The recently increased access to antiretroviral therapy (ART) in South Africa has placed additional strain on human and infrastructure resources of the public health sector. Capacity from private-sector General Practitioners (GPs) could be leveraged to ease the current burden on the public health sector.We conducted a retrospective record review of routine electronic medical record data on a systematic sample of HIV-infected adults (≥18 years old) initiated on ART at a tertiary hospital outpatient HIV clinic in Johannesburg, South Africa and down-referred to private-GPs for continued care after stabilization on ART. We compared these patients (“GP down-referred”) to a control-cohort who remained at the referring site (“Clinic A”) and patients from a regional hospital outpatient HIV clinic not offering down-referral to GPs (“Clinic B”). Study outcomes assessed are viral load suppression (VL < 50 copies/ml) and attrition from care  by 12 months of follow-up following down-referral or eligibility.A total of 3685 patients, comprising 373 (10.1%) GP down-referred, 2599 (70.5%) clinic A controls, and 713 (19.4%) clinic B controls were included in the analysis. Overall, 1535 patients (53.3%) had a suppressed viral load. A higher portion of GP down-referred patients had a suppressed viral load compared to clinic A and B patients (65.7% vs 49.1% vs 58.9%). After adjusting for demographic and baseline clinical covariates, we found no difference in viral load suppression between GP down-referred and control patients , .Clinic B controls experienced the highest attrition compared to GP down-referred and clinic A controls (33.2% vs 11.3% vs 5.9%) and had a higher risk of attrition compared to GP down-referred patients , whereas clinic B controls had a lower risk of attrition .Our results show that private-GPs can contribute to caring for stabilized public sector HIV patients on life-long ART. However, they require special efforts to improve retention in care. South Africa has made substantial investments in HIV prevention and treatment policies and programs resulting in over 4 million of the 7.9 million people living with HIV initiating antiretroviral therapy (ART) through the public health sector, 55% reduction in HIV related deaths from 2010 to 2018, compared to 33% globally, and vast improvements in life expectancy [In 2015 South Africa adopted the UNAIDS 90–90-90 targets aiming to: diagnose 90% of all people living with HIV; enrol 90% of diagnosed patients on sustained ART; and ensure that 90% of patients on ART achieve viral suppression . To achiCurrently, shortages of trained health care workers in the public health sector of South Africa constrain the delivery of quality health care , 8. ProjThe transfer of HIV infected, public sector patients who are stable on ART to private-sector medical practitioners for routine follow-up and management could ease the current public health sector burden. This model of decanting has been implemented in varying formats locally and in countries with emergent or overstretched public-sector ART programs, with differing outcomes –18. RighThe RtCHS GP down-referral program presents an opportunity to evaluate the outcomes of this down-referral model further and compare these to a public sector clinic program.Previous versions of this model of care have had mixed results with some results leading to questioning the quality of care provided by private health providers to public sector patients while others demonstrated the feasibility of achieving acceptable results at lower or equivalent cost , 16, 19.The current evidence considering the viability of down-referral of public sector HIV infected patient for management by private medical practitioners, stated above, needs to be expanded to the current guideline-setting and to include a direct comparison with a similar public sector cohort. Our study aims to compare the treatment outcomes of HIV-positive patients’ enrolled in the RtCHS GP down-referral strategy compared to eligible but not down-referred patients from the referring site and eligible patients from a comparison, external public outpatient HIV clinic within a regional hospital.TM for clinical management. A team of database management staff oversaw the design, security and maintenance of the systems, while a team of trained data management staff assisted with data capturing and routine data cleaning. In addition to clinical management of patients, the TherapyEdge-HIV™ system was also used for program monitoring and evaluation as well as operational research projects undertaken at the clinic.The GP down-referral program was anticipated to be able to manage around two thousand patients through its ten partner GPs sites. The GPs were required to have completed an HIV/AIDS management course, have a dispensing license, and facilities for the proper storage of medicines. The public health sector supplied medicines pre-packed, batched by area and delivered to the GP for dispensing to patients. The referring public sector clinic (clinic A) used an electronic patient management system called TherapyEdge-HIV™ . Right tTM system to capture medical care information for down-referred patients in their care. The National Health Laboratory Service (NHLS), the national contracted provider, provided laboratory monitoring, free of charge to the patient. NHLS had a direct interface with the TherapyEdge-HIV™ system, and GPs could access results directly through the system.GPs enrolled in the program were expected to use the TherapyEdge-HIVPatient eligibility criteria for down-referral included being initiated on, and remaining on a standard first-line ART regimen according to the prevailing South African national HIV treatment guidelines for at least 12 months at the referring site, a viral load of < 400 copies/ml, CD4 count of > 250 cells/μl at the time of down-referral. Patients on TB treatment and those with comorbidities such as renal disease, liver diseases, cardiovascular disease, neoplastic diseases and neurological disorders as well as those pregnant were excluded.Patients were offered the option of down-referral after eligibility was confirmed. Consenting patients could then choose their preferred GP from the list of enrolled GPs based on the GP practices’ proximity to patients’ homes or work. All HIV-care services in the GP down-referral program were offered free of charge to down-referred patients.Down-referred patients were dispensed two months of ART and were scheduled for their first appointment at the GP practice after that. They would see the GP every two months and go back to the referring clinic annually to take blood for clinical monitoring. Patients were managed according to national HIV treatment guidelines. RtCHS nurse case managers, who received mentoring from a RtCHS medical officer, reviewed the medical care provided by the GPs to ensure adherence to clinical guidelines and supported the GPs. Patients were referred back to the referring clinic if their viral load became unsuppressed, they had abnormal safety blood results or developed clinical problems. These included severe toxicities, opportunistic infections, abnormal weight loss, pregnancy and other significant findings. We defined loss to follow-up (LTFU) as having missed a scheduled visit by more than 90 days. LTFU patients were automatically up-referred to the referring site where counsellors would make up to three attempts to make telephonic contact with patients and urge them to return to care. All correspondence with the patients were captured in TherapyEdge-HIV™.If GPs chose to leave the GP down-referral program, their allocated patients from the program would be referred back to the referring clinic, and patients would be given the option of selecting another GP if they chose to remain in the down-referral program. Alternatively, they could decide to continue their care at the referring clinic or transfer to another public sector facility.GP down-referred: Patients from clinic A who were enrolled in the down-referral program and decanted to participating GPs. The referring clinic (clinic A) is a comprehensive HIV and AIDS care management and treatment (CCMT) clinic housed in a tertiary hospital in Johannesburg, South Africa. It has one of the largest cohorts of HIV-infected patients in the country and has been down-referring patients to local public-sector primary healthcare clinics since 2009 as a strategy to decongest the site and increase capacity for initiating more patients on ART . For theClinic A control patients: Patients from clinic A who meet the GP down-referral eligibility criteria but were not decanted.Clinic B control patients: Patients from a second HIV clinic who meet the GP down-referral eligibility criteria but were not decanted. Clinic B is a RtCHS- supported HIV clinic nested within a level 2 hospital in Krugersdorp, South Africa. Clinic B was not part of the GP down referral program, and none of their patients were offered the option of down-referral to a GP practice.We conducted a retrospective record review of electronic, routinely collected medical record data, on a systematic sample of HIV-infected adults (> 18 years old at ART initiation) initiated on ART between 1 April 2004 and 31 December 2014. The patients were enrolled as follows:Clinic A and B eligible control patients are patients who met the study inclusion criteria and were eligible to be down-referred to the GP down-referral program but were not down-referred. We assessed control patients down-referral eligibility prospectively from January 2011 to December 2015, to align the timing of eligibility assessment to the year the GP down-referral program started. We excluded patients because of missing ART start date, being under 18 years old at ART start, initiating ART outside the study period and not meeting GP program eligibility criteria.2. Baseline clinical data analysed were reported 3 months before and after down-referral or first eligibility for clinic A and B control patients.Clinical data were collected from all study sites, captured on-site and stored in an electronic patient management system, TherapyEdge-HIV™. Patient information collected includes socio-demographic factors including sex, age, nationality, highest education completed, and employment status. Baseline clinical characteristics include CD4 cell count, viral load, haemoglobin , and body mass index (BMI). CD4 was categorised as 250–349.9, 350–499.9, and ≥ 500 cells/μl. Viral load was categorised as < 50, 50–199 and 200–399 copies/ml. BMI was categorised as