Medical Branch Institutional Review Board. All subjects provided written informed consent prior enrollment into the study. Consent forms were available in both English and Swahili. For subjects who could not read either language, the consent form was explained to them by native speakers in their mother tongues.This was a prospective, observational cohort study that did not involve an intervention.The objective of this study was to compare the relative effect of participation in ongoing facilitators of retention into care and adherence that occur after treatment initiation on time to treatment failure. Specific facilitators evaluated were:Home visitsSupport Groups MeetingsPharmacy CounselingUn-announced Pill CountsThe primary outcome was defined as time to treatment failure from date of initiation of anti-retroviral medication. Treatment failure was defined as any one of the following:Virologic failure: defined as failure to achieve an HIV-1 RNA less than 200 copies/ml at 6 months or an HIV-1 RNA greater than 200 copies/ml at one year after achieving an undetectable viral load.Lost to Follow Up: Defined as not appearing for any treatment program activities for 90 days and inability of the community health workers to find the subject.Discontinuation of Therapy: Defined as not picking up prescriptions for ninety days.Death: verified by clinical personnel or by community health workers performing home visits.Participants were followed for one year after initiation of ART. Data was primarily collected from the patient medical record. Demographic data was collected from participant charts at baseline. CD4 counts were performed at initiation of therapy following standard of care. Viral loads were measured on study participants at baseline, 6 months, and 12 months. Data was managed using Microsoft Access. Dispensation of anti-retroviral drugs was collected from months 1 to 12. Participation in each of the key activities of the study was collected at each month.Kijabe utilizes patient treatment logs for both clinical and administrative purposes and these were used to confirm patient participation in study activities. All participants in support groups were required to sign an attendance sheet at the beginning of each meeting. Confirmation of home visits was confirmed by reviewing travel receipts for community health workers. Pharmacists confirm counseling visits in the dispensation record. Pill counts were solely recorded in the clinic record at the time the patient was seen by the clinician.Blood samples were drawn from participants at baseline, 6 months, and 12 months. Serum was separated and stored in a −80 freezer for batched HIV-1 RNA testing. HIV-1 RNA testing was performed at AIC Kijabe Hospital using the ExaVir HIV-1 RT assay . This is an HIV-1 RT assay that correlates well with standard HIV-1 PCR and is widely used in resource-limited setting 5.Preliminary data from a quality improvement project performed in 2008 found that 85% of HIV positive adults at Kijabe who had been on anti-retroviral therapy from 9–18 months had an undetectable HIV-1 viral load. The same study found a drop out rate of 10% for subjects who were initiated on anti-retroviral therapy. From this data, we assumed that 25% of subjects would be treatment failures in our study. Based on this, 280 subjects would provide an 80% power at a significance level of 0.05 when 5 independent variables are included; corresponding to an odds ratio of 2.The primary endpoint was time to clinical failure. Failure was defined as being lost to follow-up, death, or having a detectable viral load at the end of study follow-up. The relationship between each patient activity and treatment success or failure was evaluated using Pearson's CHI squared test, Fishers exact test or student t test where appropriate. Adherence to medication was collected by review of pharmacy refill records for each participant using the method described by El-Khatib The effects of participation in study activities on treatment failure were compared in two ways: first as a (Yes/No) and then a cut point between the differences in the medians between of treatment successes and failures. Those factors that were statistically significant (P<0.05) were further evaluated by comparing time to treatment failure. Significant differences between treatment failure and success were further evaluated using Cox Proportional Hazard regression models. The level of statistical significance utilized was p<0.05Participant Flow is shown in Time to treatment failure is shown in Multivariate analysis of the time to treatment failure using the Cox Proportional Hazards model is shown in There is an urgent need to identify effective facilitators of adherence and retention to care in resource limited settings. Observational studies can help identify potentially effective means of promoting successful treatment outcomes and can direct future investigations. Many studies are retrospective and/or use retention in care as the primary endpoint A significant finding of this study is that pill counts were associated with better treatment outcomes. Although pill counts have been widely studied as tool to measure adherence, there has been little evaluation of their use as facilitators of adherence. In our study, we examined only whether a pill count was performed but did not collect data on what the actual pill count was. We found that the number of times a pill count was performed as associated with both better adherence and better treatment outcomes, suggesting that intermittent pill counts may have a motivational effect on patients that result in better outcomes. In addition, pill counts are easy to perform, do not require special technology and are easily implemented across a wide spectrum of settings.Our data has important implications for future research and for allocation of both clinical and research resources. Performance of pill counts by the clinician would seem to be a relatively easy and effective method for promoting adherence. Community based support groups require planning, personnel and travel, but also have a demonstrated clinical benefit. On the other hand, counseling by a pharmacist was not associated with clinical benefit nor did regular home visits seem to improve outcomes. The pharmacy counseling requires the time of a pharmacist and home visits require both community health workers time and travel. It may be that these interventions are not necessary for routine patient care and are best targeted for patients who have specific needs.The results must be interpreted with caution. The study design is observational and thus there is potential for un-measured biases that could affect the outcome. An underlying assumption of the study was that each of the facilitators evaluated were independent predictors of treatment outcome. We were able to demonstrate associations between these interventions and did not find any interaction between significant facilitators on multivariate analysis. There was also no association between the total number of facilitators that subjects participated in and outcome, suggesting that the results were due to the individual factors evaluated and not to the total system of care. In addition, the study was performed within a single organization with multiple sites in rural Kenya and it is not clear how generalizable the results would be to other systems of care in other settings. We also did not perform a cost benefit analysis hence any discussion of cost effectiveness of the interventions would be speculative.However, statistical associations do not demonstrate causality and further evaluations of our findings are warranted. The best way to evaluate the effectiveness of these interventions would be with a cluster-randomized trial using multiple sites. In such a design, one site would perform pill counts, another would perform have support groups in the community setting and a third would perform post clinic pharmacy counseling. The effect on clinical failure of each intervention would be determined. This would also allow for qualitative and quantitative assessments of the social, cultural and spiritual influencers on adherence and test interventions to address those influencers on the markers of failure and success.A formal cost analysis could also be performed and the cost benefit of each intervention could be determined. Such a strategy would allow for the identification of the most cost effective interventions that could then be replicated in other settings.


