Screening for renal, hepatic and haematological disorders complicates the initiation of current first-line antiretroviral therapy (ART). Each additional test done adds substantial costs, both through direct laboratory expenses, but also by increasing the burden on health workers and patients. Evaluating the prevalence of clinically relevant abnormalities in different population groups could guide decisions about what tests to recommend in national guidelines, or in local adaptations of these.As part of enrolment procedures in a clinical trial, 771 HIV-positive adults, predominantly from inner-city primary health care clinics, underwent laboratory screening prior to ART. Participants had to be eligible for ART, based on the then CD4 eligibility threshold of 350 cells/μL, antiretroviral naïve and have no symptoms of peripheral neuropathy.Participants were mostly female (57%) and a mean 34 years old. Creatinine clearance rates were almost all above 50 mL/min (99%), although 5% had microalbuminuria. Hepatitis B antigenaemia was common (8% of participants), of whom 40% had a raised AST/ALT, though only 2 had transaminase levels above 200 IU/L. Only 2% of participants had severe anaemia (haemoglobin <8 g/dl) and 1% neutropaenia (neutrophils <0.75 × 10^9/L). Costs per case detected of hepatitis B infection was USD135, but more than USD800 for a raised creatinine.Hepatitis B continues to be a common co-infection in HIV-infected adults, and adds complexity to management of ART switches involving tenofovir. Routine renal and haematological screening prior to ART detected few abnormalities. The use of these screening tests should be assessed among patients with higher CD4 counts, who may even have fewer abnormalities. Formal evaluation of cost-effectiveness of laboratory screening prior to ART is warranted. The trend over the past decade has been to initiate antiretroviral therapy (ART) in HIV-positive patients at higher CD4 counts: ART initiation thresholds in WHO guidelines were 350 cells/μL in 2010, 500 cells/μL in 2013, and then ultimately treatment was recommended for all HIV-infected patients in 2015 [The major side effect of TDF is renal failure, secondary to tubular dysfunction . This isTDF in combination with FTC, or its analogue 3TC, is also a potent suppressor of hepatitis B, which is common in the general adult Southern African population , 7. RoutA group of patients presenting for routine HIV care in central Johannesburg, had biomedical tests done as part of screening for a clinical trial. These tests documented the prevalence of renal disease, hepatitis B , anaemia and neutropaenia. Information on the frequency of abnormal results on these tests, which are used for screening prior to ART, could help inform decisions about the usefulness of these tests are in our population and similar groups elsewhere.Antiretroviral naïve adults attending outpatient HIV testing and treatment services in central Johannesburg were invited to participate in a clinical trial (WRHI 001)  by commuOnce informed consent processes were completed, blood and a urine sample were obtained. Laboratory procedures included a full blood count, CD4 cell count, creatinine clearance, microalbuminuria measurement, urine dipstick, hepatitis B antigen test, liver function test and an HIV plasma viral load. CD4 testing was done using Beckman Coulter FC500 machines.Participants’ demographics and health status  were used to describe the study population. The remainder of the indicators assess the prevalence of renal, hepatic and haematological disease in the population. A creatinine clearance <50 mL/min was categorised as abnormal, as it is the recommended cut-off for use of TDF according to national guidelines . Microaln = 91). More than 40% of the cohort were from outside South Africa, predominantly from Zimbabwe (38% of participants). Three quarters were employed and the same proportion described themselves as being single. Around a third reported current alcohol use (36%) and 16% smoke. Only 3% of the participants had WHO Stage IV disease and 16% Stage III conditions. CD4 cell counts at screening were a median 208 cells/μL (inter-quartile range = 118–299), with only 8% having a count below 50 cells/μL and 13% above 350. Plasma viral load varied markedly between patients, with 18% having fewer than 10,000 copies per ml, while 39% had counts above 100,000.Most of the 771 patients screened for trial eligibility were from inner-city clinics in Hillbrow Johannesburg (93%), while 7% were from the nearby CMJAH hospital. Population characteristics, health status and results of laboratory screening tests are summarised in Table One percent of patients had a creatinine clearance below 50 mL/min. The CD4 cells/μL for these four patients were 128, 306, 293 and 131. Microalbuminuria was not infrequent, occurring in 5% of patients. Prevalence of proteinuria was, however, very low, measured either by microalbuminuria or 3+ proteins on urine dipstick. Prevalence of hepatitis B antigenaemia was 8%. Almost 40% of patients with hepatitis B had a raised transaminase, of whom two patients had an ALT or AST above 200 IU/L . Among the whole cohort, mild elevations of ALT/AST levels  were relatively frequent (19%), and a further 4% had even higher levels.p ≤ 0.001). Overall, 9% of patients had anaemia and 2% severe anaemia, with the frequency of anaemia in women double that of men. Only 1% of participants had neutropaenia (neutrophils <0.75 × 10^9/L). Four percent of participants had a platelet count below 125,000 cells/μl, and these were mostly around the 100,000 mark. The costs in our population of identifying one case of hepatitis B virus was USD135, and one abnormal result for creatinine clearance USD820, microalbumin USD18, AST or ALT above 40 USD19, and AST or ALT above 100 USD126.The mean haemoglobin was 12.0 g/dL (sd = 1.7) in women and 13.9 g/dL (sd = 2.0) in men , CD4 count >350 cells/μL (96), elevated creatinine clearance (5), anaemia (4), thrombocytopaenia (5), and elevated AST/ALT level (3). Some patients had multiple reasons for study exclusion.The study showed that relatively few patients had abnormal results on the tests used for screening for ART eligibility in South Africa. The prevalence of abnormal tests may be even lower among the relatively more healthy cohorts who are initiating ART presently, given that all HIV-infected patients are now eligible for ART, regardless of CD4 count. In particular, our findings raise questions about the utility of routine creatinine clearance testing before initiating TDF in our setting. Significant renal dysfunction was very uncommon in our study, as measured by creatinine clearance, although microalbuminuria was relatively frequent. As further data accrues on this topic, ART guideline committees could consider minimising the number of tests done to identify relative and absolute contraindications to the use of certain antiretrovirals. Alternatively, future guideline iterations may consider continuing the screening tests, but recommending that ART could be initiated while waiting for the results and then patients regimens be altered at the subsequent visit, if required.Findings from several other South African cohorts on creatinine clearance have been conflicting, with most also reporting low levels of renal dysfunction, but a few recording abnormal results in as many as 5% of patients initiating antiretrovirals –17. All Hepatitis B infection was common, in keeping with previous reports, both from the general population, as well as from other HIV clinics in South Africa –23. It iMild anaemia was very common, but significant anaemia (<8 g/dl), the threshold suggested in local guidelines as a contraindication to AZT, was very unusual, in keeping with other studies , 25. In Limitations of the study include the difficulties in generalising the findings to South Africa as a whole, given the urban nature and high percentage of Zimbabweans in the screened population. The findings, however, might apply to similar inner-city areas of the country, or even to parts of Zimbabwe. Also, participants in this study were on average relatively young and the creatinine clearance abnormalities may be more frequent among older patients. Excluding patients with peripheral neuropathy or exposure to antiretroviral drugs for preventing mother-to-child transmission of HIV may also limit generalisability of results, although unlikely to any meaningful degree. The use of community workers to identify potential participants, rather than using systematic sampling, may have incurred selection bias. Community workers could, for example, have been more likely to approach patients they knew, those of the same gender as themselves, or those they believed would be more likely to participate. Having been instructed to refer patients with a CD4 below 400 may have resulted in them purposely recruiting sicker looking patients. Finally, hepatitis B was screened using antigen testing, which may miss occult HBV infections , 26 and The study suggests that there might be minimal value in routine laboratory screening to identify relative and absolute contraindications to the use of commonly used antiretrovirals in patients in inner-city Johannesburg. The value of the renal, hepatic and haematological screening tests prior to ART initiation should be carefully evaluated in further longitudinal studies and in a formal cost-effectiveness appraisal. Careful assessment of the value of screening tests, which can be expensive and delay initiation of ART, is needed when initiation guidelines are constructed, as once the tests have been recommended and they become standard of care, it is becomes difficult to remove those recommendations. In particular, the use of creatinine clearance testing in our population might be re-evaluated once the prospective data from this trial are available.