underweight (BMI < 18.5), normal (BMI 18.5 to < 25), overweight (BMI 25 to < 30) and obese (BMI > 30) kg/mWe calculated time on ART before down-referral eligibility as the difference between date of down-referral eligibility and ART initiation date, and categorised as 1 to 2.9 years, 3 to 4.9 years, and 5 or more years. Guideline year of ART initiation was categorised as 2004–2010 for patients initiated on ART between 1 April 2004 and 31 March 2010, and 2011–2014 for patients commencing ART from 1 April 2010 to 31 December 2014. Patients were followed up for 12 months from the date of entry into the GP down-referral program, or earliest date down-referral eligibility was met between January 2011 to December 2015 for control patients using individual-level de-identified electronic medical record data. We created a dichotomous variable classified as ‘yes’ or ‘no’, to indicate if patients experienced comorbid conditions during the 12-month study follow-up period. Where ‘yes’ referred to any, and earliest report of any comorbid condition according to lab monitoring results or clinical report from the patient’s electronic file, and ‘no’ was defined as never having a comorbid condition reported during the study follow-up period.The primary exposure variable was the being enrolled in the GP down-referral compared to clinic A and B control patients.The primary study outcome is attrition from HIV care  during the 12 months observation period after down-referral or becoming eligible for down-referral (for control patients). The secondary outcome is viral load (VL) suppression to undetectable levels (VL < 50 copies/ml) , 26, virBaseline characteristics at down-referral or down-referral eligibility for control site patients were described using medians and interquartile ranges (IQRs) for continuous variables and proportions and corresponding percentages for categorical variables stratified by cohort (GP down-referred vs clinic A and B patients).p-value < 0.1 and a priori variables of importance were included in the multivariate model. We report adjusted relative risk (aRR) and 95% CIs.Modified Poisson regression with robust standard errors was used to estimate the relative risk (RR) of viral load suppression to undetectable levels (VL < 50 copies/ml), and the corresponding 95% confidence intervals (95% CIs). Factors identified with a univariate p-value < 0.1 and a priori variables of importance in attrition from care in the crude hazards ratio (HR) were included in the multivariate models. We report adjusted hazards ratio (aHR) and 95% CIs. The assumption of proportionality of hazards was tested using Schoenfeld residuals.We used time to event analysis to compare attrition from care between the GP down-referral and control cohorts. Person-time accrued from the date of down-referral for patients enrolled in the GP down-referral program, and date of eligibility for down-referral for patients in the control cohorts. For the attrition outcome, patients were followed-up until the last date seen at the facility (for deceased/LTFU), or completion of the 12 months of follow-up. Cox proportional-hazard regression was used to identify predictors of attrition from care. Factors identified with a univariate Data analysis was conducted using STATA version 14 .Figure Table Overall, 55.0% of GP down-referred and 50.6% clinic B controls were initiated on ART in the 2004–2010 national HIV guideline period compared to 70.3% of the clinic A controls. A total of 758 (20.6%) patients were initiated on ART between 2011 and 2014 after the GP down-referral program started and the majority of these patients (93.9%) were on ART for 1–2.99 years before down-referral eligibility. A total of 158 (42.4%) of GP down-referred patients were on treatment for five years or more before down-referral, compared to clinic A controls (24.6%) and clinic B controls (13.6%).Over a third (36.1%) of total study cohort presented with a high body mass index (BMI), whereas a higher portion of GP down-referred patients presented with a normal BMI at down-referral compared to clinic A and B patients (64.6% vs 38.2% vs 40.8%). A higher proportion of clinic A controls (41.0%) and clinic B controls (40.5%) presented with CD4 counts > 500 cells/μl compared to GP down-referred patients (22.5%). Overall, 79.6% of the GP down-referred study cohort presented with a viral load < 50 copies/ml compared to almost 60% of the combined control cohort (clinic A and B). Most of clinic A controls (64.6%) were on Stavudine (d4T)-based first-line regimens at down-referral eligibility, compared to 49.1% GP down-referred patients and 48.7% of clinic B controls.A total of 2.6% of the study cohort experienced comorbid conditions during the 12-month study follow up period. With a higher proportion of clinic A (2.7%) and clinic B controls (2.7%), patients experiencing comorbid conditions compared to 1.9% of GP down-referred patients. Of these, the majority experienced anaemia (45.8%) followed by renal dysfunction (31.2%), while 22.9% had signs of liver injury.When we explored experiencing comorbid conditions further using time to event analysis (only reporting crudes), 96 patients experienced conditions during 2888.2 person-years of observation   total study cohort had a 12-month viral load available. Clinic A control patients had the highest proportion (26.9%) of patients missing a 12-month viral load compared to GP down-referred (7.0%) and clinic B controls (11.2%). Those with missing 12-month viral loads were similar to those with 12-month viral loads in terms of demographic factors, except for sex and employment. A higher proportion was male, employed, initiated on ART during the 2004–2010 guideline period. A higher percentage was also on treatment for three years or longer at down-referral, had a baseline CD4 counts of < 500, were initiated on a d4t based ARV regimen and were transferred out  had a suppressed viral load at 12 months . Clinic A controls were 50% less likely to be deceased or LTFU compared to GP down-referred patients . Compared to South Africans, Non-South African patients were less likely to be deceased or LTFU , as well as those overweight at down-referral , and those that were initiated on d4t-based ART regimens . Longer time on ART before down-referral eligibility led to almost double the risk of attrition from care .We compared treatment outcomes between patients down-referred to private GPs and public sector patients after becoming stable on ART. We defined treatment outcomes as viral load suppression to undetectable levels (< 50 copies/ml), and attrition from care based on all-cause mortality or having no contact with the treatment site for 90 days or more after missing the last scheduled visit during the 12 month study follow-up period after down-referral or down-referral eligibility for control site patients. Our results show that down-referred public sector patients have similar virological outcomes compared to public sector control patients. However, down-referred patients had poorer retention outcomes.RtCHS provided programmatic and clinical oversight to the GPs; strengthening their capacity to provide HIV care according to national HIV treatment guidelines. Only 7% of the GP-down-referred had missing 12-month viral loads, demonstrating GPs’ high adherence to viral load monitoring required, as stated in the national HIV treatment guidelines , which iOur results are supportive of results from a donor-funded, private GP-run outpatient clinic in rural Mpumalanga, in South Africa initiating treatment naïve patient on ART that also demonstrated good treatment outcomes. They reported higher rates of viral suppression (70%)  and the Patients presenting with viral loads of 50–399 copies/ml were less likely to have a suppressed viral load at 12-month follow-up compared to those presenting with viral loads of < 50 copies/ml. Although the national HIV treatment program considers patients with a viral load of < 400 copies/ml stabilised on treatment, the viral load algorithm from the HIV treatment guidelines recommend intensified adherence support and also stresses the need for compliance to viral monitoring requirements for these patients. Future down-referral programs to private sector providers may need to strengthen providers’ routine adherence support to patients with viral loads above 50–400 copies/ml as indicated in the adherence guidelines for HIV TB and non-communicable diseases, to ensure improved treatment outcomes as stated in the adherence guidelines for HIV TB and non-communicable diceases .Results from a study comparing patients from the GP down-referral program’s referring site with patients from a private GP managed outpatient clinic in Johannesburg also found a higher proportion of GP managed patients with suppressed viral loads at 12-month follow up . The samLonger time on ART before down-referral eligibility led to almost double the risk of attrition from care. Experienced patients may no longer be as motivated to remain in care because of treatment fatigue as a consequence of being on ART for a long time, which may lead to attrition from care .Patients initiated ART regimens on d4T-based regimens as the preferred first-line ART regimen were less likely to experience mortality or LTFU at 12 months than those initiated on TDF-based first-line ART regimens, which is in contrast to results from previous studies –40. The Weight gain after ART initiation is a sign of improving health status as HIV-infected patients initiate and remain on ART . This haWe also found that Non-South African patients were less likely to be deceased or LTFU than South African patients. Which is similar to findings of a study by McCarthy K et al. (2009) that showed non-South African’s had better retention in care and lower mortality compared to South Africans . Non-SouAs South African continues efforts to expand ART access to all HIV infected individuals, it is vital to expand efforts to support GPs to be able to manage public sector patients according to treatment guidelines to achieve good treatment outcomes. Our results show that GPs can contribute to caring for HIV patients on life-long ART following stabilization, but special efforts are required to improve retention in care.The GP down-referral program required clinical stabilization as part of it is a selection criterion. Missing data in some key clinical data may bias outcomes. Misclassification as LTFU may be self-transfers to other health facilities or unreported mortality . ResultsAdditional file 1. Missing 12-month viral load.