Atazanavir increases plasma bilirubin levels in a concentration-dependent manner. Due to less costly and readily available assays, bilirubin has been proposed as a marker of atazanavir exposure. In this work, a previously developed nomogram for detection of suboptimal atazanavir exposure is validated against external patient populations. The bilirubin nomogram was validated against 311 matching bilirubin and atazanavir samples from 166 HIV-1-infected Norwegian, French, and Italian patients on a ritonavir-boosted regimen. In addition, the nomogram was evaluated in 56 Italian patients on an unboosted regimen. The predictive properties of the nomogram were validated against observed atazanavir plasma concentrations. The use of the nomogram to detect non-adherence was also investigated by simulation. The bilirubin nomogram predicted suboptimal exposure in the patient populations on a ritonavir-boosted regimen with a negative predictive value of 97% (95% CI 95–100). The bilirubin nomogram and monitoring of atazanavir concentrations had similar predictive properties for detecting non-adherence based on simulations. Although both methods performed adequately during a period of non-adherence, they had lower predictive power to detect past non-adherence episodes. Using the bilirubin nomogram for detection of suboptimal atazanavir exposure in patients on a ritonavir-boosted regimen is a rapid and cost-effective alternative to routine measurements of the actual atazanavir exposure in plasma. Its application may be useful in clinical settings if atazanavir concentrations are not available. Like other protease inhibitors (PI), atazanavir displays large interindividual pharmacokinetic variability resulting in variable drug exposure between patients. Trough concentrations above the minimal effective concentration (MEC) of 0.2 μmol/L 150 ng/ml) are recommended as a suitable target for atazanavir plasma monitoring 50 ng/ml .et al. reported that average bilirubin increase from baseline, in patients on atazanavir monotherapy, was significantly lower in subjects with virological failure compared to those without whereas there were no differences in atazanavir Ctrough between the two groups  model . Based on = 56) and unboosted (n = 56) patients were included, but the data were analyzed separately. All patients on an atazanavir/ritonavir regimen were on a 300/100-mg QD regimen except two who were on a 200- and 400-mg QD-based regimen, respectively. The regimens of the unboosted patients varied from 200 mg BID to 400 mg QD. The backbone therapies varied according to clinical practice. The bilirubin steady-state samples were collected on average at 09:23 (±1:05), while baseline samples were collected between 8:00 and 11:00 am.The Italian patients were part of the therapeutic drug monitoring programs at University of Torino. Both ritonavir-boosted (n = 76) were part of the HIV monitoring program at Oslo University Hospital. The data were extracted from the Thematic Biobank “Infectious Diseases.” All patients were administered an atazanavir/ritonavir (300/100 mg QD)-containing regimen. The backbone therapy varied according to clinical practice. The average times for bilirubin baseline and steady-state sampling were 10:13 am (±1:38) and 09:44 (±1:22) am, respectively.The Norwegian patients . Observations correctly and incorrectly identified as below MEC were considered to be true positives (TP) and false positives (FP), respectively. Observations correctly and incorrectly identified as above MEC were considered to be true negatives (TN) and false negatives (FN), respectively.The bilirubin baseline and steady-state levels were analyzed with the bilirubin–atazanavir nomogram. Individual baseline bilirubin concentrations were plotted on the e.g., a HIV test. A positive test result can indicate presence of some disease, in contrast to a negative test result which indicates absence of the disease in question. In this case, a positive nomogram result indicates presence of suboptimal exposure or non-adherence. A number of statistical metrics, commonly used to evaluate the performance of diagnostic tests, can be calculated based on the TP, FP, TN, and FN values, e.g., specificity, sensitivity, accuracy positive predictive value (PPV), and negative predictive value (NPV)  Table  10, 11), 11e.g.,In order to evaluate if the nomogram can predict non-adherence, it was applied to data from 1,000 simulated patients of whom 10% were non-adherent to therapy. Simulations of three scenarios were performed Fig. .Fig. 1StScenario 1 describes a patient forgetting or actively deciding not to take one single atazanavir dose after a period of full adherence to therapy. Patients not adherent to treatment during three consecutive days subsequent to a period of full adherence are portrayed in scenario 2. In scenario 3, it was assumed that the patient is not adhering to therapy for an extended period of time with seven consecutively missed doses following a period of full adherence. In all three scenarios, it was assumed that the patient was scheduled for an atazanavir plasma monitoring event before the scheduled dosing time at the day following a past period of non-adherence (event 1a), or on the second day after a non-adherence period (event 2). After the period of non-adherence, the patients are completely adherent to therapy. To account for situations where patients adhere to therapy in connection to clinic visits, a separate event was simulated for each scenario where the patient takes an atazanavir dose 1 h before the scheduled clinic visit without informing the physician (event 1b). In all scenarios, bilirubin at baseline and atazanavir plasma concentrations with matching bilirubin concentrations at steady state were simulated for all patients at the described monitoring events.Patients were considered to be non-adherent when the nomogram identified a simulated bilirubin sample below the cutoff corresponding to an atazanavir plasma concentration below the MEC. Similarly, patients were considered non-adherent when simulated atazanavir plasma concentrations were below MEC. Correctly identified non-adherent and adherent patients were labeled as TP and TN, respectively. Incorrectly identified non-adherent and adherent patients were labeled FP and FN, respectively. The ability to detect non-adherence from bilirubin levels of the nomogram and atazanavir plasma measurements were compared. All simulations were performed using NONMEM 7  with PsN version 3.4.2 , 13.The epiR package in R (2.14) was used for calculation of specificity, sensitivity, accuracy, PPV, and NPV for the external and simulation-based validation Table . The NorOut of 311 atazanavir observations in 166 patients on a ritonavir-boosted atazanavir regimen, 294 were above MEC. The nomogram