For patients on antiretroviral therapy (ART), treatment interruptions can impact patient outcomes and result in the accumulation of drug resistance mutations leading to virologic failure. There are minimal published data on the impact of an ART stock shortage on development of drug resistance mutations (DRMs). In this report, we evaluate data from patients enrolled in the Government of Nigeria National ART Program that were receiving treatment at the time of a national drug shortage in late 2003.We conducted a cross-sectional evaluation of samples collected between December 2004 and August 2005 from ART patients in virologic failure that either had a treatment interruption or did not during the late 2003 drug shortage period at the Jos University Teaching Hospital (JUTH). Plasma virus was genotyped, sequence data were edited and analyzed, and mutation profiles were categorized to evaluate predicted drug susceptibility. Data were analyzed to examine factors associated with development of resistance mutations. A genotypic sensitivity score to the alternate recommended regimen was computed to assess drug susceptibility if regimens were changed.A total of 56 patients were included in this evaluation . Patients in the interrupted group had more DRMs than those in the uninterrupted group (p < 0.001); interrupted patients were more likely than uninterrupted patients to have one or more TAM-2 mutations . There was a statistically significant difference in resistance to both d4T  and AZT  by drug interruption status. Examining genotypic sensitivity scores, we found that 67.9% of the interrupted patients, as compared to 25.0% of the uninterrupted patients, did not have full susceptibility to one drug in the regimen to which guidelines recommended they be switched (p = 0.001).In this small observational study, we found evidence of a difference in resistance profiles and ART susceptibility between those that were stocked-out of drug versus those that were not. We believe that these data are relevant for many other low- and middle-income countries (LMIC) that also experienced similar ART shortages as they rapidly scaled up their national programs. It is well documented that treatment interruptions (TI), both structured and unstructured (uTI), have an impact on antiretroviral therapy (ART) outcomes and can result in the accumulation of drug resistance mutations (DRMs) leading to virologic failure (VF) . The majStarting in early 2002, the GoN initiated a National ART Program to treat 10,000 adults and 5000 children infected with human immunodeficiency virus (HIV). The GoN established 26 treatment centers across the country and patients were initiated on a combination of stavudine (d4T) + lamivudine (3TC) + nevirapine (NVP) at the subsidized rate of 1000 Naira . While there was a rapid scale-up of initiating patients on ART, between November 2003 and January 2004, the program experienced an unexpected ART stock shortage, which affected an estimated 8000 patients. Treatment was resumed once the drug supplies were replaced, but the impact of that uTI had not been evaluated. In this study, we analyzed DRMs among HIV-infected individuals that eventually failed ART, stratifying between those that experienced an interruption of ART during the drug shortage period and those that continued to receive their ART.We conducted a cross-sectional evaluation of samples from ART patients that experienced VF between December 2004 and August 2005 at the Jos University Teaching Hospital (JUTH), a Harvard/AIDS Prevention Initiative Nigeria (APIN) Presidents Emergency Plan for AIDS Relief (PEPFAR) Program-supported GoN ART site; all patients had enrolled for ART between June 2004 and June 2005. Interrupted patients were those that did not receive their regular medications between November 2003 and January 2004, while uninterrupted patients were those that were able to receive their regular medication during the same time period. Inclusion criteria for this evaluation consisted of: enrollment in National Program prior to January 2004, ≥ 12 months on ART, age ≥ 18 years, and viral load (VL) count ≥ 10,000 copies (cp)/mL at or after VF.Plasma virus from patients in VF was genotyped in the reverse transcriptase (RT) and protease (PR) genes using the ViroSeq HIV-1 Genotyping System 2.0 Assay . All sequence data were edited and analyzed to generate lists of mutations and polymorphisms. Mutations were categorized according to the International Antiviral Society (IAS)-USA recommendations , 4.Following enrollment in the Harvard/APIN PEPFAR-supported GoN program, patient data were collected and stored using an electronic medical records system (EMRS) . For thePatient characteristics at the time of program enrollment were compared for interrupted versus uninterrupted patients using bivariate methods, including Wilcoxon rank-sum test for continuous variables and Chi square or Fisher’s exact tests for categorical variables, as relevant. We examined the potential association between HIV-1 subtype to DRMs and resistance using the Fisher’s exact test; for statistical analyses, A1, A3, CRF06_cpx and unknown were combined to make an “other” category. To assess if adherence patterns could potentially explain the difference in VL values at sample date, we evaluated the association using Fisher’s exact test. To evaluate the predicted impact of substituting the d4T+3TC+NVP regimen to the recommended alternative regimen of zidovudine (AZT)+3TC+efavirenz (EFV), for patients failing at that time, a genotype susceptibility score (GSS) to the alternative recommended regimen was calculated. GSS was computed based on the drug resistance scores extracted from the Stanford HIVdb. Each ARV drug in the alternative recommended regimen was assigned a score according to the five-level Stanford HIVdb interpretation: 1.00 for susceptible, 0.75 for potential low-level resistance, 0.50 for low-level resistance, 0.25 for intermediate resistance and 0.0 for high-level resistance  compared to 3 (IQR: 0–4) in the uninterrupted arm p = 0.001). The most common nucleoside reverse transcriptase inhibitor (NRTI) mutation for the cohort was M184I/V (76.8%), where 25 (89.3%) of the interrupted patients and 18 (64.3%) of the uninterrupted patients harbored the mutation at the time of sampling , with the median being 1.25 (IQR: 0.75–3.00) for the interrupted group and 0.50 (IQR: 0.25–1.00) for the uninterrupted group. Using a cut-off of GSS < 1, we found that 19 (67.9%) of the interrupted patients, as compared to 7 (25.0%) of the uninterrupted patients, did not have full susceptibility to one drug in the regimen to which guidelines recommended they be switched (p = 0.001). When we limited the GSS comparison to only those patients that had < 12 months between entry into the PEPFAR program and their sample date, GSS remained 1.25 (IQR: 0.75–3.00) for the interrupted group and dropped to 0.38 (IQR: 0.25–1.25) for the uninterrupted group, where 9 (56.3%) of the interrupted patients versus 7 (25.0%) of the uninterrupted patients had susceptibility to none of the recommended drugs (p = 0.04).In this evaluation, we examined impact of uTI on DRMs and drug resistance due to a three-month ART stock shortage by comparing a group of patients that had a temporary interruption of medication to a group that was being treated at approximately the same time, that were not impacted by the drug shortage. In this small, cross-sectional, observational study, we found evidence of a difference in resistance profiles between those that were stocked-out of drug versus those that were not. Technically, since all of the patients included in this evaluation experienced VF, patients in the uninterrupted arm likely also had some level of TI; however, the combined duration of TIs in the uninterrupted arm is not expected to have been as great as that of the patients in the interrupted arm. Furthermore, we found no statistically significant difference in adherence patterns between the groups during the time period between PEPFAR Program entry and their VL sampling date.Interestingly, we observed that over three-quarters of the study arm that did not experience the treatment interruption was female. At the time of the shortage, there was no concerted effort to ensure that more women versus men remained on treatment. Therefore, we suspect that these numbers are partially a result of sampling bias and might also be explained, to some degree, by the better health-seeking behaviors of women compared to men.Overall, the most common mutations seen in this cohort are similar to those found in other studies examining DRMs in patients failing first-line (1L) ART –10 and wThe findings regarding GSS are also noteworthy as we found that patients that experienced the interruption were less likely to have susceptibility to the alternate recommended regimen to which they would have been switched at the time these patients were receiving treatment. Thus, programs that were switching patients that experienced TI and were in VF from the regimen of d4T+3TC+NVP to the alternate of AZT+3TC+EFV were putting them on a suboptimal regimen to which they were likely developing subsequent additional DRMs.As a preliminary investigation of impact of uTI due to drug shortage, our analyses were limited due to the small sample size; this evaluation was not powered to detect a difference by drug interruption status for all the various mutations evaluated and also could not include multivariate methods to examine multiple predictors of resistance. Furthermore, given that this was a cross-sectional observational evaluation, we do not have access to data regarding baseline or transmitted mutations at the time of ART initiation; however, since the patients all started treatment in either 2002 or 2003 and because the country had little access to HIV treatment prior to that time, we suspect that the majority of the patients likely had little to no transmitted mutations or resistance. As we suspect this type of information will be relevant to multiple LMIC, which are in various stages of establishing and growing National ART Programs, we recommend a larger scale study powered to examine other confounders to allow for a better understanding of impact of stock shortages on patient outcomes.