Early identification of PIR is important given their higher morbimortality compared to adequate immune responders (AIR). In this study, 33 patients severely lymphopenic at ART onset, were followed for at least 36 months, and classified as PIR or AIR using cluster analysis grounded on their CD4+ T cell count trajectories. Based on a variety of immunological parameters, we built predictive models of PIR/AIR outcome using logistic regression. All PIR had CD4+ T cell counts consistently below 500 cells/μL, while all AIR reached this threshold. AIR showed a higher percentage of recent thymic emigrants among CD4+ T cells; higher numbers of sj-TRECs and greater sj/β TREC ratios; and significant increases in thymic volume from baseline to 12 months of ART. We identified mathematical models that correctly predicted PIR/AIR outcome after 36 months of therapy in 77–87% of the cases, based on observations made until 2–6 months after ART onset. This study highlights the importance of thymic activity in the immune recovery of severely lymphopenic patients, and may help to select the patients that will benefit from closer follow-up or novel therapeutic approaches.Poor immunological responders (PIR) are HIV-infected patients with virologic suppression upon antiretroviral therapy (ART) but persistently low CD4 However, CD4+ T cell counts remain persistently low in 9–45% of patients, depending on the criteria used, despite complete viral suppression  (The majority of patients infected by the human immunodeficiency virus (HIV) show immune recovery upon antiretroviral therapy (ART). This recovery usually occurs with a rapid increase in CD4 plateau –3. An adHIV RNA) . There i+ T cell production by the thymus, peripheral proliferation, virus- or immune-mediated cell death and/or ability to obtain survival stimuli. Differences in recovery may also derive from variations in the extent of CD4+ T cell migration from lymphoid tissues to peripheral blood or to the gastrointestinal tract soon after ART initiation  to Th17 cells, tissue fibrosis ; or large if ≥0.8;- Cohen's r: small if <0.3; medium if ; or large if ≥0.5;- - Cramér's V: small if <0.3; medium if ; or large if > 0.7;r or ρ): negligible if <0.3; low if ; moderate if ; high if ; or very high if .- Correlation coefficient  after exclusion of the extreme outliers. All the results reported included the extreme outliers.All statistical tests performed were two-tailed. Extreme outliers were defined as lower than P25 – 3 × Interquartile range (IQR) or higher than P75 + 3 × IQR. All tests were performed with and without extreme outliers to verify their effect on the glm function in R (v3.4.3) to identify associations between independent variables, assessed between baseline and 6 months on ART (inclusively), and the dependent variable “Being AIR or PIR” . Selection of variables for the multivariate analysis included a univariable logistic regression analysis for each candidate to detect potential predictors . A backward stepwise selection procedure was performed, analyzing the significance of the coefficient for each variable at each step, until no further improvement was possible. The adequacy of the final model was assessed by means of a likelihood ratio test . The Akaike information criterion (AIC) was used to compare models with the same observations. The Hosmer-Lemeshow test for goodness-of-fit was applied to the final model and diagnostic plots were carried out as well, including ROC curve. The area under the curve (AUC) statistic was also calculated as a measure of goodness-of-fit, considering: , poor; , fair; , good; and >0.9, excellent. For each model, all patients were classified as “probably PIR” (probability of being PIR ≥0.5) or “probably AIR” (probability of being PIR < 0.5). Sensitivity and specificity were calculated as the percentage of “probably PIR” among PIR and the percentage of “probably AIR” among AIR, respectively. Accuracy (ACC) was calculated as the percentage of patients PIR or AIR that were respectively classified as “probably PIR” or “probably AIR” among all patients. Relevant R code is available in Supplementary Material.Logistic regression analysis was conducted using the Demographic and clinical characteristics of the patients are presented in + T cell count trajectories of the 33 patients were automatically analyzed using R traj package, and two clusters were formed  had at least one CD4+ T cell count above 500 cells/μL during the first 36 months of ART, and were classified as AIR. Patients in cluster 2 (n = 19) had all CD4+ T cell counts bellow 500 cells/μL, during the same time period, and were classified as PIR . These individuals presented lower percentages of Ki67+ cells and of CD69+ cells among CD4+ T cells than PIR or AIR at baseline  was tendentially higher in AIR than in PIR at baseline and 6 months, and significantly higher at 12, 24, and 36 months of ART  in blood, at baseline, 6, 12, 24, and 36 months of ART. AIR and PIR presented similar thymic volumes at baseline and at 12 months of ART. However, AIR exhibited significant increases in thymic volume during that period, while PIR did not  assessment of thymic index (defined in the imaging subsection of the Methods) and volume, at baseline and 12 months of ART; (2) quantification of peripheral blood TRECs at baseline, 6, 12, 24, and 36 months of ART); and (3) quantification of recent thymic emigrants  at 24 months of ART  were: CD4+ T cell count at 6 months of ART , CD4+ T cell count at 2 months of ART  or CD4+ T cell count slope from baseline to 6 months of ART . Performing combinations of eight elements from the set of variables identified in the univariate analysis with p < 0.200, and applying a stepwise selection, four logistic models, with three independent variables and an area under the curve (AUC) >0.80, were obtained (Models 1–4). Each of the models were represented by the following equation:We performed univariate logistic regressions with all variables to identify predictors of PIR or AIR status. The variables that performed the best  and accuracy (87.1%), combined age, plasma viral load at baseline and CD4+ T cell counts at 2 months of ART  presented values in the included variables that, in combination, led to their inclusion in the opposite patient group . We foun+ T cell counts during therapy, and to build logistic models to predict PIR status using parameters assessed during the first months of ART. Although mathematical tools have been previously used to separate PIR and AIR . Our rePeripheral blood samples were provided by patients with chronic HIV-1 infection, who participated in the prospective cohort study authorized by the local ethics committee (reference 168/CES). From all participating patients, an informed written consent was obtained.RR-S, AH, and MC-N conceptualized the study. CN, AH, and MC-N designed experiments. RR-S, CN, IF, JC-G, and AH performed experiments. RR-S, CA, and EA performed statistical analysis. RR-S and CN prepared the figures. RC, AY, and MC-N supervised research. All authors discussed the results and contributed to the final manuscript.AH is a consultant for Abbvie LDA, Bristol-Myers Squibb, Gilead Sciences, Janssen-Cilag, Merck Sharp and Dohme and ViiV Healthcare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Darunavir/cobicistat/emtricitabine/tenofovir alafenamide (D/C/F/TAF) 800/150/200/10 mg is a once-daily, single-tablet regimen for treatment of HIV-1 infection. The efficacy/safety of switching to D/C/F/TAF versus continuing boosted protease inhibitor (bPI) + emtricitabine/tenofovir disoproxil fumarate (control) were demonstrated in a phase 3, randomized study (EMERALD) of treatment-experienced, virologically suppressed adults through week 48. The objective of this analysis was to evaluate EMERALD outcomes across subgroups of patients based on demographic characteristics, prior treatment experience, and baseline antiretroviral regimen.EMERALD patients were virologically suppressed . Prior non-darunavir virologic failure (VF) was allowed. Primary endpoint was proportion of patients with virologic rebound (confirmed VL ≥ 50 copies/mL) cumulative through week 48. Virologic response was VL < 50 copies/mL (FDA snapshot). Safety was assessed by adverse events, renal proteinuria markers, and bone mineral density. Outcomes were examined for prespecified subgroups by age (≤/> 50 years), gender, race (black/non-black), prior number of antiretrovirals used (4/5/6/7/> 7), prior VF (0/≥ 1), baseline bPI (darunavir/atazanavir or lopinavir), and baseline boosting agent (ritonavir/cobicistat).Among 1141 patients in the D/C/F/TAF (n = 763) and control (n = 378) arms, virologic rebound rates  were similar, and this was consistent across all subgroups. Virologic response rates ranged from 91 to 97% (D/C/F/TAF) and 89 to 99% (control) across all subgroups, with differences between treatment arms of 0 and 6%. Adverse event rates were low in both arms and across subgroups. Improvements in renal and bone parameters were observed with D/C/F/TAF across demographic subgroups.For treatment-experienced, virologically suppressed patients, switching to D/C/F/TAF was highly effective and safe, regardless of demographic characteristics, prior treatment experience, or pre-switch bPI.Trial registration ClinicalTrials.gov Identifier: NCT02269917. Registered 21 October 2014. https://clinicaltrials.gov/ct2/show/NCT02269917The online version of this article (10.1186/s12981-019-0235-1) contains supplementary material, which is available to authorized users. Darunavir/cobicistat/emtricitabine/tenofovir alafenamide (D/C/F/TAF) 800/150/200/10 mg is an oral, once-daily (QD) single-tablet regimen (STR) for the treatment of human immunodeficiency virus (HIV)-1 infection. The darunavir component has demonstrated efficacy and safety, as well as a high barrier to resistance , 4.In the phase 3 EMERALD study, treatment-experienced, virologically suppressed adults switched to D/C/F/TAF or remained on a boosted protease inhibitor (bPI) + emtricitabine/tenofovir disoproxil fumarate (TDF) regimen; D/C/F/TAF was noninferior versus the control regimen (4% margin) for cumulative virologic rebound through week 48 . High viEMERALD was a phase 3, randomized, active-controlled, multicenter, open-label trial evaluating efficacy, resistance development, and safety in treatment-experienced, virologically suppressed patients with HIV-1 who switched from a bPI + emtricitabine/TDF regimen to D/C/F/TAF, or continued on their current regimen. The bPI was darunavir or atazanavir (with ritonavir or cobicistat) or lopinavir (with ritonavir). Detailed study methods for EMERALD have previously been described .Efficacy was assessed by the proportion of patients with cumulative virologic rebound (confirmed HIV-1 RNA ≥ 50 copies/mL) through week 48 (primary endpoint) and virologic response (HIV-1 RNA < 50 copies/mL) and VF at week 48 (US Food and Drug Administration [FDA] snapshot approach) . The difTo evaluate post-baseline resistance, genotyping was performed in patients with virologic rebound who also had a viral load measurement of ≥ 400 copies/mL at the time of VF, at later time points, or at discontinuation.Safety was assessed by monitoring adverse events (AEs). For subgroups based on demographic characteristics, additional safety analyses are reported .Prespecified subgroup analyses were performed on all randomized patients who received ≥ 1 dose of study drug. Demographic subgroups were based on age (≤ 50 vs > 50 years), gender (men vs women), and race (non-black/African American vs black/African American). Prior treatment experience subgroups were based on the number of prior ARVs used  and prior VF (0 vs ≥ 1 prior VF). Subgroups corresponding to ARV regimen at baseline were based on bPI (darunavir [with ritonavir or cobicistat] vs atazanavir [with ritonavir or cobicistat] or lopinavir [with ritonavir]) and boosting agent .Additionally, because polypharmacy may be a concern among older individuals, age subgroups (≤ 50 vs > 50 years) were further divided for assessment based on the presence or absence of polypharmacy (defined as ≥ 5 [non-ARV] concomitant medications at baseline).A total of 1141 patients were randomized and treated in EMERALD, including 763 in the D/C/F/TAF arm and 378 in the control arm. Baseline demographics, clinical characteristics, and prior ARV use were generally balanced in both treatment arms  had used 8 prior ARVs, 1 patient (control arm) had used 6 prior ARVs, and 2 patients (control arm) had used 4 prior ARVs. None of these 4 patients had a history of prior VF. No darunavir, primary PI, emtricitabine, or tenofovir RAMs were observed in these patients or any arm across subgroups .The incidence of AEs was similar in the D/C/F/TAF and control arms across all subgroups, except for a numerically higher incidence of study drug-related AEs with D/C/F/TAF relative to control  . NeverthSwitching to D/C/F/TAF may be an effective strategy for individuals who are stably suppressed and would like to simplify therapy, including patients with prior experience with multiple ARVs or a history of prior VF (without a history of darunavir RAMs or VF on a darunavir-based regimen).Additional file 1: Table S1 Baseline demographic and disease characteristics. Table S2. Summary of AEs week 48 by prior treatment experience. Table S3. Summary of AEs through week 48 by ARV regimen at baseline. Figure S1. Mean change in eGFRcystC from baseline to week 48. Figure S2. Lipid values at baseline and week 48 by demographic subgroups. Figure S3. Changes from baseline in femoral neck BMD over time based on demographic characteristics.