identified 267 of these bilirubin observations correctly (TN) while 27 observations were incorrectly identified as below MEC (FP). Seven observations were incorrectly identified as above MEC (FN) while ten observations were correctly identified as below MEC (TP). Metrics for the unboosted and the ritonavir boosted subpopulations are shown in Table The predictive properties of the bilirubin nomogram and when using measured atazanavir concentrations in assessing treatment adherence are shown in Fig. PPV at events 1a and 1b was higher for atazanavir plasma measurement compared to the nomogram. However, in each scenario, the atazanavir plasma measurement was less reliable at events 1b and 2, identifying only zero and eight out of a total of 100 non-adherent patients, respectively. This is reflected by the wide confidence intervals for PPV in Fig. vs. 100% (95 CI 95–100). At event 1b, the nomogram outperformed the atazanavir plasma measurement in all scenarios, 84% (95% CI 75–90) vs. 8% (95% CI 4–15). At event 2, both methods performed poorly in terms of sensitivity (<16%).Sensitivity at event 1a was higher for atazanavir plasma measurement compared to the nomogram, 88% (95% CI 80–94) Accuracy decreased slightly going from event 1a to 1b and 2 in all scenarios for the atazanavir monitoring method. The nomogram performed more consistently at different events albeit with slightly lower accuracy. Using the atazanavir plasma measurement, specificity was consistently very high (>99%) at all events and scenarios while the nomogram performance was somewhat lower.Although not routinely recommended, plasma monitoring of atazanavir has been suggested as a tool for improving antiretroviral efficacy and safety , 2. The y- and x-axes of the nomogram represent bilirubin levels at steady state (after atazanavir treatment initiation) and at baseline (before treatment initiation), respectively. In brief, a bilirubin baseline sample should be collected between 9 am and 3 pm before initiation of therapy since bilirubin levels are stable between those hours  area of the nomogram, then the sample is expected to correspond to a drug concentration below the MEC value for atazanavir exposure. In all other areas of the nomogram, the sample is considered to correspond to a drug concentration above the MEC value. Factors known to influence bilirubin can have a confounding effect on the nomogram predictions. The most influential confounder would be one causing elevated bilirubin levels and thereby increasing the risk of false negatives, i.e., failure to identify a patient below the MEC. Gilbert's syndrome is such a confounder, where the UGT1A1 gene allele *28 has been shown to increase the risk of hyperbilirubinemia with some PI-based treatments . It should be noted that the NPV can be affected by the prevalence of suboptimal exposure and vary amongst populations with different prevalence. Here, the prevalence of suboptimal atazanavir exposure was similar for the Norwegian, Italian, and French populations: 4%, 5%, and 7%, respectively. The reason for the small discrepancy between French and Italian/Norwegian patients could be attributed to the study design. The French patients were included in a clinical trial and observed at four occasions within 6 months of follow-up after treatment initiation. The Italian and Norwegian patients were part of a monitoring program and had only a few observations per individual. Consequently, a consistently non-adherent patient would influence the prevalence estimate more in the French than in the Norwegian/Italian population.The nomogram was designed to be used as a first screening tool for suboptimal exposure and non-adherence. Given the nature of drug monitoring as a screening tool for suboptimal exposure and non-adherence, it is arguable that the confidence in a negative result (exposure over MEC) is of greater importance than the confidence of a positive result (exposure below MEC). A sample identified by the nomogram as positive can be reanalyzed, and the atazanavir concentration can be quantified, giving a definitive answer if the low bilirubin sample was a true- or a false-positive result in comparison to atazanavir concentration.The nomogram was developed using data from ritonavir-boosted patients. As evident by the current data, the nomogram performs poorly when used on patients on an unboosted atazanavir regimen. Ritonavir-boosted patients are known to have higher bilirubin levels than unboosted which also was observed here . RitonavSince the reason for low atazanavir exposure in the external dataset was unknown, a simulation study based on the developed PKPD model was designed where 10% of the virtual patients were non-adherent to therapy. Three different clinically plausible scenarios of non-adherence were evaluated under three different sampling schedules. The predictive properties of the nomogram were compared to the simulated atazanavir plasma concentrations. In general, there were no differences between the scenarios; furthermore, the nomogram and the plasma concentration measurements were similar in their ability to predict non-adherence in patients in the ideal case when the patient is sampled directly after the period of non-adherence (event 1a). If a patient decides to take an atazanavir dose before the sampling event without informing the physician, the atazanavir plasma measurement would not indicate non-adherence whereas the nomogram would (event 1b). Neither of the two approaches was successful in detecting non-adherence when the sampling event took place 48 h after a period of non-adherence (event 2). These results were expected since bilirubin half-life was previously estimated at 8.2 h when atazanavir is at steady state . For the ideal case, the NPV was estimated above 98% for both the methods resulting in good predictive properties. The use of atazanavir plasma monitoring as a tool to motivate adherence in patients has not been addressed here. The simulation study was solely designed to verify if non-adherence could be detected. However, other sources of unexpected low plasma exposure such as food–drug and/or drug–drug interactions could also be explored in a similar manner.In conclusion, this work demonstrates that the bilirubin nomogram is a rapid, cost-effective, and useful alternative to atazanavir plasma concentration monitoring for the use as a screening tool for suboptimal atazanavir exposure and non-adherence in patients. The bilirubin nomogram may diminish some obstacles in implementing drug monitoring of atazanavir-based antiretroviral therapy, such as lack of laboratories and the high cost of atazanavir measurement. The simplistic design of the nomogram and the straightforward interpretation of the results should facilitate its implementation in clinical practice.