HIV infection is associated with an increased risk of cardiovascular disease in connection with atherosclerosis and thromboembolic complications. The pathogenesis of atherosclerosis is still unclear in this group of patients. Studies on pathogenesis of atherosclerosis in the general population emphasize the role of the extrinsic pathway of blood coagulation, particularly the tissue factor (TF) and tissue factor pathway inhibitor (TFPI). The effect of persistent activation of the immune system on enhanced expression of TF on the surface of monocytes in subjects infected with HIV is known to be correlated with the level of HIV RNA in blood serum.The aim of this study was to evaluate the concentration of TF and its inhibitor TFPI in blood plasma, the impact of traditional and non-traditional cardiovascular risk factors on their concentration and the impact of both markers of haemostasis on the severity of subclinical atherosclerosis as assessed by the intima-media measurement of the carotid artery in HIV infected patients.The study included 121 HIV-infected people with known clinical, immunological and virological status. The control group consisted of 42 healthy individuals, selected in terms of age and sex.Higher concentrations of TF occurred in HIV-infected patients with a low current plasma HIV RNA level, nadir CD4+ T-cell count and longer duration of cumulative antiretroviral treatment. In multivariate analysis, it was the length of cumulative NRTI treatment that impacted on the concentration of TF. The determinants of cardiovascular disease (CVD) risk factors and inflammatory markers did not show any effect on the concentrations of TF.The TFPI level in HIV-infected patients was significantly higher than in the control group and was negatively correlated with the current level of HIV RNA and nadir CD4+ T-cell count, being higher in patients subjected to antiretroviral treatment. It was shown that the higher the cardiovascular risk and the higher the levels of total cholesterol, low-density lipoprotein cholesterol (LDL) and non-high-density lipoprotein cholesterol (non-HDL), the higher the concentrations of TFPI observed. The levels of TF and TFPI were positively correlated with carotid intima media thickness (cIMT); in the multivariate analysis, TF, non-HDL cholesterol and lifetime smoking (pack-years) independently affected the growth of cIMT. A similar effect on cIMT was demonstrated by TFPI. People infected with HIV demonstrate greater severity of subclinical atherosclerosis and a higher incidence of cardiovascular diseases (CVD). Discussions revolving around the causes of this condition point to the role of the HIV infection per se, the metabolic consequences of antiretroviral (ARV) treatment and the severity of certain traditional risk factors in this population. The effects of chronic inflammation, leading to endothelial cell activation and disturbances in the coagulation system, are also stressed .Stimulation of the immune system by HIV leads to increased expression of the tissue factor (TF) on the surface of monocytes . TF is aThis form is bound by microparticles [MP] or membrane vesicles, released from platelets, endothelial cells, smooth muscle cells, monocytes and lymphocytes. Because of the presence of P selectin glycoprotein ligand 1 (PSGL-1) on the surface of MP, it was found that they are capable of attaching themselves to the activated platelets where damaged vessels and thrombus formations occur –8.The main factor inhibiting extrinsic coagulation is the tissue factor pathway inhibitor (TFPI). TFPI is primarily synthesized by endothelial cells and blood platelets, monocytes and fibroblasts ,9. Most The increase in TF expression as well as the endothelial dysfunction and secretion of the pro-inflammatory cytokines is associated with increased prothrombotic properties of blood in people living with HIV. It has been observed that the greater the severity of infection , the greater the risk of thromboembolic complications –13. The To our knowledge this is the first report concerning information on the TFPI levels in people infected with HIV.The aim of this study was to evaluate the concentration of the tissue factor and its inhibitor in blood plasma, assess the impact of traditional and non-traditional cardiovascular risk factors on their concentrations and analyze the impact of both markers of haemostasis on the severity of subclinical atherosclerosis as assessed by the intima-media measurement of carotid arteries in patients infected with HIV.Only blood sampling was an invasive procedure. The protocol was evaluated by the ethics committee and the approval of the local Bioethic Committee's was obtained (KB/no/3/2008). Both HIV-infected patients and patients from the control group provided informed consent to participate in the study, according to the Helsinki Declaration.The study was performed in Lower Silesia, Poland.It was an observational, cross-sectional study which consisted of two groups: the study group of HIV- infected patients and control group matched for age and gender, generally fit. Duration of the study was 4 years. Data characterizing these groups, as well as the parameters of subclinical atherosclerosis, cardiovascular risk and risk factors, data on the virological status, immunological status and antiretroviral treatment were part of a larger observation and follow up ,15.The study was conducted in two groups:The consecutive, routinely scheduled HIV-positive patients attending the Acquired Immunodeficiency Syndrome Outpatient Clinic who fulfilled the inclusion and exclusion criteria were invited to participate in the study. One hundred twenty one patients  were included in the study. The HIV infection characteristics of the patients living in the Lower Silesia region in which our study was conducted are similar  and representative of the entire country.The control group included HIV-negative age- and gender-matched healthy subjects recruited contemporaneously  from the study group region, not meeting the project's clinical and laboratory exclusion criteria. These healthy subjects were recruited at random through two general practices in the region's capital and in the province. Seventy three subjects agreed to participate, of whom 31 were not included—two because of positive HBs antigen, 18 because of data in the medical history indicating the occurence of cardiovascular disease or significant chronic disorders and 11 persons missed the study visit.The inclusion criteria comprised an identified HIV infection and the respondent's informed consent to participation in the study. The following exclusion criteria for study and control group were: currently recognized acute medical condition, currently recognized AIDS, creatinine levels >2 mg/dL, more than fivefold increase of aminotransferase levels. Additionally the history of cardiovascular diseases was the exclusion criterion for the control group. The HIV-infected patients commenced the tests with a known clinical, immunological and virological status with regard to both HIV infection and hepatitis B (HBV) and/or hepatitis C (HCV) co-infection. The study group included 121 patients and the control group—42 individuals. The characteristics of the study and control groups are shown in The study protocol included: medical history, the occurrences of cardiovascular diseases and risk factors, physical examination, resting ECG and anthropometric measurements with a standard calculation of the body mass index (BMI). A positive family history of cardiovascular diseases was based on the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria . AdditioAn assessment was made regarding HIV infection data that included the following: infection duration, route of infection, history of AIDS, HCV and HBV co-infections, current CD4 cell count, nadir CD4, HIV RNA and zenith HIV RNA, type of ARV used during the study period, duration of ARV treatment (in years), cumulative treatment time of the antiretroviral treatment and for the individual classes of antiretroviral drugs.In assessing the impact of antiretroviral therapy on concentration of tested haemostasis markers, only ARV-treated patients were taken into account. 110 individuals received cART  in combination of nucleoside reverse transcriptase inhibitors (NRTI) and protease inhibitors (PI) or non-nucleoside reverse transcriptase inhibitors (NNRTI). The treatment was previously changed several times, mainly because of the toxicity of drugs, and therefore could not be subjected to statistical analysis of the impact of individual antiretroviral drugs on the level of TF and TFPI. The other respondents have not been treated during the observation.In all subjects an echo-Doppler examination of the carotid arteries was performed, registering ultrasound images for further treatment, in order to assess subclinical atherosclerosis with a computer-measured carotid intima-media thickness (cIMT). The carotid ultrasound was performed in B-mode with a high-resolution USG LOGIQ 7 machine from GE with a broadband linear probe of 6–12 MHz and 5x magnification. Four series of images were collected for each patient: common carotid arteries and the bulb on both sides. The images were recorded in three projections for each series: anterior, lateral and posterior. Far wall images of the distal common carotid arteries and bulbs were recorded parallel to the probe surface at the smallest artery diameter and then saved on the hard drive and on DVDs. A place with local cIMT thickening exceeding 1.5 mm was adopted as atherosclerotic plaque .After 14 hours of fasting, blood was collected for laboratory testing, performed by standard laboratory methods: haematology test, levels of aminotransferases, creatinine, glucose, total cholesterol (TC), HDL fraction, triglycerides (TG), fibrinogen, D-dimers, wide range C-reactive protein (CRP) and insulin. Concentration of LDL cholesterol was calculated according to the Friedewald rule, non-HDL cholesterol according to the TC—HDL formula and insulin resistance with the HOMA-IR indicator (homeostasis model of assessment: insulin resistance) according to the following formula: fasting insulin levels (uIU/ml) x fasting glucose (mmol/l) /22.5. Insulin resistance was diagnosed with a HOMA-IR value > 2.5Blood collected for testing of TF and TFPI, and also pro-inflammatory markers concentration levels was put into S-Monovette test tubes containing sodium citrate. These were centrifuged for 15 minutes at +4°C at 2000 x g. Eppendorf micro test tubes were filled with 200 μl of plasma. The prepared samples were banked at -70°C. TF concentration was determined by enzyme immunoassay (ELISA) of the citrate plasma with a reagent kit (IMUBIND® Tissue Factor ELISA from American Diagnostica) and expressed as pg/ml; the test detected TF, TF-apo and TF/VII complex. The TFPI enzyme immunoassay (ELISA) in the citrate plasma was performed with a reagent kit  and expressed as ng/ml; the test detected the full-length TFPI and ‘truncated’ TFPI forms that remained bonded with TF or factor VII.The TNF-α enzyme immunoassay (ELISA) in the plasma was performed with a reagent kit  and expressed as pg/ml. The IL-1β enzyme immunoassay (ELISA) in the plasma was performed with a reagent kit (Human IL-1β ELISA kit from Diaclone) and expressed as pg/ml.Anti-HIV, anti-HBc and anti-HCV antibodies were examined in the control group using the enzyme immunoassay method with microparticles. Plasma HIV RNA levels were tested by the use of Roche Amplicor 1.0 standard assay and CD4 T cell counts by FACScan (Becton-Dickinson) flow cytometry method.The