Changes to the U.S. President's Emergency Plan for AIDS Relief (PEPFAR) funding have led to closures of non‐governmental HIV clinics with patient transfers to government‐funded clinics. We sought to determine the success of transfers in South Africa using a national data source.All adults (≥18 years) on antiretroviral therapy (ART) who visited a single PEPFAR‐funded hospital‐based HIV clinic in Durban, South Africa from March to June 2012 were transferred to community‐based clinics. Previously, we matched patient records from the hospital‐based HIV clinic with National Health Laboratory Services (NHLS) Corporate Data Warehouse (CDW) data to estimate the proportion of patients with a CD4 count or viral load (VL) in the CDW during the year before transfer. As a proxy for retention in care, in this study we evaluated whether patients had a CD4 count or VL at another facility within approximately three years of transfer. Patients referred to a private doctor at transfer were excluded from the analysis. We assessed predictors  of not having post‐transfer laboratory data using Cox proportional hazards models.Of the 3893 patients referred to a government facility at transfer, 41% were male and median age was 39 years (IQR 34 to 46). There was a post‐transfer CD4 count or VL from another facility for 23% of these individuals within six months, 44% within one year, 57% within two years and 61% within approximately three years. Male sex  and shorter duration on ART  were associated with not having a post‐transfer record.Using data from the NHLS CDW, 61% of patients had evidence of a post‐transfer laboratory record at another facility within approximately three years after closure of a large South African HIV clinic. Males and those with shorter time on ART prior to transfer were at highest risk for lacking follow‐up laboratory data. As patients transfer care, national data sources can be used to evaluate long‐term patient care trajectories. The Sinikithemba HIV clinic at McCord, which became a PEPFAR‐funded site in 2004, was an integral part of the South African ART scale‐up and initiated over 10,000 patients on ART All patients who returned to the clinic for clinical appointments, laboratories or pharmacy refills to Sinikithemba between 12 March 2012 and 30 June 2012, the “transfer period,” were referred for transfer to one of 171 different clinics in the Durban area. Patients taking first‐line ART were transferred to primary healthcare clinics (PHC); those taking second‐line line ART were transferred to community health clinics (CHC), which provide a higher level of services than PHCs. Patients with comorbidities requiring medications not on the South African Essential Drug List were referred to hospital‐based clinics. Data collected by the clinic at the time of transfer included sex, age, most recent CD4 count and viral load (VL) prior to transfer, and the type of clinic to which the participant were referred. We have previously reported on the Sinikithemba transfer process evaluating linkage to the initial transfer clinic visit and patient attitudes about their transfer experience using telephone surveys combined with validated clinic visits 2.2The study population included adults on ART who visited the Sinikithemba HIV clinic during the transfer period. Patients <18 years old on 30 June 2012 were excluded, as we focused on the adult transfer process. For the post‐transfer linkage assessment, we excluded patients who were referred to a private doctor, because we did not expect these individuals to have laboratory data in the NHLS system. Participants provided verbal consent for study participation at the time of transfer. The study protocol was approved by the McCord Hospital Research Ethics Committee  and the Partners Human Research Committee .2.3The National Health Laboratory Service was established in 2001 and supports national and provincial health departments in South Africa. It is the largest diagnostic pathology service in the country, providing laboratory and related public health services to over 80% of the population through a national network of 265 laboratories 2.4We sought to estimate the cumulative frequency of linkage using evidence of post‐transfer CD4 count and VL after 30 June 2012. We sent a list of all 4257 McCord Hospital transfer patients with corresponding identifiers  to the NHLS to obtain laboratory records within approximately three years of the transfer (the last NHLS records extracted were in June 2015). To assist with the matching process, we also sent the last known CD4 count and VL values and dates recorded in the electronic medical record at McCord Hospital. From the original 4257 patient list, duplicated patient IDs (n = 12), patients <18 years old on 30 June 2012 (n = 337), and patients referred to private clinics (n = 15) were removed from the data sets prior to matching, resulting in a cohort of 3893 transfer patients for potential matching with NHLS laboratory data. Although laboratory monitoring recommendations have changed over time, laboratory monitoring of some kind (CD4 or VL or both) have been recommended every six or twelve months in South Africa for the duration of the study period.We assessed deaths by cross‐matching patients with the South African National Population Register using South African Identification Numbers in May 2014. Valid South African Identification Numbers were available for 94% of patients. More than 90% of deaths nationwide are captured in this register 2.5As we reported previously, after extensive efforts assessing the quality of subject matching for data provided by the NHLS, we determined that 99.7% of the individuals were correctly matched 2.6Standard life table methods were used in the analysis. Proportions linked at various time periods after the end of the transfer period were estimated using Kaplan‐Meier methods. We used a Cox proportional hazards model to examine the hazard ratio of slower time to re‐linkage (having no post‐transfer measurements in the CDW over time). Predictors considered included sex, age, CD4 count at transfer , virological suppression pre‐transfer , duration on ART prior to transfer  and type of transfer facility. We excluded a total of 44 individuals with missing values for CD4 , ART duration , or missing location of future care (21 missing); several individuals were missing data on more than variable. We retained missing VL as a separate category, due to the large number of missing records (298). We calculated hazard ratios (HR) with 95% confidence intervals (CI) for each individual factor. We then developed a multivariable model including all factors other than VL that were statistically significant in the univariate analysis and calculated adjusted hazard ratios (aHR). The goal of the model was to identify factors associated with higher risk of failure to re‐link to care after clinic transfer to help guide programmes when considering which patients may be higher risk for LTFU post transfer. Statistical analyses were performed using SAS software, version 9.4 .33.1Of the 3893 participants included in the analysis, 41% of the cohort was male and median age was 39 (interquartile range (IQR) 34 to 46)  of these patients were re‐linked to care by the end of our study period ), age <30 ), CD4 count <200/μL , and shorter duration of ART (<3 months hazard ratio 3.96 (2.90 to 5.40) and three months to one year hazard ratio 1.35 (1.19 to 1.54), both compared to >1 year) were associated with slower re‐linkage over time (Table 3.3.1p > 0.10)  was a significant predictor of slower linkage, VL was not included in the multivariable model. Males were more likely to be unlinked (adjusted hazard ratio 1.20 (1.10 to 1.31)) but age and CD4 count were no longer significant predictors  had evidence of a post‐transfer, follow‐up visit based on NHLS CD4 count and VL data at approximately three years. We consider 61% to be a low estimate, based on the 221 who reported transferring to a private doctor in our prior study In the multivariable analysis, male sex was a significant predictor of not having a post‐transfer record found in the NHLS CDW. Other factors that we evaluated  were not significantly associated with not having a post‐transfer record. These findings are consistent with our results from a previous study evaluating linkage to an initial transfer clinic visit, in which we found that age and most recent CD4 count were not associated with failure to be found at an assigned validation clinic In the ART duration adjusted model, spending <3 months on ART prior to transfer substantially increased the risk of not having a post‐transfer record. As detectable viral load was not a predictor of slower linkage, and missing VL information was strongly associated with transfer within three months of starting ART, ART duration, rather than viral suppression, may be a more appropriate measure for defining “high risk” transfer patients.There are several limitations to using national laboratory data as a marker of cumulative frequency of linkage to care. Our results are an estimate of patients who re‐linked to care following transfer from McCord Hospital. This estimate is lower and may be more accurate than our previous estimate of 82%, which was based on patient self‐report of re‐linkage and validation of an initial clinic visit for a subset of patients Despite the drawbacks of this methodology, this study has several important strengths. Using NHLS data can transcend the limitations of collecting and analysing data within individual programmes, which presents challenges such as differences in record‐keeping methods and marked variability in how patients are identified. The ability to accurately classify “lost to follow‐up” patients as having actually transferred care elsewhere is important for evaluating the quality of the national governmental ART programme, especially as known Centers of Excellence have been disbanded 5The cumulative frequency of linkage to care after large‐scale patient transfers is difficult to assess but has become increasingly important as PEPFAR funding in South Africa decreases and patient care transitions to public sector community clinics. We found that a national data set collected for clinical purposes may be used for evaluating linkage of individual patients within public, government‐funded clinics. This ability to track patients may not only aid in the evaluation of re‐linkage and retention outcomes following HIV‐clinic closures and patient transfers but may also serve as an effective method to study patient flow and movement more generally within the HIV care system in South Africa, such as following transfer to postpartum HIV care. Additionally, this study utilizes NHLS data in a novel way. While NHLS CDW data have been used in several cross‐sectional studies The authors have no competing interests to declare.All authors have contributed significantly to this work and have reviewed and approved of this manuscript. IVB, Principal Investigator of this project, led the design and execution of this study as well as all stages of manuscript writing and preparation. MH and RAP led all data analysis efforts. MH initially helped to conduct the preliminary data analysis, while RAP collaboratively refined the analysis presented in the manuscript. RAP also contributed substantially and oversaw all method and analysis development. CC and JG both played significant roles in initial data collection and the procurement of records from McCord Hospital. SC also played a significant role in the procurement of CD4 and viral load records from the National Health Laboratory Services, which were used in the data crossmatch. EL, KAF, and RPW provided consultation during analysis development and contributed to manuscript editing and review. Additionally, EL provided biostatistical consultation. SCF, the Research Assistant, contributed significantly to manuscript writing, editing, and review.