Low levels of serum selenium are associated with increased risk of mortality among HIV+ patients in East Africa. We aim to assess the effect of selenium supplementation on CD4 cell count, HIV viral load, opportunistic infections, and quality of life in HIV-infected patients in Rwanda.3, and not yet on ART will be recruited. Patients will be randomized at each study site using a randomized block design to receive either the selenium micronutrient supplement or an identically appearing placebo taken once daily. The primary outcome is a composite of time from baseline to reduction of CD4 T lymphocyte count below 350 cells/mm3 (confirmed by two measures at least one week apart), or start of ART, or the emergence of a documented CDC-defined AIDS-defining illness. An intention-to-treat analysis will be conducted using stepwise regression and structural equation modeling.A 24-month, multi-centre, patient and provider-blinded, randomized, placebo-controlled clinical trial involving 300 pre-antiretroviral therapy (ART) HIV-infected patients will be carried out at two sites in Rwanda. Patients ≥ 21 years of age with documented HIV infection, CD4 cell count of 400-650 cells/mmMicronutrient interventions that aim to improve CD4 cell count, decrease opportunistic infections, decrease HIV viral load, and ultimately delay initiation of more costly ART may be beneficial, particularly in resource-constrained settings, such as sub-Saharan Africa. Additional trials are needed to determine if micro-supplementation can delay the need for more costly ART among HIV-infected patients. If shown to be effective, selenium supplementation may be of public health importance to HIV-infected populations, particularly in sub-Saharan Africa and other resource-constrained settings.NCT01327755 In sub-Saharan Africa, more than 30 million people are living with HIV/AIDS, malnutrition and food insecurity are endemic  HIV infeIt is well understood that micronutrient deficiencies and HIV disease progression aggravate each other ,11. AmonWhile many observational studies on selenium in HIV-infected patients have been conducted in developed countries over the past decade, few have been conducted in sub-Saharan Africa. Clinical trial data on selenium supplementation is also limited in both developed and developing countries. There are two recent high-impact randomized clinical trials (RCTs) that have been conducted to assess the individual association of selenium supplementation on HIV viral load and CD4 cell count. The first trial, conducted in Miami by Hurwitz et al , found tAlthough the results of these two clinical trials appear to contradict, it is important to recognize the inherent differences in their design, setting, and populations under study. Additional evidence from other settings and populations is still required to more accurately determine the effect of selenium supplementation on HIV viral load and CD4 cell count in HIV-infected individuals. Therefore, we have designed a randomized trial to examine the effect of selenium supplementation on CD4 cell counts in HIV-infected patients who are not yet on ART. This trial will take place in Rwanda, where an estimated 250,000 adults and children are living with HIV , of whicGlobal Benefit has sponsored this trial. The trial sponsors and investigators will make financial assurance statements to the concerned bodies  on the availability of funds for the completion of the trial.NCT01327755.This trial has been registered with ClinicalTrials.gov. The registration number is This study is a 24-month, multi-centre, patient, provider and analyst-blinded, randomized, placebo-controlled clinical trial involving 300 pre-ART HIV-infected patients in Rwanda. , or start of ART, or the emergence of a documented CDC-defined AIDS-defining illness. We hypothesize that selenium supplementation in pre-ART patients will improve CD4 cell counts, decrease opportunistic infections, decrease HIV viral load, and delay ART initiation.The primary outcome of the study is time from baseline to reduction of CD4 T lymphocyte count below 350 cells/mmPatients will be recruited at two purposely-selected health facilities that offer care and treatment for HIV/AIDS patients in Rwanda. These facilities have been chosen due to the feasibility of recruiting all patients within a 3-4 month period and the feasibility of coordination.3 will be selected because they are at similar immunological level and hence they will not be eligible to start ART treatment at the start of the study. Eligible participants must also be willing to practice a barrier method of birth control at all times. Written informed consent will be required from patients to participate in this study.Patient eligibility will be restricted to HIV-infected adults 21 years of age and older at study enrolment. Only patients not yet eligible for ART will be included. Only patients with CD4 cell count between 400 and 650 cells/mmEligible patients will be identified from patient registers. These patients will be informed about the study during a regular scheduled clinic visit or through home visits by site staff not otherwise involved in data collection. Patients who are interested in the study will be provided with further details and consent procedures. Those fitting the inclusion criteria will be enrolled and followed for 2 years. Study assessments will occur at baseline, 6, 12, 18, and 24 months.Patients will be excluded if they are intending to be transferred out of the clinic catchment area before study ends and/or if they are scheduled to start ART. Moribund patients, pregnant women, and those unable or not wanting to commit to barrier method of birth control will also be excluded.Participants will be randomized using a simple randomized block design to receive either selenium or an identically appearing placebo to be taken once daily for 24 months. The Rwandan Ministry of Health recommends the use of co-trimoxazole, a sulfonamide antibiotic combination of trimethoprium and sulfamethoxazole used for the treatment of a variety of bacterial infections, for all HIV-infected patients. Therefore, all participants will also receive co-trimoxazole irrespective of experimental assignment. Participants who do not return to the clinic as scheduled will be followed up at home.Study participants will be identified by a unique study identification number. The randomization schedule will be prepared by the product manufacturer, Seroyal, and the randomization sequence will be concealed from the study investigators and providers. Study participants will be assigned a specific allocation number. An unblinding list will be kept at the site of the manufacturer and will be provided as needed to the Data and Safety Monitoring Officer (DS). Site study personnel  and study participants will be blinded with respect to the allocation. Unblinding of an individual study participant will be indicated in the event of a medical emergency where the clinical management/medical treatment of the study participant could be altered by knowledge of the group assignment allocation of the investigational product.The trial intervention will consist of capsules containing 200 mcg of selenium in the form of selenium yeast, which contains selenomethionine. Capsules will be provided in bottles of 30 (one months supply) and participants will be instructed to take one capsule daily. Selenium will be stored in a dry cool location during the course of the trial. Shelf life of the product is well beyond the two-year time period expected for the trial duration. To ensure optimal adherence, participants will receive adherence counseling at baseline and when picking up refills on a monthly basis. Additional adherence counseling will be provided to patients who have sub-optimal adherence.Selenium capsules are supplied by Seroyal, a Canadian Nutraceutical company. The sponsors of the trial and the investigators will be responsible for ensuring that the investigational pharmaceutical product is safe. Comparator products (placebo) supplied for the clinical trial will be of proven quality and will be available to be verified by the National Bureau of Standards. Records will be kept of information about the shipment, delivery, receipt, storage, return, and destruction of any remaining pharmaceutical products. The investigators will not supply the investigational product to any person not targeted to receive it.3 (confirmed by two measures at least one week apart), or start of ART, or the emergence of a documented CDC-defined AIDS-defining illness. Secondary outcomes includes viral suppression at 6, 12, 18, and 24 months; quality of life; weight gain; presence of opportunistic infections and mortality.The primary outcome of the study is a composite outcome involving reduction of CD4 T lymphocyte count to below 350 cells/mmTrained nurses will use a structured questionnaire to collect data on patients' demographics