normality of data was tested with the Pearson D' Agostino test. Because normal distribution of variables did not apply, analyses were performed with non-parametric tests. The results of quantitative variables were presented as medians and quartile ranges or in the case of qualitative characteristics with numbers and percentages. The analysis of variations between two quantitative characteristics was performed with the Mann-Whitney test. The analysis of the relationship between two qualitative characteristics was tested with the χ2 test (chi square) or Fisher's exact test in the case of a small number of subgroups. Linear regression analysis was performed to investigate the effect of variables on the characteristics of atherosclerosis. Multivariate regression analysis was performed to assess the independent impact of variables on cIMT and multivariate logistic regression analysis to assess the risk of plaque. The level of significance was set at p<0.05. Program R for Windows (version 3.1.2) was used for the analyses.HIV-infected patients had significantly higher levels of TFPI (p = 0.0001) than the control group; however, differences in the concentration of TF between the two groups were not demonstrated . The TF A significant positive, weak correlation was observed between the concentrations of TF and TFPI in the HIV-positive cohort , whereas weak inverse correlation was observed in the control group  .Among the traditional cardiovascular risk factors high concentrations of total cholesterol, non-HDL and LDL cholesterol significantly increased the concentration of TFPI. A positive correlation has been observed between TFPI level and the cardiovascular risk according to NCEP-ATP III criteria .In the examined group, analyses were also performed with univariate linear regression to study the effect of the status of virological and immunological parameters as well as the correlation with antiretroviral treatment time on the concentration of TF and TFPI. The negative effect was demonstrated of current HIV RNA and nadir CD4+ T-cell count on concentration of TF and TFPI. Strong independent effect of cumulative ARV treatment time and NRTI and PI treatment time was recorded on higher concentration of TF .Concentrations of TF and TFPI were higher in patients with undetectable HIV RNA .In the stepwise multivariate regression model, the effect of the cumulative NRTI treatment time on the concentration of TF was found .After application of the same multivariate regression model for TFPI we found no effect of traditional CVD risk factors on its concentration. A negative, independent effect was demonstrated of the nadir CD4+ T-cell count on concentrations of TFPI .Univariate linear regression analysis revealed a positive correlation between both TF and TFPI with cIMT in the study group .In the multivariate regression model involving risk factors after removal of variables with the backward elimination method, a positive, independent effect of TF, non-HDL cholesterol levels and value of pack-years on the value of cIMT was demonstrated; however, no effect of TF was shown on the presence of atherosclerotic plaque .In the same regression model only the independent effect of the current intensity of cigarette smoking on the presence of atherosclerotic plaque was demonstrated .Based on the observation, that the concentration of TF correlates with the progression of subclinical atherosclerosis, an additional analysis was performed in order to examine whether the high level of TF were accompanied by such risk factors as HIV RNA plasma level, CD4+Tcell count and traditional risk factors for cardiovascular diseases. We found that TF level > 450 pg/ml was presented only in patients on ARV with HIV RNA level below detection limit. This homogenous group was compared with the group of patients with TF level <450pg/ml. The cut-off point was established on the base of the graphic presentation of correlation between TF level and current HIV RNA level .vs. 40 years, p = 0.02), had significantly higher systolic blood pressure , arterial hypertension occurred more frequently  and they also had higher cardiovascular risk . In addition, they were characterized by greater value of cIMT as compared to patients with low concentration of TF .Patients with TF> 450 pg/ml were demonstrating undetectable viral load, the significantly higher current CD4+ T-cell count (p = 0.031) and longer duration of HIV infection (p = 0.59). Moreover, patients with a longer cumulative antiretroviral treatment time (p = 0.003), as well as the cumulative PI (p = 0.006) and NRTI treatment time (p = 0.008), had significantly higher levels of TF . Among tIn the study group a negative correlation between the concentration of TF, current level of HIV RNA and nadir CD4+ T-cell count was demonstrated. It was proven that the cumulative PI and NRTI treatment time, exhibits a strong, independent effect on the concentration of TF in plasma. In addition, it was observed that patients receiving cART with HIV RNA values below the detection limit, had significantly higher levels of TF. High values of TF were observed only in ARV treated patients. Moreover, patients with high levels of TF were older and had more advanced subclinical atherosclerosis and higher systolic blood pressure values, and were treated longer with the antiretroviral therapy.In the present study no effect of traditional cardiovascular risk factors on the concentrations of TF was demonstrated.HIV infection leads to the activation of coagulation processes, which may lead in turn to the development of atherosclerosis and venous thromboembolism. It was observed that the greater the severity of infection , the greater the risk of thromboembolic complications ,12. The The concentration of TF was higher in the study group than in the control group, but the difference was not statistically significant, despite the increased severity of subclinical atherosclerosis in HIV infected patients. Higher concentrations of TFPI were also found in the HIV-positive cohort than in healthy subjects, possibly because of the short duration of the activity and rapid elimination of TF from the circulation and, at the same time, a high concentration of TFPI, which plays an important role in the removal of secreted TF ,8,9.Our observations suggest that in patients receiving cART, who have good response to treatment, with HIV RNA values below detection limit, endothelial injury is mainly the result of the toxicity of PIs and NRTIs, and is manifested by increased levels of TF in the subjects' plasma. Endothelial dysfunction and high values of TF are associated with increased cardiovascular risk, greater severity of subclinical atherosclerotic lesions and hypertension.We also demonstrated that at low nadir CD4+ T-cell count the concentration of TF were significantly increased. Nadir CD4+ T-cell count reflects a profound immune deficiency in the past, and is also associated with chronic immune activation and persistent microbial translocation . An incrSignificantly higher concentrations of TFPI were revealed in HIV infected people than in the control group. The concentration of TFPI was negatively correlated with the current level of HIV RNA and nadir CD4+ T-cell count. Moreover, it was increased in patients treated with antiretroviral drugs. There was no effect, however, of the cumulative antiretroviral treatment time or the cumulative treatment time with individual classes of drugs on the concentration of TFPI. However, it was dependent on the traditional cardiovascular risk factors: the greater the cardiovascular risk evaluated according to NCEP ATP III criteria, and the higher the concentrations of total cholesterol in plasma, LDL and non-HDL cholesterol, the higher the concentrations of TFPI in our observations.The TFPI is a primary factor inhibiting the extrinsic blood coagulation system and it participates in the regulation of haemostatic mechanisms ,8.Almost 90% of circulating TFPI is associated with plasma lipoproteins ,10,22,23In the present study a correlation between the concentration of TFPI and particular atherogenic cholesterol fractions was reported, but no relationships were shown with the fibrinogen level. A negative correlation was observed between TFPI and the level of HIV RNA.High concentrations of TFPI among individuals with undetectable HIV RNA may, as in the case of TF, be indicative of endothelial injury resulting from antiretroviral treatment. As a matter of fact, we did not notice the cumulative effect of antiretroviral therapy or cumulative treatment using a particular class of drugs on the value of TFPI; however, the TFPI concentration was significantly higher among individuals who were treated (p = 0.0003) than among those who had not undergone antiretroviral therapy.The present study also revealed a weak positive correlation between the concentrations of TF and TFPI in the study group. This may be because of the increased severity of subclinical atherosclerosis in HIV-infected individuals, whereas the increased TFPI concentration in serum is a response to increased expression of TF by injured endothelial cells and activated monocytes. Falciani and colleagues described a positive relationship among people with coronary heart disease in general population . This maThe results of studies in recent years have indicated the involvement of extrinsic coagulation pathways in haemostatic disorders and thromboembolic complications in the course of cardiovascular diseases and atherosclerosis ,26,27. TTF and TFPI significantly influenced the value of cIMT in the cohort. In the multivariate regression model an independent effect of TF, TFPI, non-HDL cholesterol and lifetime smoking on the value of cIMT was demonstrated.The increase in TF expression, the endothelial dysfunction and the secretion of proinflammatory cytokines are associated with increased prothrombotic properties of blood in people living with HIV ,14,18,26The presence of TFPI in atherosclerotic lesions is considered as the main mechanism limiting the thrombogenicity of the atherosclerotic plaque; it also prevents vessel reocclusion. TFPI released from endothelial cells and circulating in the blood plays an important role in inhibiting TF, limiting its procoagulant activity ,25. The This is the first study demonstrating the effect of TF and TFPI on the severity of subclinical atherosclerosis in patients living with HIV and indicating the relationship of their concentrations with antiretroviral treatment.There were some limitations of this study. First, our study and control groups were relatively small and we could not achieve adequate power of our statistical analysis. There was also no possibility for comparison of our results with another contemporary analysis in the Polish general population. Second, due to frequent changes in the treatment, could not be subjected to statistical analysis the impact of individual antiretroviral drugs on the level of TF and TFPI. In addition, tested states of plasma TF  could not detect the TF activity. It is believed that the functionally active form of TF is microparticle TF, which represents only small portion of circulating TF ,6.Nucleoside reverse transcriptase inhibitors had impact on TF plasma level and may contribute to a systemic hypercoagulable state. Findings of our study identify TF and TFPI as potential markers for advanced subclinical atherosclerosis in HIV-infected patients and could have practical applications in primary prevention of cardiovascular diseases in people living with HIV.S1 Fig(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.


