In Brazil, HAART currently reaches over half of HIV-infected subjects. In the context of a remarkable HIV-1 genetic variability, highly related variants, called quasispecies, are generated. HIV quasispecies generated during infection can influence virus persistence and pathogenicity, representing a challenge to treatment. However, the clinical relevance of minority quasispecies is still uncertain. In this study, we have determined the archived proviral sequences, viral subtype and drug resistance mutations from a cohort of HIV+ patients with undetectable viral load undergoing HAART as first-line therapy using next-generation sequencing for near full-length virus genome (NFLG) assembly. HIV-1 consensus sequences representing NFLG were obtained for eleven patients, while for another twelve varying genome coverage rates were obtained. Phylogenetic analysis showed the predominance of subtype B . Considering the minority variants, 18 patients carried archived virus harboring at least one mutation conferring antiretroviral resistance; for six patients, the mutations correlated with the current ARVs used. These data highlight the importance of monitoring HIV minority drug resistant variants and their clinical impact, to guide future regimen switches and improve HIV treatment success.Increased access to highly active antiretroviral therapy (HAART) by human immunodeficiency virus postive (HIV New recommendations that stimulate the initiation of HAART for all HIV-positive individuals, independent of CD4+ T lymphocyte counts, were implemented in October 2013 in order to reduce the transmission of the virus [According to the Joint United Nations Programme on HIV/AIDS, approximately 36.7 million people were living with the human immunodeficiency virus (HIV) worldwide at the end of 2016, making HIV infection a major public health problem [A remarkable HIV-1 genetic variability results from mutational events associated with the high error-prone rate of the viral reverse transcriptase (RT), high virus replication rates and homologous recombination events. HIV-1 diversity imposes an important clinical challenge, as it allows the virus to adapt to and evade immune responses and antiretroviral therapy , therefoThe HIV evolutionary dynamics associated with ART pressure allows the appearance of drug-resistant variants ,10. AntiAiming to establish more efficient HAART regimens, genotypic assays to detect drug resistance-associated mutations have been widely used in the clinical setting . This prHIV minor drug resistant variants may persist in the infected individual ,29. DespIn the present study, we have assessed the presence of HIV minority drug-resistant variants archived in PBMC samples in a Brazilian cohort of chronic HIV patients undergoing HAART as first-line therapy and with undetectable viral load. Upon analyzing the HIV antiretroviral resistance profiles of the patients by NGS, our study was able to evidence a high prevalence of drug resistance mutations in this cohort, despite the therapeutic success achieved by their carriers. This is the first study investigating the presence of HIV minority drug resistance mutations among Brazilian patients under virologic control.+ T-cell counts, CD8+ T-cell counts, treatment history) data. Written informed consent was obtained from all participants and data were processed using unique identifiers to ensure confidentiality.A cross-sectional study was carried out among patients attending a sexually transmitted diseases/HIV ambulatory at Hospital Federal de Ipanema, Rio de Janeiro, Brazil, between February and July 2016. These patients were recruited during the clinical follow-up routine and had a 10 mL sample of whole peripheral blood collected. A questionnaire was applied to collect epidemiological  and clinical  [Inclusion criteria included age ≥ 18 years, being under first-line HAART and having undetectable HIV viral load for at least 12 months prior to collection date. Patients who had a history of previous virological failure, that underwent a change in the therapeutic regimen due to intolerance or poor adherence, that were classified into clinical and/or immunological AIDS   or that Plasma and buffy coats were separated by centrifugation of the whole blood, and the latter were used for genomic DNA extraction with the Genomic DNA Extraction Kit  following manufacturer’s specifications.® 96 Well Thermal Cycler  using Platinum™ Taq DNA Polymerase High Fidelity (Life Technologies) in a final volume of 25 µL. An overview of all PCR fragment coverage regions and list of primers used are provided in Nested PCR was carried out for the amplification of near full-length HIV genomes using a set of five overlapping fragments of approximately 2 kb each, or alternatively four fragments, with ~3 kb each, as previously described ,54. EachPCR products were visualized in 1% agarose gels. Duplicates were made for each PCR and pooled before proceeding to the purification step with the GFXTM PCR DNA and Gel Band Purification Kit . The purified fragments were quantified in a NanoDrop ND 1000 apparatus  and diluted to 4 ng/µL before being pooled per sample. The final product pool per sample was diluted to 0.4 ng/µL for library construction.Libraries were prepared using the Nextera XT DNA Sample Preparation kit  according to the manufacturer’s protocol. Briefly, after the fragmentation step using transposon technology, libraries were subjected to a PCR where they were tagmentated with Illumina sequencing adaptors and molecular tags (indexes) to identify each sample. A purification step was performed to select fragments of ~800 bp and libraries were quantified by qPCR with the KAPA library quantification kit . Individual libraries were then diluted to 4 nM, considering the mean size of each library and the quantification performed, and pooled. The final product was diluted to 12 pM and sequenced in a MiSeq Illumina platform (2 × 301 paired-end run) (Illumina) with 1% denatured PhiX DNA as a sequencing control.https://dholk.primate.wisc.edu/wiki/dho/public/page.view?name=default). The alignment parameters used in the assembly were as previously described [The analysis of the obtained files was performed in Geneious v.9.1.3 as described by Dudley et al. . Brieflyescribed . For thihttp://hivdb.stanford.edu/hiv/). Transmitted drug