at baseline. Additionally, at baseline and at each follow-up visit, a questionnaire will be used to collect information on psychosocial factors, access to care and treatment, attitudes towards and experiences with the supplementation, quality of life, self-efficacy, nutrition, opportunistic infections, and adherence to the study protocol. The questionnaire will be available in English and Kinyarwanda. Nurses will also use a data abstraction tool at baseline and at each follow-up visit to obtain information on height, weight, blood pressure and other clinically relevant information.At baseline, and at month 6, 12, 18 and 24, blood draws will be done for assessment of CD4 cell count and HIV viral load. Blood samples will be collected into EDTA tubes . The viral load samples will be transported and analyzed at the National Reference Laboratory within 4 hours; plasma will be separated from cells by centrifugation at 200 g, aliquoted and stored at -70°C until the real time PCR will be performed using Cobas TaqMan 48  that has a detection limit of 40 RNA copies/ml. The RNA will be extracted from the plasma with chloroform, followed by alcohol precipitation and dilution with the HIV Monitor specimen diluent (100 μl). Amplification and detection of the extracted RNA (40 μl) will be performed in accordance with the manufacturer's instructions. CD4 cell count will be measured at the National Reference Lab, or the site laboratory, using FACS Count (Becton Dickinson immunocytometry).We chose our CD4 depletion event rate based on the work of the CASCADE cohort which displays an average CD4 depletion of 114 (32-229) cells/μl per year and where 54% of individuals had a decline > 100 cells/μl per year if CD4 evaluations were rare, as in Rwanda . We expeWe applied several sample sizes to display that even small changes in the relative risk of the intervention will yield a large impact on the required sample size  reporting as defined in the Division of AIDS (DAIDS) AE Manual. At this level, this study will report all AEs following any exposure to the investigational product. AE follow-up will be reported on a standardized form during the protocol-defined AE reporting period, which will be the entire study duration for an individual participant (from study enrollment until study completion or study discontinuation of a participant for any reason). After the end of the protocol-defined AE reporting period, sites will report serious, unexpected, clinical suspected adverse drug reactions or if the study site staff becomes aware of the event on a passive basis (e.g. from publicly available information).AEs will be managed in accordance with good medical practices by the clinical study team who will assess and treat the study participant as appropriate, including referral. All study participants experiencing AEs, regardless of severity, will be followed until satisfactory resolution, return to baseline, or until the toxicity is presumed to be irreversible. If at the end of the study, an AE  which is considered possibly, probably or definitely related to the investigational product is unresolved, follow-up will continue until resolution if possible and the study participant will be referred. If treatment and medical care is required as a result of harm caused by the investigational product or study procedures, this will be provided free of charge to the participant.This trial will be conducted in compliance with the protocol approved by the institutional review boards of the Canadian College of Naturopathic Medicine and Wilfred Laurier University in Canada, and the National Ethics Committee (NEC) in Rwanda. No deviation from the protocol will be implemented without the prior review and approval of the IRB except where it may be necessary to eliminate an immediate hazard to a participant. In such case, the deviation will be reported to the IRB as soon as possible.A signed consent form will be obtained from each participant. The consent form describes the purpose of the trial, the procedures to be followed and the risks and benefits of participation. A copy of the consent form will be offered to the participant. Trial participants will receive 1,000 Rwandan Francs  per month for their travel to and from the clinic for interviews, lab work-up and picking up their supplement. In addition to this compensation, in the event that the study shows benefit, Global Benefit  is committed to supplying free supplementation to all participants in the trial for a period of at least one year following study completion.The sponsor and investigators will be responsible for implementing and maintaining quality assurance and quality control systems with written standard operating procedures to ensure that the trial is conducted and data are generated, documented, and reported in compliance with the protocol, good clinical practice, and the applicable regulatory requirements. The sponsor will also be responsible for securing agreement from all involved parties to ensure direct access to all trial related sites, source data and documents, and reports for the purpose of monitoring and auditing by the sponsor, and inspection by domestic and foreign regulatory authorities.Micronutrient interventions that aim to improve CD4 cell count, decrease opportunistic infections, decrease HIV viral load, and ultimately delay initiation of more costly ART may be beneficial, particularly in resource-constrained settings, such as sub-Saharan Africa. It has been shown that selenium is deficient in HIV-infected populations ,28. For AE: adverse event, AIDS: acquired immune deficiency syndrome, ART: antiretroviral therapy, CD4: cluster of differentiation 4, DAIDS: Division of AIDS, EAE: early adverse event, FDA: US Food and Drug Administration, HIV: human immunodeficiency virus, IRB: institutional review board, ITT: intention to treat, NEC: National Ethics Committee; NRL: National Reference Laboratory, PCR: polymerase chain reaction, RNA: ribonucleic acid, SEM: structural equation modeling.The authors declare that they have no competing interests.JK, VM, JK, JCK, RS, HF, AU, MB, CN, AM, JPH, JN, DH, VM, JBN, PN, EM, EJM, DS, DJM, DW conceived and designed the study. JK, VM, JK, JCK, RS, HF, AU, MB, CN, AM, JPH, JN, DH, VM, JBN, PN, EM, EJM, DS, DJM, DW interpreted the results of the literature search. JK, VM, JK, JCK, RS, HF, AU, MB, CN, AM, JPH, JN, DH, VM, JBN, PN, EM, EJM, DS, DJM, DW drafted the manuscript. All authors read and approved the final manuscript.


Since the use of more tolerable and less toxic combined antiretroviral (ARV) therapy, most drug-naïve HIV patients achieve viral suppression and immunologic recovery, combined with less AIDS related events. Nevertheless, drug switches are still frequent both as a mean of adherence and toxicity management and as a response to virologic or immunologic failure. The aim of the study was to analyse the number, timing and cause of modifications in the first ARV regimen in order to elucidate more about adverse drug reactions in the current HIV ARV therapy guidelines, indirect signs of adherence and premature virological failure.Non-controlled, observational, retrospective study, based on the clinical files and on a national questionnaire audit for all naïve-patients that began cARV therapy between January 2007 and Mars 2010, followed in an Infectious Diseases Clinic in a central Hospital in Lisbon. SPSS 15.0 was used for statistical analysis.During study period, 69 patients of the 285 naïve-patients who started ARV therapy changed their regimen, 64% were male, with a median age of 43 years old. A significant group was born in African Portuguese speaking countries (30%). Most switches occurred on the first 6 months (n=42), 22% on the first month and just 11% after one year of treatment, with more than one modification in 15% of patients. The drug regimen prior to modification included a NNRTI in 62% of the patients. The back-bone regimen included TDF/FTC in 66%, ABC/3TC in 12% and still AZT/3TC in 22% of them. Adverse drug reactions were the most frequent cause of therapy modification (59%), including toxicity in 19 cases and intolerability in 22, reflecting the known side effects of the drugs. Other switch causes were the evidence of virological failure (15%), the simplification of the regimen (10%) and the adjustment during pregnancy (5%). About a fifth of the patients had adherence irregularities. The rate of viral suppression at week 24 of ARV was significantly lower in the group of patients who switched ARV , which had also worse immunological recovery at 24 and 48 weeks of ARV.Toxicity and intolerability remain the main reasons to change the first ARV regimen, more frequently during the first to sixth months of therapy, witch reinforce the need to evaluate early events that can compromise the adherence, the emergence of resistances and long term toxicity and longevity of this patients.