Optimal HIV treatment monitoring remains a big challenge in resource limited settings. Guidelines recommend the use of clinical and immunological criteria in resource limited settings due to unavailability of viral load monitoring; however their utility is questionable. This study aimed at assessing the accuracy of immunological criteria in detecting treatment failure among HIV infected Tanzanian adults receiving first line ART.A clinic based cross sectional study was conducted between February and July 2011 at Bugando Medical centre (BMC) HIV care and treatment clinic (CTC) involving HIV infected patients aged 18 years and above, receiving first line ART; followed up for at least 1 year. Viral load was tested for every enrolled patient. Standard WHO criteria were used to define immunological failure. Virological failure was defined as one viral load measurement of >5000 copies/ml and was used as a gold standard. A 2 × 2 table was used to assess the accuracy of immunological criteria in detecting treatment failure.2 (IQR 19.7–24.0). Out of the 274 study participants 156 (56.9%) had immunological failure. Only 60 of the study participants (21.9%) had viral load >5000. Only 42 patients (70%) were found to have both immunological failure and virological failure. The sensitivity of immunological criteria in detecting treatment failure was 70%, specificity 46.7%, positive predictive and negative predictive values of 26.9 and 84.7% respectively.A total of 274 HIV-infected adults were enrolled into the study. Out of these, 65.7% were females, the median age was 39 years (IQR 33–45), the median BMI 21.9 kg/mWHO immunological criteria have low sensitivity and positive predictive value for detecting treatment failure. Relying on CD4 counts for treatment monitoring would therefore lead to misclassifications of treatment failure that could result into unnecessary or delayed switch to second line ART. Access to viral load monitoring is important to avoid these misclassifications. HIV remains a public health concern in sub-Saharan Africa despite the epidemic being on its 3rd decade. Estimates show that up to 70% of HIV cases are still found in sub-Saharan Africa . Efforts3 while on treatment [Guidelines for the use of ART in resource limited settings (RLS) where viral load monitoring is not routinely available recommend the use of CD4 count and clinical monitoring to diagnose treatment failure . WHO guiSeveral studies have found low sensitivity and positive predictive value of the WHO immunological criteria on predicting treatment failure –7. This This was a clinic based cross sectional study done at Bugando Medical Centre (BMC) HIV care and treatment clinic (CTC) between February and July 2011.The study was conducted at Bugando care and treatment centre (CTC) in Mwanza, Tanzania. Bugando is a tertiary and teaching hospital for the Lake Zone of Tanzania. The hospital serves around 13 million people from 6 regions of the Lake Zone, which are Mwanza, Kagera, Shinyanga, Tabora, Mara and Kigoma. The hospital runs both inpatient and outpatient treatment activities, with an approximate bed capacity of 900. CTC activities is one of the core part of outpatient activities, which started in 2004, and currently it serves more than ten thousand patients, of whom about 5000 are on ARTs. More than two-thirds of these patients are on first line regimens and the rest are on the second line regime.The study involved 274 adult HIV-infected patients aged 18 years and above enrolled to HIV care and initiated on first line ART, with a minimum follow up of 1-year. Patients who were critically ill, with concurrent infections, and those on second line were excluded from the study.Sample size was estimated using Kish and Leslie formula. A minimum sample size was 220 patients obtained by assuming that 50% of the patients on first line ART will have virological failure. Patients fulfilling the inclusion criteria were serially enrolled until sample size was reached.The adult HIV patients on first line ART with a minimum follow up of 1 year (12-months) were identified from daily CTC listing at Bugando on routine basis and they were invited to participate in this study. These patients are usually followed up monthly or after every 2-month depending on their clinical status. CD4 measurements are usually routinely performed after every 6-month. After giving consent a structured questionnaire was used to collect information regarding, demographic data, date of diagnosis of HIV, date of ART initiation and regime, compliance level which was assessed using patient response method, BMI, TB status, co morbidities, co medications, CD4, VL, ART serum levels, and other routine laboratory. For each patient enrolled into the study, 1.5 ml of blood was collected in an EDTA bottle for viral load. This sample was also centrifuged in the ICU side lab, at 3000 rpm to obtain plasma which was again transferred into cryovials and sent to BMC main lab for viral load using FACS—calibur analyzers according to manufacturer’s guidelines.The data was entered, verified and a cleaned, using Microsoft excel spread and the data analysis was done using STATA version 14 . Continuous variables were summarized by medians and interquartile ranges (IQRs) and categorical variables were summarized by frequency and percentage. A 2 × 2 table was used to assess the accuracy of the immunological criteria for detecting treatment failure using viral load as the gold standard.3; all in the absence of concurrent infections. Virological failure was defined as one viral load measurement of >5000 copies/ml.Definitions for immunological failure were derived from the WHO guideline for this analysis . A partiThis study was approved by Bugando Medical centre (BMC)/Catholic University of Health and Allied Sciences (CUHAS) joint research and ethical committee. Further approval was sought from the department of Internal medicine, Bugando Medical centre.Informed consent to participate in the study was obtained from all study participants. The study enrolled only patients who provided written consent. All patients with immunological failure were switched into second line of ARV following failure of improvement of their CD4 two weeks of intensified adherence and in some with virological proof of ARV treatment failure as per existing treatment guidelines. Patients who declined consent were not denied of their services.2 (IQR 19.7–24.0) (Table 3 (IQR 60–210). Most of the study participants (97.4%) had adherence level of >95%.A total of 274 HIV-infected adults were enrolled into the study. Out of the study participants, 65.7% were females, the median age was 39 years (IQR 33–45), the median BMI 21.9 kg/mBased on the WHO criteria for immunological failure, out of the 274 study participants 156 (56.9%) met the criteria. Only 60 of the study participants (21.9%) had viral load >5000. Only 42 participants out of the 60 with confirmed virological failure (70%) were found to have both immunological failure and virological failure. The sensitivity of immunological criteria in detecting treatment failure was therefore 70% with specificity and positive predictive values of 46.7 and 26.9% respectively. The negative predictive value was found to be 84.7% , the PPV was low (26.9%). Using the WHO immunological criteria would miss up to 30% of the patients who were truly having treatment failure while it would misclassify as treatment failure 73.1% virologically suppressed patients. In this cohort, 21.9% of the participants had virological failure.These findings are consistent with studies elsewhere in resource limited settings. A study analyzing data from 10 programmes in Africa and south America found that the PPV ranged from 10 to 30% with a relatively higher NPV . AnotherThe low PPV found in this study means that only a few of the patients that were identified as having treatment failure by the immunological criteria were actually having treatment failure. The false positive rate of immunological criteria for this study was 73.1%. This is comparable to a study done in Nigeria which found the false positive rate to be around 60.8%. The low specificities and PPV of immunological criteria found in our study, that is similar to many other studies in Africa and other RLS mean that patients with adequate viral suppression are at risk of being classified as having treatment failure and switched to second line. Monitoring treatment failure using immunological and/or clinical criteria is associated with the emergence of nucleoside reverse transcriptase inhibitor and non-nucleoside reverse transcriptase inhibitor resistance . Based oThe sensitivity in this study was relatively higher compared to other studies. This could be explained by the cut off used in this study (>5000 copies/ml) which has been suggested by WHO to increase sensitivity in resource limited settings . AnotherA systematic review that assessed the 2010 WHO immunological criteria found that these criteria have low sensitivity and positive predictive value in identifying individuals with virological failure . The senOur study had several limitations. We could not consider clinical outcomes linked with the treatment failure. Such information is not routinely collected during clinic visits. It was not possible therefore to assess the link between the clinical criteria; we had to focus therefore on laboratory criteria. The study was a cross-sectional study. Longitudinal study design would have been a better design for such a study question. These findings were from a single clinic; the results may not be generalizable to the general population therefore. Our study however is the first study in Tanzania comparing immunological criteria that is the one most used and the gold standard virological criteria in diagnosing treatment failure. It also adds more emphasis on the disadvantages of switching clients to second line based on clinical and immunological criteria that have low sensitivity and positive predictive value; this outlines the need for confirmatory viral load test.CD4 criteria for the detection of treatment failure have poor performance for detecting virological failure based on WHO-definition. The low specificity and positive predictive values indicate that using CD4 criteria increases the risk of misclassifications; individuals with adequate virological suppression may be misdiagnosed with treatment failure leading to unnecessary switches while those with virological failure may be missed leading to delay in switching. This further highlights the need to confirm virological failure before switching to second line. Targeted viral load strategy should be used in resource limited settings to confirm treatment failure in cases suspected by clinical and immunological criteria.