Antiretroviral therapy (ART) has become the standard of care for patients with HIV infection in South Africa and has led to the reduction in AIDS related morbidity and mortality. In developing countries, the nucleosides reverse transcriptase inhibitors (NRTIs) class are widely used because of their low production costs. However patients treated with NRTIs develop varying degree of toxicity after long-term therapy. For this study patients are administered with a triple therapy of two NRTIs and one non-nucleoside reverse transcriptase inhibitor (NNRTI).In this study the progression of HIV in vivo is divided into some viral load states and a continuous time-homogeneous model is fitted to assess the effects of covariates namely gender, age, CD4 baseline, viral load baseline, lactic acidosis, peripheral neuropathy, non-adherence and resistance to treatment on transition intensities between the states. Effects of different drug combinations on transition intensities are also assessed.The results show no gender differences on transition intensities. The likelihood ratio test shows that the continuous time Markov model for the effects of the covariates including combination give a significantly better fit to the observed data. From almost all states, rates of viral suppression were higher than rates of viral rebound except for patients in state 2  where rates of viral rebound to state 3  were higher than rates of viral suppression to undetectable levels. For this transition, confidence intervals were very small. This was quite notable for patients who were administered with AZT-3TC-LPV/r and FTC-TDF-EFV. Although patients on d4T-3TC-EFV also had higher rates of viral rebound from state 2 than suppression, the difference was not significant.From these findings, we can conclude that administering of any HIV drug regimen is better when based on the viral load level of an HIV+ patient. Before initiation of treatment, patients should be well equipped on how antiretroviral drugs operate including possibilities of toxicity in order to reduce chances of non-adherence to treatment. There should also be a good relationship between patient and health-care-giver to ensure proper adherence to treatment. Uptake of therapy by young patients should be closely monitored by adopting pill counting every time they come for review. The first acquired immunodeficiency syndrome (AIDS) case emerged in the early 1980s and since then, the AIDS prevalence has been increasing , 3.In South Africa, the antiretroviral therapy available at the present moment are the nucleotides reverse transcriptase inhibitors (NRTIs) class which includes among others zidovudine (AZT), didanosine (ddI), lamivudine (3TC) and stavudine d4T) . Other NT . OtherHowever, patients treated with NRTIs develop varying degrees of myopathy or neuropathy after long-term therapy . AZT cauScience literature has successfully established the efficiency of ART in controlling HIV; however its effectiveness depends particularly on the adherence of patients to ART . AdherenThe development of drug-resistant variants that can develop in HIV/AIDS patients under ART makes it not feasible to completely eradicate the virus . This reStochastic models have proved to be the best when dealing with real life situations particularly when modelling biological phenomena such as in vivo HIV dynamics. As the HIV progresses in an individual, there is random movement between states, stochastic models are very good at handling these random variables. Stochastic processes also allow modelling the effects of covariates like stage of infection, virus subtype, presents of STIs, sexual practices, condom use, religion, education, age, gender and genes on transition intensities. In particular, time-homogeneous Markov models are usually used to model the evolution in chronic diseases .k = 6 states belonging to the state space S = {1, 2, …, k = 6}. Consider the ith individual being in some state at time t. Let X(t) denote the state occupied by a randomly chosen individual at time t. Assuming that the individual’s movements obey a continuous time-homogeneous Markov process, then for 0 ≤ s ≤ t the k × k transition probability matrix P with entries:Consider a model consisting of Can be specified in terms of transition intensitiesqij(t) are the entries of the k × k transition intensity matrix Q(t). Since our model is time-homogeneous we consider qij(t) = qij independent of time. Q = (qij) is the transition intensity matrix. For this model, transition probabilities are stationary such that:Here qij in the Markov model. Variables associated with the transition intensities are assumed to have a multiplicative effect of the form;For each of the individuals, covariates are measured. Interest centres on the relationship between the covariates and the transition intensities Z is the s-dimensional vector of covariates. βijs is the vector of s regression parameters relating to the instantaneous rate of transition from state i to state j. i to state j.Where zs is a s-dimensional vector of covariates and βijs represents a vector of vector of s regression parameters relating the transition rates from state i to state j to the covariates Eq.  can be wTij, i; j = 1, …, k, be the total time spent by all individuals in state i before making a transition to state j. we also let bij, i; j = 1, …, k, be the total number of transitions from state i to state j. Then the maximum likelihood estimates of the baseline transition intensities are;Maximum likelihood estimates of the baseline transition intensity matrix can be obtained by maximising the likelihood function with respect to the parameter βij0 are chosen to be equal to zero, which means that there are no covariates effects.Estimates of zsh is the s-dimensional covariate vector for patient h and R is the risk at time t for making a transition from state i to state j.Where In this study, we explore the effects of treatment toxicity (lactic acidosis (LA) and peripheral neuropathy (PN)), non-adherence (NA), treatment line, CD4 baseline, viral load baseline, age on the changes in the level of viral load in the plasma cells. The analysis is done using a time-homogeneous Markov model with covariates. In medical research, the state of the patient at observation time is the only thing known with certainty. The researcher may know the time interval in which a transition has occurred, but not the exact time. Thus, time-homogeneous Markov models which are interval censored can handle such data .In the section that follow, methods used in analysing the data are explored. This is followed by a section 3 on results and discussions. Lastly in section 4 conclusion of the findings is done.3 and 96 had a CD4 baseline above 200 cells/mm3. Upon initiation of treatment a number of factors were considered. These include drug toxicity which results in lactic acidosis and peripheral neuropathy. Other variables include non-adherence to treatment therapy, treatment change, treatment line and resistance to treatment. 101 patients developed lactic acidosis, 43 developed peripheral neuropathy and 36 showed some signs of non-adherence to treatment.The model is applied to 320 HIV-1 infected patients on anti-retroviral therapy (ART) from a Wellness clinic in Bela Bela, South Africa, from year 2005 to year 2009. These patients were observed after 3 months of treatment uptake and every 6 months thereafter. This yielded 2259 observations. From these patients 224 were females and 96 were males. 172 patients were aged between 15 and 45 and 72 were over 45 years of age. The mean age of the patients at enrolment was 40.62 years. 267 had a viral load baseline above 10,000 copies/μL and 49 had a viral load baseline below 10,000 copies/μL. At enrolment, the mean viral load was 138,208 copies/μL with a maximum of 818,600 copies/μL. 226 patients had a CD4 baseline below 200 cells/mmFor each and every visit time, blood samples were obtained for each patient and stored frozen until assayed. Plasma HIV RNA was measured using an amplicator HIV-1 monitor assay kit which has a lower limit of sensitivity of 50 copies/μL.t = 0 the regimens that were mostly administered to patients were the triple combination therapy, d4T-3TC-EFV (208 patients) and d4T-3TC-NVP (92 patients). d4T and 3TC represent Stavudine and Lamivudine respectively which fall under nucleoside reverse transcriptase inhibitors (NRTI) class. EFV and NVP stand for Efavirenz and Nevirapine respectively and are from the non-nucleoside reverse transcriptase inhibitors (NNRTI) class. Table t = 0 to t = 3.5 years.At t = 0, 2 patients were administered with this triple combination. Other treatment combinations that were administered include FTC-TDF-NVP, AZT-ddI-LPV/r, d4T-3TC-LPV/r, ddI-d4T-3TC, FTC-TDF-LPV/r. However, these were not frequently administered and hence they were treated as other (D7) combinations for analysis purpose. The table below shows the frequencies for each of the treatment combinations;In patients who showed some signs of non-adherence, d4T was substituted with AZT (Zidovudine). A switch from d4T-3TC-EFV (D1) to AZT-3TC-EFV (D2) was most common rising from 10 patients in the first 6 months to 92 patients in 30 months . During the same period the number of patients who switched from d4T-3TC-NVP (D3) to AZT-3TC-NVP (D4) rose from 6 to 45. After 1 year of treatment uptake one patient was introduced to FTC-TDF-EFV (D5) and after three and half years, the frequency increased to 10 patients. Another combination of FTC-TDF-NVP was also introduced to 3 patients after 2 years and the number rose to 7 after 3 years. AZT-3TC-LPV/r (D6) was also administered and at The table shows that d4T-3TC-EFV was the most frequently used drug combination.For each visit, viral load in the plasma was also measured. In this study, if the viral load was below 50 copies/μL it recorded it as undetectable. In this study, the progression of HIV/AIDS is defined by change in viral load level. The viral load levels are divided into 5 transient states and the sixth state being the absorbing state, death. The viral load states and other factors that are likely to determine change in viral load levels are defined in the next sub-section.For this study, variables are coded as follows:Age = Non-adherence (NA) = Gender = Treatment Change (TC) = X(t)) = Viral load levels (t = 0 (baseline), that is