The development of HIV drug resistance and subsequent virological failure are often cited as potential disadvantages of early cART initiation. However, their long-term probability is not known, and neither is the role of duration of infection at the time of initiation.Patients enrolled in the UK Register of HIV seroconverters were followed-up from cART initiation to last HIV-RNA measurement. Through survival analysis we examined predictors of virologic failure (2HIV-RNA ≥400 c/l while on cART) including CD4 count and HIV duration at initiation. We also estimated the cumulative probabilities of failure and drug resistance (from the available HIV nucleotide sequences) for early initiators (cART within 12 months of seroconversion).3 and HIV duration 3  years, virological failure occurred in 163 (15%). Higher CD4 count at initiation, but not HIV infection duration at cART initiation, was independently associated with lower risk of failure (p=0.033 and 0.592 respectively). Among 230 patients initiating cART early, 97 (42%) discontinued it after a median of 7 months; cumulative probabilities of resistance and failure by 8 years were 7%  and 19% , respectively.Of 1075 starting cART at a median (IQR) CD4 count 272  cells/mmAlthough the rate of discontinuation of early cART in our cohort was high, the long-term rate of virological failure was low. Our data do not support early cART initiation being associated with increased risk of failure and drug resistance. Some major treatment guidelines currently recommend initiation when CD4 cells count drops below 350 cells/mmon  adjusting for the potential effects of: age, and CD4 count  at cART initiation, sex, risk group, class of initial cART, calendar year of initiation (<2000 vs. ≥2000), and diagnosis during acute HIV infection . The choice for calendar year cut-off reflected the introduction of boosted protease inhibitors (PIs). Models using fractional polynomials indicated a non-linear age effect on survival and two age groups based on median age at seroconversion (<36 and ≥36 years) were, therefore, used  of 7  months. The Kaplan-Meier cumulative probability of discontinuing treatment was 13%  and 32%  at 1 and 8 years after cART initiation, respectively. Patients with higher CD4 count and shorter HIV duration at cART initiation were more likely to interrupt treatment (both p log-rank tests <0.001)  .3 at initiation. Treatment discontinuation in this subgroup was more common (42%) compared to the overall included patients after median cART duration of 7  months, reflecting a common practice during the study period of prescribing short-course therapy in primary HIV infection.Two hundred and thirty patients started cART within 12 months of HIV seroconversion with median CD4 count of 390  cells/mmOne hundred sixty three (15%) experienced at least one episode of virological failure during a follow-up of 4524 person years (PY). The crude rates of failure were 3.6  events per 100 PY overall and 3.7 , 3.4  and 3.6  for individuals with HIV infection duration at cART initiation ≤3 months, 4-12 months and >12 months, respectively.A summary of the univariate and multivariable Cox models of the association of covariates, i.e. characteristics at cART initiation, on the risk of failure is presented in 10 copies/mL. The crude drug resistance rate was 1.4  events per 100 person-years (PY) of follow-up overall (including periods off ART), and 1.3  events/100 PY on cART. The most common resistance mutations were non-nucleoside reverse transcriptase inhibitors (NNRTI) (49 patients) and nucleoside reverse-transcriptase inhibitor (NRTI) drugs (64 patients), while resistance to PIs and nucleotide transcriptase inhibitors (NtRTI) were less common . Similarly to the virological failure analysis, we found no evidence of an association between duration of HIV infection at cART initiation and development of drug resistance when this was treated as the outcome in the unadjusted and adjusted analyses. Moreover, we found similar significant effects for the other covariates with the adjusted hazard ratio for cART initiation ≥2001 vs <2001 being 0.53 .There were 270 episodes of virological failure and, of these, 36 (13%) were followed by a genotyping test within 12 months. Two hundred and thirty-three patients (21%) had a least one HIV nucleotide sequence during follow-up, of whom 82 (36%) had evidence of drug resistance, 72 while on cART. Median [IQR] HIV-RNA in the 3 months before the genotypic resistance test was 5.0  logOf the 230 patients who initiated cART early, 106 (45%) had a resistance test while ART-naïve, of whom 6 (6%) had evidence of transmitted drug resistance. Thirteen developed at least one drug resistance-associated mutation based on the 76 available HIV-RNA nucleotide sequences, 10 while known to be on cART. The estimated crude drug resistance rate was 1.1 /100 PY overall (including periods off ART) and 1.1 /100 PY while on cART. Thirty-three (14%) of 230 early initiators experienced at least one episode of virological failure during 936 PY follow-up. To our knowledge, this is the first study to examine the role of HIV infection duration at cART initiation on the risk of virological failure and development of drug resistance, and to provide estimates of the risk of these two events for patients initiating cART within 12 months of HIV seroconversion. Although, as expected, we found that higher CD4 count at cART initiation was associated with lower risks of failure and resistance, we found no evidence to suggest that this was the case for infection duration at the time cART is initiated. Of note, however, we also found that, for patients initiating cART within 12 months of infection, the proportion developing resistance by 8 years lay between 7% and 19% comparable to estimates reported for individuals initiating cART during chronic infection .3 was high in our cohort  and virological failure (from HIV-RNA results). Moreover, given that not all patients were genotyped shortly following infection, some resistance-associated mutations may, in fact, have been transmitted. It is, therefore, possible that our estimates of risk of resistance are somewhat pessimistic. Fourth, although it is recommended that HIV testing be offered at least annually to at risk individuals in the UK , is confIn conclusion, we found no evidence to suggest that initiation of cART in early HIV infection is predictive of increased risk of virological failure while on treatment and drug resistance. Moreover, the long-term risk of failure in patients who initiated early, was relatively low. Our findings should provide comfort to patients and their clinicians that early initiation of cART is unlikely to have a detrimental impact on the long-term risk of drug resistance. Nevertheless, data from randomised clinical trials are needed to shed light on the long term clinical benefits and side effects, including development of drug resistance, of early versus delayed cART initiation.