Selecting the optimal combination of HIV drugs for an individual in resource-limited settings is challenging because of the limited availability of drugs and genotyping.The evaluation as a potential treatment support tool of computational models that predict response to therapy without a genotype, using cases from the Phidisa cohort in South Africa.Cases from Phidisa of treatment change following failure were identified that had the following data available: baseline CD4 count and viral load, details of failing and previous antiretroviral drugs, drugs in new regimen and time to follow-up. The HIV Resistance Response Database Initiative’s (RDI’s) models used these data to predict the probability of a viral load < 50 copies/mL at follow-up. The models were also used to identify effective alternative combinations of three locally available drugs.The models achieved accuracy (area under the receiver–operator characteristic curve) of 0.72 when predicting response to therapy, which is less accurate than for an independent global test set (0.80) but at least comparable to that of genotyping with rules-based interpretation. The models were able to identify alternative locally available three-drug regimens that were predicted to be effective in 69% of all cases and 62% of those whose new treatment failed in the clinic.The predictive accuracy of the models for these South African patients together with the results of previous studies suggest that the RDI’s models have the potential to optimise treatment selection and reduce virological failure in different patient populations, without the use of a genotype. A case with all the necessary data  is termed a treatment-change episode (TCE). The models are trained using TCEs from the RDI database, containing data from 160 000 patients from more than 40 clinics, cohorts and clinical trials in more than 20 countries around the world in order to make the models’ predictions as generalisable as possible to patients from different settings. The models consistently achieve accuracy (measured as the area under the receiver operating characteristic curve [AUROC]) in the region of 75%–80% in their predictions of virological response to therapy.9 The RDI models are made freely available as a treatment-support tool, the HIV Treatment Response Prediction System (HIV-TRePS), via http://www.hivrdi.org.The application of the models as a treatment decision-making aid has been assessed in prospective clinical pilot studies involving highly experienced HIV physicians in well-resourced settings and found to be a useful clinical tool.10In the EuResist versus Expert (EVE) study, the EuResist group has also reported the successful development of predictive models that performed as well as the predictions made by HIV physicians and virologists, albeit without the benefit of full treatment history, in a retrospective study.7 Nevertheless, this is comparable to the accuracy of using genotyping with rules-based interpretation to predict response to therapy. With the accelerated roll-out of HIV therapy in low middle-income countries, the RDI has now been able to collect significant data from such settings, predominantly sub-Saharan Africa, and has developed models including these data that have proved comparably accurate for test cases from high and low middle–income countries.11Historically the RDI models have been trained with data almost exclusively collected from well-resourced settings as this is where antiretroviral therapy was first available. While these models were highly accurate for cases from similar settings they were less so for cases from low-middle income countries not represented in the training data set, typically achieving AUROC values of 60%–70%.12 ‘Phidisa’ means ‘to heal’ in Setswana.Here we report on the evaluation of the current RF models used to power the online HIV-TRePS, using data from the Phidisa cohort in South Africa. Project Phidisa is a clinical research project focused on the management and treatment of HIV infection in the uniformed members of the South African National Defence Force (SANDF) and their dependents with HIV infection treated between 2004 and 2012.6 In the development of the models, the term ‘baseline’ relates to data collected while on the failing regimen. In summary, the following data from 22 567 TCEs including 1090 from Southern Africa were used: on-treatment baseline plasma viral load (sample taken ≤ 8 weeks prior to treatment change); on treatment baseline CD4 cell count (≤ 12 weeks prior to treatment change); baseline regimen (the drugs the patient was taking prior to the change); antiretroviral treatment history; drugs in the new regimen; a follow-up plasma viral load determination taken between 4 and 52 weeks following introduction of the new regimen and the time to that follow-up viral load. These data were coded as 42 input variables for input into the models: baseline viral load (log10 copies HIV RNA/mL); baseline CD4 count (cells/mm3); treatment history comprising 20 binary variables coding for any experience of zidovudine, didanosine, stavudine, abacavir, lamivudine, emtricitabine, tenofovir DF, efavirenz, nevirapine, etravirine, indinavir, nelfinavir, saquinavir, amprenavir, fos-amprenavir, lopinavir, atazanavir, darunavir, enfuvirtide, raltegravir; antiretroviral drugs in the new regimen, comprising 19 variables covering zidovudine, didanosine, stavudine, abacavir, lamivudine, emtricitabine, tenofovir DF, efavirenz, nevirapine, etravirine, indinavir, nelfinavir, saquinavir, (fos)amprenavir, lopinavir, atazanavir, darunavir, enfuvirtide, raltegravir; and time from treatment change to the follow-up viral load (number of days).11The RF models used to power the HIV-TRePS system (V5.3.2.0) for patients without a genotype were trained to predict the probability of virological response  to a new therapy introduced following virological failure (≥ 50 copies HIV-RNA/mL), using methods described in detail elsewhere.11The models’ performance was assessed by 10× cross-validation during model development and then with an independent global test set of 1000 cases including 100 from southern Africa, which were partitioned from the overall pool of available, complete TCEs. To prevent overfitting, we stopped the training process when the validation errors had their global minima. The accuracy of the models as predictors of virological response was evaluated using the models’ estimates of the probability of response following initiation of the new drug regimen and the actual responses observed in the clinic (binary response variable: response = 1 vs failure = 0) to plot ROC curves and assessing the AUROC. The optimum operating point (OOP) for the models that was derived during cross-validation was used as the cut-off for classifying predictions as ‘response’ or ‘failure’ and used to obtain the overall accuracy, sensitivity and specificity of the system. The models’ performance was compared with genotypic sensitivity scores derived from genotyping with rules-based interpretation systems , for those cases with genotypes available. The models achieved AUCs of 0.79–0.84 (mean of 0.82) during cross-validation, 0.80 with the global test set and 0.78 with the southern African subset. The AUCs were significantly lower (0.56–0.57) for genotyping with rules-based interpretation.TCEs were extracted from the full Phidisa data set that had all the data required by the models, as described above. The performance of the models as predictors of virological response for these cases was evaluated by comparing the average of the 10 RF models’ estimates of the probability of response following initiation of the new drug regimen to the actual responses observed in the Phidisa patients using the method described above.In order to assess the potential of the models to help avoid treatment failure in a resource-limited setting, where models that do not require a genotype may be of most value, they were used to identify antiretroviral regimens that were predicted to be effective for the Phidisa cases. Of particular interest were those cases where the new regimen selected in the clinic failed to re-suppress the virus. Baseline data were used by the models to make predictions of response for alternative three-drug regimens in common use, comprising only those drugs that were in use in the Phidisa cohort at the time. Again, the OOP (the cut-off above which the models’ estimate of the probability of a response is classified as a prediction of response) that was derived during model development was used, as a test of how generalisable the system is.10 copies/mL) than the global data used to train and test the models. The original test data set of 1000 TCEs had a median baseline viral load of 3.97 and the 100 southern African TCEs amongst them had a median viral load of 4.32. The median baseline CD4 count of the Phidisa cases was also somewhat lower at 230 cells/mL than the global data at 260 cells/mL but was substantially higher than the original 100 southern African TCEs at 163 cells/mL.The baseline, treatment and response characteristics of the data sets are summarised in In other respects, as might be expected, the 402 Phidisa cases resembled the southern African subset of the original test set somewhat more than the global test set as a whole (and the training data from which they were partitioned). For example, 79% of the Phidisa patients were moving from their first-line to second-line therapy, as were 62% of the original South African test set, versus 38% of the global test set. The Phidisa and original Southern African cases had less previous drug exposure overall (median of 3 vs 4 drugs) and greater previous exposure to NNRTIs (90% and 94% vs 63%) and less to PIs (16% and 11% vs 63%), reflecting the fact that the great majority of the African cases were moving from first-line therapy of 2 N(t)RTI+NNRTI to second-line therapy, comprising 2N(t)RTI+PI in half of the Phidisa cases and 70% of the original southern African cases.There was a similar proportion of virological failures amongst Phidisa cases (70%) and original training and test sets (66% and 64%) but fewer in the original southern African set (48%).p < 0.01), but the comparison with the 100 southern African TCEs within it was not (p = 0.26).When tested using the Phidisa cases, the committee of 10 RF models achieved an AUC of 0.72, compared with 0.80 when tested with the original global test set and 0.78 with the 100 southern African TCEs within that test set . The oven = 349) and later switches (n = 59), the results were not significantly different. The AUC values were 0.73 and 0.68 with overall accuracy of 64% and 68%, respectively.When the models were tested separately with the cases of switching from first-line to second-line , for 69% of all the Phidisa cases . In thesThere were 281 Phidisa patients (70%) that went on to fail the new regimen introduced in the clinic. For these, the models were able to identify one or more locally available three-drug regimens that were predicted to be effective in 62% of cases. The median number of these alternative regimens identified was 10. The models identified alternatives with a higher estimated likelihood of response than the regimen actually used in the clinic in all of the failures with a median of eight alternatives.11 The AUC value of 0.72 for the models compares favourably with AUC values typically in the range of 0.55–0.65 for genotyping. Genotyping is not used to select the next regimen in South Africa but mainly to differentiate patients with drug resistance from those with poor adherence and detect patients with second-line failure who require a third-line, selected on the basis of clinical information and according to strict protocols as part of a public health strategy. Nevertheless, genotyping with rules-based interpretation is widely considered as the gold standard for predicting drug sensitivity and response and is in widespread use for individualised treatment selection around the world. As such it provides a highly relevant benchmark against which to compare this and other approaches for predicting response to HIV therapy.The RDI’s computational models that do not require a genotype predicted virological response to a change in antiretroviral therapy following virological failure for patients from the Phidisa cohort with a level of accuracy that was at least comparable to that of genotyping with rules-based interpretation as a predictor of virological response, as observed in several previous studies.It is also encouraging that the models were able to identify several alternative, available three-drug regimens that were predicted to produce a virological response for two-thirds of the cases from the Phidisa cohort, including the virological failures. Furthermore, the models were able to identify regimens with a higher predicted probability of success than the regimen that failed in the clinic, for all cases.13The online treatment support tool, HIV-TRePS, through which the models are made available has the facility for users to include the annual cost of drugs in their setting and instruct the system to include the annual