at treatment commencement; . In this case X(t) = {1, …, 6}. We assume that the transition rate between states for any subject is governed by some covariates identified above. The effects of the covariates; age, lactic acidosis (LA), peripheral neuron (PN), gender, CD4 baseline (CD4BL), treatment line (TL), viral load baseline (VLBL), treatment change (TC), non-adherence (NA) and resistance to treatment (Res) on transition intensities, qij, is assessed. The log-likelihood linking qij with the linear effects of covariates is given by:Results from Table As defined in Eq. .h in this study is given by the model:Thus, the transition intensity for a patient 3, TL = 0 (second line), VLBL = 0 , no TC, no NA and no resistance. The transition intensities, qij, are presented in rates per year. qij are the elements of a 6 × 6 transition intensity matrix Q from a continuous time-homogeneous Markov process. As indicated in Eqs. , CD4 baseline (CD4B), age, gender, treatment line (TL), treatment change (TC), non-adherence (NA), lactic acidosis (LA), peripheral neuropathy (PN), resistance to treatment (Res) and triple therapy (Therapy) was done using the “msm” package in R. The log-linear model as indicated in Eq. , gender, resistance to treatment (Res) and treatment change (TC) had no significant effects on HIV progression based on viral load. As a result, these variables were removed from the model. Results from the model with all covariates are shown in the appendices. Table Table βij’s for cases in which j is a worse state compared to i. Patients with peripheral neuropathy also have accelerated transition rates from state 2 to state 3. Although transition from state 2 to state 1 are accelerated, the rate is slower than that from state 2 to state 3. From the results it can also be noted that having lactic acidosis (LA) accelerates transition from state 2 to state 3 more than either having peripheral neuropathy or non-adherence. Patients who enrolled when their CD4 cell count was below 200 cells/mm3 have higher transition rates from a viral load level between 10,000 and 100,000 copies/μL (state 3) to a viral load between 100,000 and 500,000 copies/μL (state 4). Having a viral load baseline level greater than 10,000 copies/μL at enrolment increases viral rebound from state 2 to state 3.Results from Table The different treatment combinations give precise estimates of the log-linear effects as shown by the confidence intervals that are narrow. Given the different combination therapy administered to patients, transitions to viral rebound are greater than transitions to viral suppression for patients with viral load 2 and 3 .From the different combination therapy that was administered to the patients, D4T-3TC-EFV was the most frequently administered triple therapy with 889 cases, followed by AZT-3TC-EFV and D4T-3TC-NVP and AZT-3TC-NVP with 475; 431 and 279 cases respectively. Table Results from Table Overall, the model shows higher transition rates to viral suppression compared to the transitions to viral rebound. For patients with a viral load between 10,000 and 100,000 copies/μL, drug combination d4T-3TC-EFV has the highest transition rates to recovery followed by the triple combination AZT-3TC-EFV, d4T-3TC-NVP, AZT-3TC-NVP, FTC-TDF-EFV, AZT-3TC-LPV/r respectively. When the viral load is still above 100,000 copies/μL, the triple combination AZT-3TC-LPV/r gives the best results followed by FTC-TDF-EFV, AZT-3TC-NVP, d4T-3TC-NVP, d4T-3TC-EFV, d4T-3TC-NVP, AZT-3TC-EFV in that order. However, for this cohort AZT-3TC-LPV/r as not frequently administered. For patients in state 2, viral rebound to state 3 is greater than viral suppression to undetectable levels and these rates of viral rebound are the highest for patients being administered with triple combinations AZT-3TC-LPV/r and FTC-TDF-EFV.t = 0, the present time and death (absorbing state). For a patient in state r at t = 0, the expected total time the patient is to spend in state s before relapse to death is given by:prs is the probability of transition from state r to state s. The value of state r is by default set to be 1 . The results are given below.Below are the expected amount of time spent in each state from Thus the patients are forecasted to spend approximately 18.5 years in a state of undetectable viral load (state 1) and in the other states patients are expected to spend less than 2.5 years since these are temporal states. This is evidenced by the fact that throughout the 5 year study period, only 17.8% of the patients were reported dead with 10.9 points occurring during the first 6 months.In order to assess the goodness of fit of the continuous time-homogeneous Markov model for the effects of covariates, the expected percentage prevalence is plotted against the observed percentage prevalence. The prevalence is averaged over the covariates observed in the data. The percentage prevalence is plotted as functions of time for each viral load state. Figures t = 0 state 1 had the least percentage prevalence, from 1 year onwards about 80% of the patients had attained an undetectable viral load (state 1). States 2 and 3 had the highest percentage prevalence at t = 0, however in less than 6 months of treatment uptake, the percentage prevalence had dropped  + 2(k) where k is the number of parameters in the model. For example, the model with covariates excluding the combination therapy (VLS3.cov.msm) has got 26 degrees of freedom and −2 ×  log (likelihood) = 2635.207, thus AIC = 2635.207 + 2 × 26 = 2687.207 as shown in Table A further assessment of the fitted models is done using the Akaike Information Criteria (AIC). For each model, Results from Table This study is carried out from a cohort of HIV+ patients receiving antiretroviral therapy in Bela Bela South Africa. Using the data, four nested continuous time homogeneous Markov models were fitted. The first one had no effects of covariates, the second one had the log-linear effects of covarites without combination therapy, the third one had the log-linear effects of different combination therapy and the last one had the log-linear effects all covariates including combination therapy. These covariates include; adherence to treatment, development of drug toxicity in the form of peripheral neuropathy and lactic acidosis, change in treatment therapy, gender, age, CD4 baseline and viral load baseline and resistance to treatment on transition intensities are assessed. From the fitted model the variables; gender difference and change of treatment do not exhibit any significant effects on the transition intensities hence they were removed from the model.The fitted models were assessed using the AICs and pairwise likelihood ratio test. The continuous time Markov model with all covariates including combination therapy had the lowest AIC an indication that it gives the best fit of the data than all the other models and also the likelihood ratio test revealed that it fits well. This as further confirmed by the likelihood ratio test which showed that the model with all covariates including combination therapy fits significantly better than any other model nested within it. Exclusion of covariates had caused some irregularities in predicting mortality which were corrected after the inclusion of covariates effects in the fitted model.The results from the analysis showed that although close to 80% of the individuals had their viral load suppressed to undetectable levels in the first year of treatment uptake, some viral rebound were also notable particularly from state 2  to state 3 . Further analysis showed that this rebound was accelerated by non-adherence to treatment, lactic acidosis and resistance to treatment. However, for patients who developed peripheral neuropathy, there is an accelerated transition to both viral rebound and viral suppression from state 2 although the rate of viral rebound is greater than the rate of viral suppression. For these patients when the viral load is above 100,000 copies/μL there are reduced rates of viral suppression. This corroborates work done by Simpson who argued that greater incidences of peripheral neuropathy are in the strata of patients with plasma HIV RNA levels greater than 10,000 copies/μL . PatientDisease progression is faster on patients below the age of 45 compared to patients over 45 years in the cohort. This shows that older patients have a better understanding of the treatment therapy resulting in a better adherence to the treatment therapy. On the other hand, young patients have substantial challenges in achieving level of adherence necessary for successful therapeutic outcomes.3 accelerates the transitions from state 3 to state 4 . This is also the case with younger patients below the age 40 years.From state 3 , rates of viral suppression are higher than the rates of viral rebound particularly for patients administered with d4T-3TC-EFV. A CD4 baseline below 300 cells/mmNon-adherence accelerates viral rebound for patients with viral load levels between 100,000 and 500,000 copies/μL (state 4). This supports the issues raised by Chesney  that witOverall, the model shows higher transition rates to viral suppression compared to the transitions to viral rebound.This study has revealed the major attributes to viral rebound on HIV+ patients which is notable as patients attain a viral load level between 50 and 10,000 copies/μL. The major attributes were non-adherence, lactic acid, resistance to treatment, and different combination therapy like AZT-3TC-LPV/r and FTC-TDF-EFV. However, assuming that the patient was initially in state 1  he is expected to spend approximately 18.5 years in state 1 before he dies. This is evidenced by the fact that throughout the 5 year study period only 17.8% of the patients were reported dead with 10.9 points occurring during the first 6 months.Hence the need to administer HIV drug regimens is better based on the viral load level of a patient. Before initiation of treatment, patients should be well equipped on how antiretroviral drugs operate including possibilities of toxicity in order to reduce chances of non-adherence to treatment. There should also be a good relationship between patient and health-care-giver to ensure proper adherence to treatment. Uptake of therapy by young patients should be closely monitored by adopting pill counting every time they come for review.