The development of resistant strains of HIV is the most significant barrier to effective long-term treatment of HIV infection. The most common causes of resistance development are patient noncompliance and pre-existence of resistant strains. In this paper, methods of antiviral regimen switching are developed that minimize the risk of pre-existing resistant virus emerging during therapy switches necessitated by virological failure. Two distinct cases are considered; a single previous virological failure and multiple virological failures. These methods use optimal control approaches on experimentally verified mathematical models of HIV strain competition and statistical models of resistance risk. It is shown that, theoretically, order-of-magnitude reduction in risk can be achieved, and multiple previous virological failures enable greater success of these methods in reducing the risk of subsequent treatment failures. The development of multi-drug regimens for HIV therapy has resulted in HIV infection becoming a chronic, manageable disease in first world countries Mutation events in HIV replication appear to be dominated by point-substitution events, which occur with very high frequency. This, coupled with the high turnover rate of HIV in uncontrolled infection, create a situation in which multi-drug resistant virus develops frequently. When a resistant mutant emerges, it becomes necessary to switch to a new three-drug regimen, whose components exhibit no cross-resistance with the failed three-drug regimen Attempts have been made to re-sensitize the virus to previously failed regimens through the use of treatment interruptions; the theory is that the wild-type virus, which enjoys a competitive advantage in the absence of therapy, would re-establish dominance and potentially drive the resistant virus extinct through competition Attempts have also been made to use previously failed drugs in novel combinations in order to preserve some usefulness from previously failed treatments in MDR patients. The problem with this, however, is that the existing mutations represent a lowering of the mutational barrier. The only way to overcome this in the long-term seems to be an increase in the number of drug components used, which may succeed at the goal of reducing viral load at the cost of raising the side effects of the HAART drugs to an unacceptable level. A notable exception to this was the recent Tetriz study The development of drug resistance in HIV infection is driven by two phenomenon: mutation and selection. Mutation in HIV replication occurs at a well-characterized, relatively constant rate of approximately When antiviral medication is used, the wild-type is no longer the fittest virus; their interference with the virus' ability to replicate is such that the virus population will shrink exponentially. Various mutations exist that, if present, reduce the ability of the antiviral drugs to interfere with HIV replication; if they interfere to the extent that the mutant virus population is able to grow overall, the mutation can provide clinically significant resistance.The likelihood of a particular resistance mutation emerging is influenced by two major factors. The first is the relative fitness of the mutation under the current treatment. This may be calculated by considering the relative effectiveness of the mutation at negating the effect of the drug and the relative fitness cost of the mutation. Fitness cost, in this sense, means the decrease in the viruses' ability to effectively replicate in the absence of treatment as a result of the mutation.The second factor influencing likelihood of emergence is the genetic distance of the resistance mutation from the existing virus pool. This is the number of point mutations necessary to generate the resistance mutation. If the HIV genome is considered to reside in a sequence space, the genetic distance is equivalent to the Hamming distance. Because mutation is a random process, mutations with a high genetic distance are very unlikely to emerge.Extensive data on resistance mutations to the antivirals listed above has been collected online at the Stanford HIV database Since the (103N) mutation gives broad class resistance against all NNRTIs, any follow-up therapy will not use NNRTIs. Neither (74V) nor (184V) exhibit strong cross-resistance patterns with any other NRTIs, so a possible follow-up therapy would be the two NRTIs tenofovir and zidovudine together with the PI nelfinavir. Clinically significant resistance to these three drugs could be conferred by the set of mutations  on the viral RT protein and the mutation (30N) on the viral protease protein. This set of mutations has a genetic distance of 4 from the wild-type, but a genetic distance of 6 from the mutant virus generated in the first round of treatment. This is because the inclusion of either the (74V) or the (184V) mutation increases the susceptibility of the virus to both tenofovir and zidovudine. Consequently, any resistant virus arising to the second treatment will probably arise from the wild-type viral pool, and will probably not carry the (74V), (103N), or (184V) mutations.Therefore, after developing resistance sequentially to these two treatment regimens, the patient will be carrying three viral strains; the wild-type, one mutant carrying the  RT mutations and another mutant carrying the  RT mutations and the (30N) Protease mutation,. A resistance mutation to a third antiviral schedule consisting of a permutation of the first two regimens, say abacavir, zidovudine, and nevirapine, would have to arise from one of these parent viruses. The common resistance mutation to this combination with the closest genetic distance to the wild-type carries the mutations  would have a genetic distance of 5. This is also the variant with the shortest genetic distance to the first mutant, with a genetic distance of 4 . The variant with the shortest genetic distance to the second mutant would carry the RT mutations , and would have a genetic distance of either 2 or 3, depending on whether the protease-resistance mutation renders it inviable.The computation of genetic distance between HIV strains is relatively straightforward, but there are a few ways in which the genetic distance can be over- or under-estimated. The first is the non-uniqueness of the genetic code; multiple genomic sequences can code for the same amino acid. The calculations carried out in the previous section assumed the parent genome contained the sequence with the shortest genetic distance to the mutant; this may lead to an underestimation of genetic distance.When using genetic distance to estimate mutational barriers, as described in the next section, the existence of viable transitional forms can result in an overestimation of genetic distance. That is, while the genetic distance to the identified resistant strain may be high, an unidentified partially resistant strain with a lower genetic distance may provide a “stepping-stone” for the development of the fully resistant strain.The research of Bonhoeffer and Ribeiro In this section, equations are presented that determine the drug-resistance emergence risk, which is the mutation probability from the current virus pool to a resistant mutant for the new regimen. To accomplish this, a Poisson distribution is used to model the mutation process. The probability of pre-existing resistant genotypes depends upon two key variables: the total number of active virions for each type of virus present in an infected host, Therefore, assuming a point mutation rate of Consider the simplest case. Assuming that the current virus pool consists of only one kind of virus strain and the genetic distance from the current virus strain to the resistant mutant is either 1, 2 or 3. As To model HIV dynamics, a set of ordinary differential equations is used that includes terms describing the mutations among different virus types. This model depicts the interactions between a wild-type virus population sensitive to all antiviral drug regimens and a resistant mutant virus population only sensitive to treatment with some, if any, antiviral drug combinations. The model is in shown in Equation 2This model includes states CD4+ T cells are generated from their source at rate The infected CD4+ T cells The virus To apply the system of equations describing the evolution of viral loads and CD4+T cells to a specific individual, the parameters of the nonlinear differential equations need to be estimated. Using patient data from the AutoVac study Parameter estimates were generated for each of 12 patients. For each patient, treatment was interrupted and after a period of time, the treatment was restarted. This cycle of interrupting and reinstating the treatment is repeated 3 or 4 times. The MCMC procedure produced 200,000 possible combinations of parameters that are consistent with the patients' data. From this result, the marginal probability densities for of the six parameters can be established. Among the 12 patients, 3 of them have no detectable virus after an interruption and another 3 appear to be subject to multiple large exogenous disturbances  which are not accounted for in our model, and result in poor fits. The results of the other 6 patients by using this identification method are shown in In the case of patients who have failed one or more drug regimens previously, the need to preserve the remaining regimens becomes all the more important. Interestingly, the previously failed regimens provide