costs of different regimens in the report, alongside the predictions of response produced by the models. This raises the possibility that physicians can use the system to identify alternative regimens that are not only predicted to be more likely to produce a response but may be less costly than the regimen they would otherwise use. Indeed, a recent study of cases treated in India revealed that substantial cost savings may be possible through use of the system.The models’ accuracy of prediction for the Phidisa cases was somewhat less than that observed during model development and previous independent testing with cases from a range of settings, including a subset from southern Africa, although the latter difference did not achieve statistical significance. South Africa has a uniform programme with strict and rational criteria for regimen switches. In this context, the moderate performance of the TRePS algorithm is not unexpected.When tested with the original global test set, using the OOP derived during cross-validation, the models showed higher specificity at 79% than sensitivity (66%). This is the pattern found in previous modelling studies and means the models are generally ‘conservative’, making relatively fewer incorrect predictions of response than incorrect predictions of failure. It is interesting to note that the reverse was true when the models were tested with the subset of the original test set that came from southern Africa (specificity of 60% and sensitivity of 81%). This suggests that an upward adjustment of the OOP above which a prediction would be classified as a response might be desirable to rebalance the classification and optimise performance for patients from this region, possibly related to treatment starting later in the course of the disease than for the global data. For the Phidisa cases, the specificity was again reduced at 62%, meaning that the models incorrectly predicted response for 38% of the observed failures. However, unlike the original southern African test cases, there was no apparent compensatory increase in sensitivity, which was 67%.The fact that the Phidisa patients had somewhat lower baseline viral loads than the original global data used to train and test the models, as well as the southern African cases from the original test set, and substantially higher CD4 counts than the latter is consistent with Phidisa being a closely monitored cohort and much of the training data being collected from open clinical practice. Nevertheless, the Phidisa and the original southern African patients were mainly moving from first- to second-line therapy, so the lower sensitivity of the models (60%) and lower observed response rate (30%) for the Phidisa patients compared with the original southern African patients  remains unexplained.14 Although viral load monitoring is not yet routine in most resource-limited settings, it is now recommended as the preferred approach to monitoring antiretroviral therapy success and diagnosing treatment failure in the latest WHO guidelines.15 As technological advances enable lower test costs and simpler equipment requiring less infrastructure, maintenance and technical expertise, so the use of viral load in clinical practice is likely to increase.16 It should also be noted that another input variable is the CD4 count. This is more affordable and in more widespread use in resource-limited settings but not universal, and its use may diminish as viral load testing expands.It is important to note that one of the input variables for these models was the plasma viral load, which previous studies have shown to be very important to the predictive accuracy of the models.The definition of virological failure used in the study was a single viral load value of > 50 copies HIV RNA/mL, compared with repeated measure of 400 copies/mL or 1000 copies/mL in clinical practice in South Africa. This threshold was used because the majority of the data used to train the models and the majority of the settings in which the models are used use a definition of 50 copies/mL. A single measure was used in order to maximise the number of TCEs available for training the models. As the size of the RDI data set increases, we could consider the use of multiple viral load measurements and the exclusion of those cases with only one.The study has some limitations. Firstly, it was retrospective and, as such, no firm claims can be made for the clinical benefit that the use of the system as a treatment support tool could provide. Another limitation is that the Phidisa cases came from a carefully monitored, military cohort and the cases used in the analysis are, by definition, those with complete data around a change of therapy. Such data may not be truly representative of the general patient population. Nevertheless, the performance of the models in predicting outcomes for this independent South African cohort is encouraging in terms of the applicability of the approach.In switching from first- to second-line, following treatment protocols, the system can provide predictions of which NRTI backbone and choice of third agent, comprising locally available drugs, offer the highest probability of response.In switching from second to third-line or beyond, the system can help the healthcare professional assemble an individualised regimen with the highest probability of response.In doing so, the system can utilise genotypic information where available, or produce predictions of response that are comparable, or are most likely superior to those of genotyping with rules-based interpretation for cases where genotyping is not available or affordable.The system can help to reduce treatment costs. By entering their local drug costs into the system, healthcare professionals can identify the most effective regimens within a budget limit, or select the least costly of a number of regimens with similar estimates of effectiveness.By putting the distilled treatment experience of hundreds of physicians treating tens of thousands of patients around the world at their fingertips, the system can give relatively inexperienced healthcare professionals the confidence to make treatment decisions in settings or cases not covered by current treatment guidelines, for example, in salvage therapy with limited drug options available.The RDI models and more accurately the HIV-TRePS system that they power have wide-ranging potential utility in South Africa and other resource-limited settings, for example:In this study, we challenged the RDI models that do not require a genotype to predict virological response for patients in the Phidisa military cohort in South Africa, most of whom were moving from first- to second-line therapy. The models performed less well than with a more diverse global test set but still achieved a level of accuracy that was at least comparable to that observed in previous studies using genotyping with rules-based interpretation as a predictor of response.It is encouraging that the models were able to identify alternative, available regimens that were predicted to be effective for the majority of the Phidisa cases, including those that failed the new regimen prescribed in the clinic.These results and those of previous published studies suggest this approach has the potential to optimise treatment selection, reduce virological failure, improve patient outcomes and potentially reduce drug costs in South Africa and other resource-limited settings where resistance testing is unavailable or unaffordable.Note: The methodology, development and cross-validation of the random forest models studied in this paper were described in previous publications by the RDI and its collaborators . Consequently, there are some overlaps between parts of the Methods section of the papers. The novel aspect of the current paper is the evaluation of the models as a potential clinical tool with a substantial cohort of patients from South Africa.


Efavirenz-based antiretroviral therapy (ART) regimens are preferred for treatment of adult HIV-positive patients co-infected with tuberculosis (HIV/TB). Few studies have compared outcomes among HIV/TB patients treated with efavirenz or non-efavirenz containing regimens.HIV-positive patients aged ≥16 years with a diagnosis of tuberculosis recruited to the TB:HIV study between Jan 1, 2011, and Dec 31, 2013 in 19 countries in Eastern Europe (EE), Western Europe (WE), and Latin America (LA) who received ART concomitantly with TB treatment were included. Patients either received efavirenz-containing ART starting between 15 days prior to, during, or within 90 days after starting tuberculosis treatment, (efavirenz group), or other ART regimens (non-efavirenz group). Patients who started ART more than 90 days after initiation of TB treatment, or who experienced ART interruption of more than 15 days during TB treatment were excluded. We describe rates and factors associated with death, virological suppression, and loss to follow up at 12 months using univariate, multivariate Cox, and marginal structural models to compare the two groups of patients.Of 965 patients  50% were from EE, 28% from WE, and 22% from LA. Among those not receiving efavirenz-containing ART, regimens mainly contained a ritonavir-boosted protease inhibitor (57%), or raltegravir (22%). At 12 months 1.4% of patients in WE had died, compared to 20% in EE: rates of virological suppression ranged from 21% in EE to 61% in WE. After adjusting for potential confounders, rates of death , virological suppression , and loss to follow up , were similar in patients treated with efavirenz and non-efavirenz containing ART regimens.In this large, prospective cohort, the response to ART varied significantly across geographical regions, whereas the ART regimen (efavirenz or non-efavirenz containing) did not impact on the proportion of patients who were virologically-suppressed, lost to follow up or dead at 12 months.The online version of this article (10.1186/s12879-018-3077-x) contains supplementary material, which is available to authorized users. Mortalil trials –12. Efavl trials –12. The l trials –15. Hencl trials –22.On the other hand, late presentation of HIV-positive adult patients into care is common in both resource-constrained and high-income settings –25 and eMycobacterium tuberculosis on culture or PCR), probable TB (Acid-Fast Bacilli on ‘smear’ or granulomatous inflammation on biopsy specimens) and presumptive TB  were included in the study. Details of the TB:HIV study have been published elsewhere [http://www.cphiv.dk). Due to the observational nature of the study, all decisions regarding use of tuberculosis and ART treatment regimens was at the discretion of individual clinicians.The TB:HIV study is a collaboration between TB and HIV clinicians from 19 countries in Europe and Latin America. Patients aged 16 years or older were included if they were HIV-positive and were diagnosed with TB between January 1, 2011, and December 31, 2013. Patients with confirmed TB  were included. Patients, who interrupted ART for more than 15 days while on anti-TB drugs, were excluded. In the non-efavirenz group, patients receiving ART but not containing efavirenz within the above time frame were included. Patients in this group included those in receipt of ritonavir-boosted protease inhibitors, triple nucleoside reverse transcriptase inhibitors, integrase inhibitors or nevirapine. Baseline was considered the date anti-TB treatment was commenced. Demographic and clinical characteristics of patients at baseline were described by receipt of ART in each region and for all study participants. Patients were recorded as dead, in care with virological suppression, in care without virological suppression, in care without viral load information, or lost to follow up at 12 months after their TB diagnosis. A viral load < 400 copies/mL between 8 and 12 months after baseline was considered as virological suppression. Patients with a last visit date reported before 12 months of follow-up were considered lost to follow up. All patients were included in analysis of time to outcome, and were censored 12 months after the TB diagnosis.The main outcome was mortality during the first year following TB diagnosis comparing efavirenz and non-efavirenz containing ART regimens, using three different statistical methods to account for potential confounders and bias: inverse probability weighting, Cox models and marginal structural models. For the first method, Kaplan-Meier survival curves were estimated using inverse weighting. Weights were calculated from a logistic model to predict efavirenz use, adjusting by traditional confounders: region, age, gender, CD4 count at TB diagnosis, type of TB, route of HIV transmission, naïve status, rifamycin use (rifampicin or rifabutin), and multidrug-resistant TB (MDR-TB). In the second method, a Cox model was used to estimate risk of death using the same co-variables, stratifying by naïve status. Continuous variables such as age and CD4 cell count were included in the models using splines with three knots to fit a non-linear and less biased relationship with the outcome . FinallyFollowing standard use of