Virological treatment failure is a problem that a Human Immune Virus patient faces after starting treatment due to different factors. However, there were few studies done on the predictors of virological treatment failure among adult patients on first-line antiretroviral therapy in Ethiopia in general, and no study was done in the study area in particular. Therefore, the aim of the study was to identify predictors of virological treatment failure among adult patients on first-line antiretroviral therapy in Woldiya and Dessie Hospitals, Northeast Ethiopia.Hospital based case–control study was conducted in Woldia and Dessie Hospitals from from 12 August 2016–28 February 2018 on 154 cases and 154 controls among adult patients on first-line antiretroviral treatment. All cases were included and comparable controls were selected using stratified random sampling technique. Data were collected by document review using checklists and entered into Epidata version 3.1 and analyzed by SPSS version 21.Multivariable logistic regression analysis was done to identify the independent predictors of virological treatment failure.3  compared withCD4 T-cell count of > 200 mm3, current body mass index(BMI) < 16 kg/m2  compared with BMI > 18.5 kg/m2, BMI between 16 and 18.5 kg/m2  versus BMI > 18.5 kg/m2, poor adherence to antiretroviral therapy  compared with good adherence.In this study, statistically higher odds of virological failure was observed among patients who had current CD4 T-cell count of < 200 mmThis study showed that low current CD4 T-cell count and body mass index, as well as poor adherence for ART treatment predicts virological failure. Therefore, deliberate efforts are urgently needed in HIV care through improving their nutritional status by enhancing nutritional education and support, and by strengthening enhanced adherence counseling. The global scale-up of antiretroviral treatment (ART) under the public health approach of standardized and simplified regimens has registered significant gains, with increasing access to treatment for millions of people, and a reduction in new infections and Human Immune Virus(HIV)-associated morbidity and mortality . GloballThe primary goal of ART is to prevent HIV-associated morbidity and mortality and an effective ART can reduce viremia and transmission of HIV to sexual partners by more than 96% . TherefoThe World Health Organization (WHO) and Ethiopian National guideline defines virological treatment failure as plasma viral load above 1000 copies/ml  after at least 6 month of ART treatment –6. DiffeAt the individual patient level, failed ART regimen or HIV drug resistance limits treatment options, complicates succession of therapy and puts the patient at increased risk for drug toxicity , 16 whiccontrols .Facility based unmatched case control study conducted in Woldia General and Dessie Referral Hospitals, North East Ethiopia from 12 August 2016–28 February 2018 with data extraction period from March 13–27/2018. Both Hospitals started ART services in 2005 and serving for HIV infected patients until now. Routine viral load test was started after 12 August 2016 in both hospitals. According to the information from the records of the hospitals, between 12 August 2016 to 28 February 2018, there were 9013 adult patients on first –line ART that had two consecutive documented viral load tests result in Woldiya and Dessie hospitals. From 9013 adult patients that had two consecutive documented viral load test result in both hospitals, 154  and 534  of them had viral load result of above 1000 and ≤ 1000 copies/ml, respectively. All HIV-infected patients aged 15 years and above who had taken first-line ART for at least 6 months with two consecutive documented viral load test results were the source population. All cases and selected controls who had documented viral load test results from 12 August 2016–28 February 2018 were the study population. HIV-infected patients aged 15 years and above whose plasma viral load was > 1000 copies/ml in 2 consecutive viral load measurements in a 3-month interval with enhanced adherence support after at least 6 months of starting first-line ART regimen is defined as cases  whereas HIV-infected patients aged 15 years and above whose plasma viral load was < 1000 copies/mL in 2 consecutive viral load measurements after at least 6 months of starting first-line ART regimen is defined as Sample size was determined using Epi Info7 version 3.5.3, by taking age less than 35 years as predictor of virological treatment failure which gave larger sample size , 1:1 casData were extracted by document review using a structured checklist prepared in English adapted from Ethiopian Federal Ministry of Health ART clinic intake and follow up form. Data collectors and supervisors were trained for two days about the objectives of the study, contents of tools and how to collect the data before the data collection. The data was collected by 6 ART trained nurses that work at ART clinic. Two runners were used for bringing cards from the card room. The principal investigator and the supervisors were closely monitored the whole data collection process on a daily basis. To keep the quality of the study, document review checklist was prepared based on federal ministry of health of Ethiopia standard ART intake and follow up form. Data collectors and supervisors were ART trained nurse. Training was given for data collectors and supervisors before data collection and there was close follow up of data collectors by supervisors and the principal investigator including observation of how they were extracting the recorded data. Moreover, data quality was also ensured during collection, entry.p-?>value < 0.25 in bivariate analysis were included into a multivariable logistic regression analysis using backward likelihood ratio method to identify the independent predictors of virological treatment failure. The final model was assessed for goodness-of-fit using Hosmer–Lemeshow test. No evidence indicating lack of fit was found . Finally, variables that had significant associations with virological treatment failure were identified based on the adjusted odd ratio (AOR) with a 95% CI and p-value < 0.05. And also, effect modifications among independent predictors were assessed using interaction term and no effect modification was found.Data were checked for completeness, coded and, finally it was entered into Epi Data version 3.1, cleaned and analyzed by using SPSS version 21. Descriptive statistics, including frequencies mean and percentages were used to describe demographic, clinical, and treatment-related characteristics of patients. Binary logistic regression analysis was carried out for independent variables with an outcome variable to select candidate variables for multivariable analysis. Variables with a A total number of participants included in the study were 308 (154 cases and 154 controls). At baseline, the mean age of the cases and controls were 30 years (SD: 8 years) and 31 years (SD: 9 years) respectively. About 55.2% of cases and 53.9% of the controls were females; likewise, 47.4% of cases and 52.6% of the controls were married. Similarly, 38.3% of cases and 42.9% of the controls were attained primary education. By their occupation, 33.8% of cases and 30.5% of the controls were farmers. About 69.5 and 74% of the cases and controls, respectively were, disclosed their serostatus . The study showed 29.2% of cases and 11.7% of the controls had history of regimen change/individual drug  were all included in multivariable logistic regression model using backward likelihood ratio method (LR) it was found that current BMI, adherence level to ART treatment, and Current CD4T-cell count had statistically significant association with virological failure .When variables that had association with virological failure in the bivariate analysis  among those who had Current BMI of < 16 kg/m2 compared with those who had Current BMI of > 18.5 kg/m2. In addition, the odds of virological failure was 5.4 times more  among those who had poor adherence compared with those who had good adherence to antiretroviral treatment  among those who had Current CD4 T-cell count of ≤200 mm3), low current BMI (< 16 kg/m2 and 16–18.5 16 kg/m2), Poor adherence to ART were found to have increased odds of virological failure.This study was aimed to assess predictors of virological failure among first-line ART users and showed that low current CD4 T-cell count count  CD4 count, low current (recent) body mass index, and poor adherence to antiretroviral treatment. Therefore, deliberate efforts are urgently needed in HIV care by concerned bodies like ART case managers, adherence counselors in the hospitals on patients with low body mass index, low current CD4 count , and improving poor adherence to ART treatment by strengthening enhanced adherence counseling.