additional control inputs which can be used to reduce the risk of failure for the new regimen at a lower systemic cost than is possible when only one failing regimen is available to use.The multiple failed regimens allow two options for achieving a transient reduction in viral load. Cycling through the previously failed regimens before returning to the currently failing regimen is one such option. Consider a patient with viral dynamics described by Equation 2 who has developed virus strains Our approach can be formulated as an optimal control problem in two steps. In the first step, the allowable patterns of treatment cycling where either The achievable minimum according to this method will be limited by the initial load levels of the various resistant viruses A better option for inducing a transient minimum in the case of multiple previously failed regimens is to introduce a permuted antiviral regimen. It is very likely that that no strain exists which is resistant to permutations of the previously failed therapies. While an antiviral regimen consisting of the permuted components of previous regimens will not provide sufficient mutational barrier to be a viable long-term option, they will allow a dramatic transient reduction in the total viral load, and the corresponding risk of pre-existent resistance.In the mathematical formulation, the same cost function as Equation 4 is applied. However, the simplified model is not applicable for this case, because In this model, there are multiple virus strains, When utilizing permuted regimens, the optimal switching strategy involves switching from the failing regimen directly to a permutation of previously failed regimens prior to introduction of the naive regimen. Every previously dominant strain will be susceptible to this permuted regimen, so this will result initially in exponential decline in the total viral load. However, the reduced genetic distance inherent in using components of previously failed regimens means that resistance to the permuted regimen is likely to emerge. The expected achievable risk reduction can be estimated by assuming that the strain resistant to the permuted regimen pre-exists, with initial conditions related to measures of genetic distance.Assuming that the two previously dominant resistant strains Although current guidelines Both of these methods use optimization to find schedules that create a transient minimum in the total viral load. Successful implementation of these methods require accurately finding the time when this minimum occurs and switching to a naive regimen at that point. The exact time point of the achieved minimum may vary considerably from its calculated point due to parameter variation and the stochastic uncertainty in calculating initial values of emerging resistant virus The case where a patient has a single previously failed regimen had been the focus of our previous studies Our objective is to find a drug-switching schedule that yields the minimum risk, which is calculated as shown in the method in the Analysis section. For patients with only a single previously failed regimen, this can be achieved only through the use of interrupted schedules of treatment. The concept is identical to that driving the regimen cycling approach described in the previous section, except that periods of no treatment are allowed. If the resistant virus generated during the previous therapy has associated fitness cost with respect to the wild-type virus, then these periods of no treatment allow the wild-type virus to re-establish dominance. Re-introduction of the failing regimen will then result in a transient decrease in total viral load before the resistant strain re-establishes dominance.As in the previous section, this is an optimal control problem in two steps. The optimal schedules consist of an interruption of length An example is used to illustrate how our algorithm works. The simplified model of Equation 2 is employed with the parameters which are identified for Patient 1. Assume that the point genetic distances Treatment interruption regimens have been associated with a high rate of resistance emergence We have presented methods to reduce the risk of drug resistance emergence in HIV by manipulating the viral load prior to introduction of a naive antiviral regimen. These methods rely on creating a transient reduction in total viral load prior to introduction of a naive regimen. If the patient has failed multiple previous regimens, this may be accomplished either through regimen cycling or the use of a permuted antiviral regimen. If the patient has failed only a single previous regimen, this may be accomplished through the use of interrupted therapy. The optimal switching regimens are computed using simple model-based open-loop optimal control algorithms. In all cases, the models predict achievable order-of-magnitude reduction in the risk of resistance to the naive regimen pre-existing its introduction.The method proposed in this paper uses predictive models of HIV evolution under conditions of dynamic treatment to find treatment schedules that minimize the probability of certain resistant strains emerging. The application of dynamical systems and control to evolutionary systems will likely find broader application, as problems of drug resistance continue to increase in other areas of medicine.While risk reduction should be achievable through any of these three methods, regimen cycling and interrupted therapy carry an increased risk of disease progression and/or further development of resistant virus. For these reasons, initial clinical investigations will focus primarily on the method of permuted regimens. Nevertheless, the other two methods may be useful in certain circumstances.The problem of multi-drug resistance in HIV is extremely widespread, and methods that preserve remaining suppressive antiviral regimens have the potential to significant decrease morbidity and mortality in the HIV-positive population. The necessary next steps for implementing this method are outlined below:The methods introduced in this paper have the potential to significantly reduce the incidence of pre-existence related treatment failure. The methods involving permuted regimens could be applied to select groups of patients without significant further work. These patients would have to be in a closely monitored clinical situation, have a history of consistent viral genotyping showing strain patterns amenable to this method, and would have to be available for frequent viral load sampling. For these methods to be more broadly applied, several issues involving measurement, viral load history, and sampling frequency will need to be addressed.The model of Equation 2 represents a highly reduced form of the HIV dynamics, and neglects many known factors in HIV infection. Perhaps chief among these is the immune response to HIV, which can change the infection rate The methods described in this paper rely heavily on the frequent and accurate measurement of the HIV viral load, both for risk computation and model identification. Viral load measurements are complicated by the existence of a detection limit of 50 copies/mL. Targeting viral load reduction below the limit of detection is therefore problematic. However, reduction of viral load to this limit of detection is sufficient in most cases to achieve a significant order-of-magnitude reduction in risk.A second issue related to measurement is the proportion of the measured viral load which is noninfectious. Cells with defective integrated viral genomes may produce replication-incompetent virus particles, and a proportion of the particles produced by all infected cells will also be replication-incompetent. The most direct measurement of this phenomenon estimates that between 5–13% of the total free virus particles are capable of successfully completing the infection process  A third issue, related to the second, is the fact that plasma viral concentrations are not the same as the virus concentrations in the lymphoid tissues, where the majority of the HIV virus resides and the where the majority of virus dynamics occur. HIV virus penetrates into many different tissues in the body, and there is evidence that these tissues are sufficiently compartmentalized to allow for divergent evolution of the virus in different compartments The most promising method presented in this paper is the introduction of permuted antiviral regimens prior to the introduction of a naive regimen. In order to safely choose these permuted regimens, it is necessary to know which resistant viruses are present in the patient's viral reservoirs. Consistent viral genotyping following every treatment failure would provide this information; unfortunately, this is rare. It may be possible to estimate the likely distribution of resistant viruses in a patient based on a history of antiviral use and failure, using genetic distance and fitness information from the HIV database.All the methods presented in this paper induce a transient crash in the viral load, and depend on being able to switch therapies at or near the minimum of this crash, prior to rebound. In this paper, the method suggested for this is consistent, frequent sampling of the viral load during the transient period. While this should work, it is expensive both in economic terms and in terms of patient burden. Optimal minimal sampling methods to find the viral load minima with the fewest possible measurements should solve this issue; this research is ongoing