marginal structural models, patients who did not receive ART at initiation of TB treatment were included in this analysis since, as they were on tuberculosis treatment, they had the potential to be started by clinicians on ART with any regimen, as in local clinical practice . The datOf 1389 patients in the study, 965 (69%) received ART during the predefined time-frame and were included in the main analysis: 647 were in the efavirenz group and 318 in the non-efavirenz group. We excluded 177 patients because they started ART after the time-frame (i.e. > 3 months after initiation of TB treatment), and 18 because of ART interruptions (> 15 days) during TB treatment. Baseline characteristics of participants stratified by ART regimen and ART status are shown in Table p < 0.01), and 32%, 15% and 25% for those taking a non-efavirenz regimen, respectively (p < 0.01)  for efavirenz compared with non-efavirenz ART regimens (Table p < 0.001) for patients with CD4 of 350 cells/mm3 compared to those with CD4 50 cells/mm3, and naïve patients had a lower risk of death  (Table By 12 months 113(12%) deaths had occurred: 84(13%) in the efavirenz group and 29(9%) in the non-efavirenz group. Using an inverse probability weighting method, no difference in survival  was observed between efavirenz and non-efavirenz treated individuals . These results, obtained using this model were consistent with the other statistical techniques used in this study. Furthermore exploration, in an analysis restricted to patients starting efavirenz compared with those starting a ritonavir-boosted protease inhibitor regimen only, showed that the odds of death for those who started an efavirenz-containing ART regimen was statistically better .Of the 1389 patients in the prospective HIV:TB study 229(16.5%) never started ART and were therefore, only included in the marginal structural model. Of these 229 patients 92% were from EE, 4% from WE and 3% LA. Their median CD4 cell count was 149 cells/uL. 60% were IDU, 38% had MDR-TB and 65% had disseminated TB . Truncating at other percentiles showed similar results. Additional file This analysis included 518 ART-naïve patients: 420 (81%) receiving efavirenz-based ART and 98 (19%) non-efavirenz containing ART. Adjusted survival curves are shown in Additional file p = 0.77) for efavirenz, compared with non-efavirenz containing ART regimens. Gender, age, route of HIV transmission, disseminated TB, rifamycin use, being ART-naïve, and having documented MDR-TB were not significantly associated with virological suppression  patients had undetectable plasma HIV viral loads (less than 400 copies/ml). The proportion of patients with virological suppression was less frequent in EE, regardless of the ART regimen, compared to the other regions. Distribution by ART regimen and treatment experience is shown in Table p = 0.40). However, a higher risk of loss to follow-up was found in patients from EE, compared to patients from other regions  were lost to follow up at 12 months  infections, diagnostic delay, and access to anti-TB drug susceptibility tests were included. In contrast with previously described findings in this cohort, MDR-TB was not associated with mortality at 12 months, probably because those with MDR-TB were less likely to start antiretroviral therapy and were therefore not included in the current study: moreover, a resistance test was performed in only 54% of the selected population. In the main model used, no differences in mortality between regimes was observed, but in an explorative marginal structural model restricted just to patients treated with a boosted protease inhibitor or efavirenz, survival was statistically different, favoring the group receiving efavirenz-based ART. This finding may reflect poorer tolerability of protease inhibitors, greater frequency of drug-drug interactions, or previous virological failure that could additionally impact on mortality. Surprisingly, there was a high proportion of subjects receiving PI-based ART as their first regimen (57%). This group of patients were mostly in the Eastern European region (61%), and most likely the reason for initiating ART with a PI-containing regimen was because of the local availability. Moreover, only 22% of those on non-efavirenz regimens received integrase inhibitors, a majority from Western Europe. Any inferences based on outcomes with this class of ART should be interpreted with caution as the study was not designed to directly compare integrase inhibitors with efavirenz, and additionally, the main analysis may have lacked power to detect a survival benefit.The proportion of patients who were lost to follow up was high, especially among patients from Eastern Europe, irrespective of ART regimen. This is consistent with findings from other studies in countries with a high burden of TB . Higher Our study has several limitations. These include the relatively short follow-up period, differences between regions in terms of routine care such as access to HIV-RNA determinations, heterogeneity of use of ART, availability of rifabutin and integrase inhibitors, and timing of ART initiation at TB diagnosis. In terms of data availability, we recognize limitations in information regarding reasons for initiating or changing specific ART regimens, which did not allow us to accurately distinguish between changes due to adverse effects or changes due to ART failure and we cannot exclude the possibility that some naïve patients might have been receiving ART – although this was not detected during our quality assurance procedures. However, this is the largest multi-regional TB:HIV cohort and the first to comprehensively compare the concomitant use of TB treatment and efavirenz and non-efavirenz containing ART regimes. Using marginal structural models, there was a slightly better outcome for patients treated with efavirenz-containing ART, although this was not consistent in all analyses and therefore not categorical. These results warrant further analyses.In this multi-centre, multi-region cohort study, overall, poor outcomes were more frequent in Eastern Europe. Use of efavirenz or non-efavirenz containing ART regimens did not impact on mortality, virological suppression, or loss to follow-up at 12 months following diagnosis of TB. Considering these differences between regions irrespective of ART regimen, more, prospective studies are needed, particularly in resource-constrained regions in which access to rifabutin and integrase inhibitors is still limited.Additional file 1:Figure S1. Probability of death in efavirenz and non-efavirenz containing ART groups by inverse probability weighting method. Figure S2. Cox adjusted survival by ART regimen during the first year stratified by naïve status. Figure S3. Adjusted probability of death in the efavirenz group compared to the non-efavirenz group for naïve patients only. Table S1. Risk factors for death in naïve patients. Figure S4. Adjusted probability of death in the efavirenz group compared to the non-efavirenz group for non-naïve patients only. Table S2. Demographic and clinical characteristics of patients who never started ART at the time of starting TB treatment. Table S3. Risk factors for death in non-naïve patients. (DOCX 1262 kb)Supplementary material response to ART in HIV-TB.


The PIVOT trial examined whether patients with suppressed viral load on combination antiretroviral therapy could be safely switched long-term to ritonavir-boosted protease inhibitor (PI) monotherapy. The main trial publication reported that only one of 296 patients allocated to PI monotherapy experienced a loss of drug options due to protease mutations  likely selected by study drug.To assess if we had missed low frequency mutations, using a more sensitive methodology.We performed next generation sequencing (NGS) on all available frozen plasma samples with VL >1000 copies/ml from patients who were randomised to PI monotherapy. Assays were performed at Public Health England laboratories using a previously described method.Median coverage depth was 76,000 and the threshold for detection of minority variants was 2%. Drug susceptibility was predicted using the Stanford HIVdb algorithm.17 of 26 potential samples, all from different patients, were identified and successfully tested. The median viral load was 6780 copies/ml and the median time since randomisation was 43 weeks. NGS revealed previously unidentified minority variant protease mutations  in three samples, at frequencies ranging between 2% and 10%. None of these mutations predicted intermediate or high level resistance, the trial primary outcome.This report adds to the body of evidence that ritonavir-boosted PI monotherapy, when used as a switch strategy with prompt detection of viral load rebound and early re-introduction of combination therapy, rarely leads to the development of clinically important protease resistance mutations. The priOf the 296 patients who were allocated to PI monotherapy, 105 were genotyped at least once , and the primary outcome observed in six patients. Three of these six patients had reverse transcriptase mutations but no protease mutations. In two other patients, both of whom received darunavir monotherapy and had no prior PI exposure, L90M was the sole protease mutation detected; this is rarely selected de novo by darunavir and is a relatively common transmitted mutation . It appe1.1To examine whether the main analysis of PIVOT had missed low frequency mutations, by applying a more sensitive sequencing methodology to selected stored samples.1.2We performed next generation sequencing (NGS) on all available frozen plasma samples with VL >1000 copies/ml  that had been stored from patients who were randomised to PI monotherapy. NGS detects the same range of mutants than Sanger sequencing but with much higher sensitivity. Assays were performed at Public Health England, with sequencing performed using MiSeq Reagent Kit version 2 (Illumina) . The thr2https://hivdb.stanford.edu/).Of 26 potential samples, 17  were identified and successfully tested. One patient received atazavair, two patients lopinavir, and 14 patients darunavir, as ritonavir-boosted monotherapy. The median (IQR) HIV VL was 6780  copies/ml, the median (IQR) time since randomisation was 43 (33–53) weeks, and the median (IQR) interval between first detectable VL and the date of sample was 6 (4–8) weeks. 10 of the samples had previously been tested by Sanger sequencing from the same blood draw; a paired comparison showed 99.2% homology at the nucleotide level, using a NGS variant detection level of 20%. The 7 remaining samples were from patients who had been tested by Sanger sequencing at a different time point. Protease mutations were defined as major or accessory, and drug susceptibility predicted using the Stanford HIVdb algorithm (version 8.1.1)  revealed minority variant mutations in three of the 17 patients tested, all of whom were infected with subtype C virus . PatientIn summary, NGS did not identify any additional patients who met the trial primary outcome of intermediate or high level resistance. Furthermore, the phenotypic impact of low-level variants may be over-estimated by the Stanford algorithm, which was developed in the context of majority variants.3This report adds to the body of evidence that ritonavir-boosted PI monotherapy rarely leads to the emergence of protease resistance mutations when used as a switch strategy with prompt detection of VL rebound and early re-introduction of effective combination therapy. The risk may be higher in a routine clinical study setting, where monitoring may be less intensive. However, studies of second-line PI monotherapy in settings without VL monitoring have found that protease resistances emerges slowly  Another In spite of the substantial evidence of safety, PI monotherapy has failed to achieve wide clinical acceptance as a maintenance strategy in patients with established viral suppression, possibly due to a higher overall risk of virological rebound compared with combination therapy. However, recent analyses have shown that patients at lower risk of virological rebound can be readily identified  and thatNational Institute for Health Research Health Technology Assessment programme (project number 06/403/90) and the Medical Research Council (grant MC_UU_12023/23). The views and opinions expressed herein are those of the authors and do not necessarily reflect those of the HTA programme, NIHR, NHS or the Department of Health.This work was supported by the NP, WS, DTD, and AA-P report grants from the National Institute of Health Research Health Technology Assessment programme during the conduct of this study. NP reports grants, personal fees, and non-financial support from AbbVie and Janssen; personal fees and non-financial support from Merck and Roche; grants and non-financial support from GlaxoSmithKline; and non-financial support from Gilead Sciences, all outside the submitted work. AA-P reports grants from Janssen and ViiV Healthcare outside the submitted work.The trial protocol was approved by the Cambridgeshire 4 Research Ethics Committee and Medicines and Healthcare Products Regulatory Agency.