We evaluated a home-based adherence intervention and characterized self-reported adherence, virological response and drug resistance among adolescents failing atazanavir/ritonavir (ATV/r)-based 2nd line treatment with virological failure  ≥1 000 copies/ml) were randomized to either standard care (SC) or SC with addition of modified directly administered antiretroviral therapy (mDAART) for 90 days. VL was measured and questionnaires were administered at study entry and at 3 months. Genotyping was done for participants with continued failure. Primary outcome was suppression to VL < 1 000 copies/ml.HIV-positive adolescents (10–18 years) on ATV/r-based 2nd line treatment for >180 days were enrolled, 23(46%) were randomized to mDAART and 27(54%) to SC. Fifty-four percent were female; mean age was 15.8 years; mean baseline VL was 4.8(log10) copies/ml; 40% reported adherence <80% in previous 1 month at baseline; 40% suppressed (VL <1 000 copies/ml) after follow-up. mDAART resulted in significantly increased self-reported adherence ; closely following dosing schedule ; VL decrease (p=0.031) and modest increase in virological suppression to <1 000 copies/ml (p=0.105). Genotyping in 28/30 participants with continued virological failure demonstrated high level atazanavir resistance  in 6(21%); 3(11%) of whom also had high level resistance to lopinavir and darunavir .Fifty adolescents aged 10–18 years on 2nd line treatment failure, significantly increased self-reported adherence and decreased viral load. High level ATV/r resistance was demonstrated.The mDAART intervention modestly improved virological suppression among adolescents with ATV/r-based 2nd line treatment decreases viral load and increases self-reported adherence. Early drug-resistance testing could reduce morbidity and mortality.Targeting mDAART to adolescents who are virologically failing PI-based 2 However, sub-Saharan Africa (SSA) continues to bear the highest burden of HIV infection in the world, accounting for about 90% of all HIV infections . About 2reatment . Adolesc failure .Virological failure in adolescents is thought to be a result of poor adherence . Patersond line treatment failure. Poor adherence selects drug resistance mutations due to on-going viral replication at sub-inhibitory PI concentrations -based 2nd line treatment for ≥6 complete consecutive months; had virological treatment failure ; knew their HIV status; provided informed consent and assent; were registered at Harare hospital paediatric opportunistic infections clinic and stayed within Harare hospital catchment area. Adolescents were consecutively screened for eligibility using a questionnaire and viral load measurement. The screening viral load was also used as baseline for enrolled participants. Participants were excluded if they were on anti-TB treatment; did not want to be followed-up at home; had viral load <1 000 copies/ml within the previous 2 months or were on ATV/r as 1st line treatment.A randomised, controlled trial (RCT) comparing modified directly administered antiretroviral therapy (mDAART) + standard care (SC) versus SC + self-administered treatment (SAT) for 90 days. Data was collected between January 2015 and May 2016. Eligible participants were included if they: were HIV positive with a documented result; were aged between 10 and 18 years; were on ATV/r-based 2st or 2nd line treatment. The study was divided into 2 phases:Total patient sampling of eligible, assenting and consenting adolescents was considered after noting that the clinic had 267 children, adolescents and young adults on boosted protease inhibitors from 0 to 22 years of age, either as 1Eligible participants were randomised to intervention (mDAART + SC) or control (SC + SAT) arms. Randomisation was done using random numbers sealed in opaque envelopes. Questionnaires were administered at baseline and after follow-up. Participants were followed for 90 days. At the end of follow-up, viral load was measured again. Self-reported adherence was measured using AIDS Clinical Trials Group (ACTG) adherence follow-up questionnaire (QLO702) and visual analogue scale (VAS) .Participants with continued treatment failure  had genotypic HIV drug resistance testing. Components of each arm are summarised in www.HIVDB.stanford.edu) to interpret drug susceptibility.Samples for viral load and HIV drug resistance testing were collected in 2×4 ml K-EDTA tubes respectively, gently inverted 8 to 10 times to prevent clotting, transported at atmospheric temperature to the laboratory. The Roche COBAS AmpliPrep/COBAS Taqman HIV-1 Test version 2.0 was used for viral load measurement, with a linear range of 20 to 10,000000 copies/ml. HIV drug resistance mutations were generated by the Celera ViroSeq® HIV-1 genotyping system version 2.0 (Abbott Molecular Diagnostics). Sequencing was done on 3500 Genetic Analyser supplied by Thermo Fisher, Life Technologies. Mutations were identified with ViroSeq software and analysed with the Stanford database , Biomedical and Research Training Institute (BRTI) and Medical Research Council of Zimbabwe (MRCZ/A/1840). This clinical trial was registered with Pan African Clinical Trial Registry (PACTR201502001028169) and National Institutes of Health (NIH) Data from questionnaires was entered into research electronic data capture (REDCap), a web-based application . All datst line, 2 line and total time on ART; baseline, follow-up and change in viral load; pill burden per day; dosing frequency and body-mass index (BMI)-for-age  clinical stage at ART initiation; baseline, latest and on-treatment peak CD4 cell counts; time on 1-for-age . StepwisFifty participants were recruited. Of the participants who were screened, 53/108 (49%) were virologically suppressed . One hundred and six (98%) participants accepted home visits. Only 2/108 (2%) participants who were eligible refused home visits citing their intrusive nature. Twenty-three (46%) and 27(54%) participants were randomised to intervention and control arms respectively .st line ART, 68% had WHO clinical stage 3 or 4 disease, and 42% had a CD4 cell count <200 cells/mm3. At enrollment into study, 52% had CD4 count <200 cells/mm3 and 30% had low BMI-for-age (thinness or severe thinness). Eighty-six percent were taking tenofovir/lamivudine (300 mg/300 mg) fixed dose combination (FDC) and ATV/r (300 mg/100 mg) FDC; 90% were taking a total of 2 to 4 ART tablets (including cotrimoxazole prophylaxis) a day; and 90% were taking ART (including cotrimoxazole prophylaxis) once a day. Mean total time on ART was 78 months (Mean age was 15.8 years. Most participants were either in secondary or high school (form 1–6) (78%). There were more females (54%) than males. 46% were double orphans. Only 20% lived with their biological parent(s). At initiation of 18 months .10 copies/ml, 74% had overall decrease in viral load, 46% had ≥1 log10 decrease in viral load and 40% achieved virological suppression   .Common reasons for missing ART were simply forgetting (68%), being away from home (62%), problem with keeping time (50%) and falling asleep before taking medication or waking up late (46%) .52% of the participants in mDAART achieved virological suppression compared to 30% in standard care. There was a modest increase in viral load suppression in mDAART compared to SC after stratifying by viral load <1 000 vs ≥1 000 copies/ml (p=0.105). Viral load decreased more in mDAART arm compared to standard care (p=0.03) and viral load at follow-up was lower in mDAART compared to standard care (p=0.04). Average self-reported adherence in previous 1 month measured by visual analogue scale at follow-up was higher in mDAART compared to standard care (p=0.05), and the number of participants who reported closely following their dosing schedule in the previous 4 days was higher in mDAART compared to standard care at follow-up (p=<0.001) .There were no significant differences between suppressed and unsuppressed participants. Multivariate models were assessed comparing mDAART to SC, fitting self-reported adherence characteristics associated with virological suppression .Participants in mDAART were 90% less likely to report <80% adherence in the previous 1 month (p=0.023), were 4.8 times more likely to closely follow their dosing schedule in the previous 4 days (p=0.004) compared to those who were not exposed to the intervention .rd line integrase strand transfer inhibitors (InSTI)-based regimens . The other 3 had no resistance to LPV/r, and were switched to LPV/r, which is the available alternative 2nd line treatment (Thirty (60%) participants had viral load >1 000 copies/ml at 3 months and 28/30 (93%) had a genotypic HIV drug resistance test within 1 month of follow-up viral load measurement. Three (11%) participants had wild type virus . PI resireatment . The mosreatment .nd line treatment was associated with a significantly greater decrease in viral load and increase in self-reported adherence compared to standard care, and it also modestly increased virological suppression. Our findings support earlier findings which found that DAART decreases viral load by an effect size between 20 and 30% and increases self-reported adherence when targeted to at-risk populations (Directly observed treatment (DOT) has been successfully implemented in anti-TB treatment. However, its use in HIV treatment is controversial. In our study, a short-term mDAART intervention provided to adolescents failing 2ulations . At-riskWe also found that 40% of adolescents had adherence <80% at baseline. Adolescent adherence to treatment is lower than that for children and adults . As chilForgetfulness was the most common cited reason for missing doses, and concurs with findings from earlier studies in adults . mDAART nd line regimens are often going to school. A community or clinic-based DOT intervention could face challenges in implementation due to busy lifestyles and stigmatisation. A home-based adherence intervention offers lesser burden to adolescents. However, the cost involved in mDAART, the intrusive nature of the intervention, breach of confidentiality of HIV status and migration of participants pose challenges to implementation. If DAART is going to be implemented, there needs to be careful consideration to confidentiality of patients’ HIV status, convenience to the patient and flexibility. Community health workers can assume this responsibility as they are familiar with communities they work in and have a portfolio full of other responsibilities . Use of technology  could reduce the need for many physical home visits. Family members/friends could also observe dose ingestion on days that mDAART will not be done. Once daily ART regimens also ease implementation DAART.Acceptance rate for home visits in our study was surprisingly higher than previously reported . This find line ART was shorter than time on 1st line ART in this study. This finding concurs with findings from previous studies, and is worrying. Risk of subsequent treatment failure increases after 1st line failure , resulting in treatment failure. Our sample was also small for power to be adequate.