resistance mutations (TDRM) were also included and defined according to the classification of the World Health Organization established by Bennett et al. [The annotated drug resistance mutations were based on the consensus of the International Antiviral Society  and the t et al.  and in tt et al. ,62,63,64The variant finder of Geneious v.9.1.3 was used to call nucleotide variants from the reference sequence at frequency higher than 1%. This tool evaluates nucleotide substitutions found in the alignment along with their frequency and whether they are synonymous or non-synonymous. The frequency and the number of reads representing each ARV-resistant variant was determined in relation to the annotated reference and exported to an Excel file. Variants between 20% and 1% of frequency were considered as minor variants, since 20% is the minimum frequency associated with detection of mutations by commercial genotype resistance assays that use Sanger sequencing .bootscanning tool of Simplot v.3.5.1 [bootscanning breakpoint analysis.A consensus sequence was derived for each sample from the reference assembly with Geneious using the 50% stringency setting. HIV-1 subtype classification of each query sequence was inferred through phylogenetic analysis performed with the maximum likelihood (ML) method using PhyML v.3.0  and the  v.3.5.1  for deteenv gene were selected and unique haplotypes were identified and their relative frequency considered. Amino acid sequences of the haplotypes were analyzed only if started and ended with cysteine residues and contained 32–38 amino acid residues, a pattern consistent with functional sequences [http://coreceptor.geno2pheno.org) [For the phenotypic prediction of HIV co-receptor usage, the reads spanning the complete region of V3 loop of the equences . Valid heno.org)  using a eno.org) ,72,73,74+ T-cell count was 712.5 cells/mm3. All patients have reported being infected by sexual transmission, 56% of which were men who have sex with men. Most patients  were under HAART composed of tenofovir (TDF), lamivudine (3TC) and efavirenz (EFV) at the time of sample collection, but less frequent ARV schemes were also used  failed to have more than one viral DNA fragment PCR-amplified and were excluded from further analyses, while 23 samples amplified at least two fragments and were sequenced using NGS. Of the latter, eleven samples had the near full-length genome (NFLG) sequenced, and the remaining samples were missing one or two DNA fragments over the genome. The average number of reads obtained per sample were 774,536 . After assembling, the average coverage per nucleotide position was 7193 and it was homogeneous over the regions sequenced. The Gag CDS was complete for 22 samples (96%), the Pol CDS for 15 (65%) and the Env CDS for 17 (75%).env gene, with a frequency of 3% in patient #13.Most samples  carried archived viruses harboring at least one mutation associated with antiretroviral resistance . Among t+ T-cell counts) were obtained during the preparation of this report to evaluate the clinical progress of the viral infection. The time information varied between 9 and 17 months after the initial collection time. Median baseline CD4+ T-cell counts were 758 cells/mm3  and median baseline CD8+ T-cell counts were 8675 cells/mm3 , higher compared to the values observed at the time of patient enrollment. With respect to HIV viral load (n = 31), only one patient presented a detectable load  approximately one year after inclusion in the study. There was no change in the antiretroviral regimen of any patient, except for patients using LPV/r, who were switched to DRV/r due to changes in first-line regimens recommended by the Brazilian Ministry of Health.Follow-up data of the patients under study , and two URF were found, one URF-BF1 and one URF-BC , yet the number of substitutions did not allow the classification of this sequence as hypermutated. The other (patient #15) had a frameshift deletion (7 bp) within the pol gene, generating multiple stop codons in that ORF.Two of the 11 NFLG described harbored stop codons in one or more open reading frames (ORF). One (patient #5) had multiple stop codons in the env region of the sequences and the resutls are depicted in Virus envelope tropism analysis was performed with the reads spanning the V3 Several viral factors are known to affect and modulate progression of HIV-1-related AIDS from the early assymptomatic phase of the disease, like the virus genetic diversity, viral fitness and co-receptor tropism ,77,78,79The majority of our patients self-reported as men who have sex with men (56%), had a median time of HIV-1 diagnosis to initiation of ART of about 1.2 years and they have been successfully treated for approximately three years. With the new recommendations by the Brazilian Ministry of Health implemented at the end of 2013, it is expected that the time lapse between HIV diagnosis and HAART initiation approaches zero, which contributes to reducing virus transmission. On the other hand, the test-and-treat approach highlights the importance of monitoring the prevalence of antiretroviral resistance and TDRM at population level, to avoid resistance dissemination and therapeutic failures due to transmitted drug resistance.n = 3, 13%) or the BC recombinant structure  identified in this study showed similarity with the recombination patterns of known CRFs or other recombinants. CRFs were also not detected among HIV-1 NFLG obtained from children and adolescents from São Paulo, Brazil, which corroborate the hypothesis that novel recombinants are continually arising in the Brazilian epidemic [As seen in Latin America and the Caribbean countries, our study found a prevalence of subtype B infections 19; 83%) ,81,82. A% ,81,82.epidemic . The anaThe integrated state of HIV allows its persistence as archived proviruses in PBMCs. Consequently, this may potentially compromise the efficacy of targeted antiretroviral drugs exerting a long-term impact on responses to HAART ,93. AlsoWe found 18 patients (78%) who were successful responders to first-line ART  that carried archived viruses harboring at least one drug resistance-associated mutation. Only a single study has been previously published in this regard on HIV patients experiencing suppressive therapy . The autEight of our proviral sequences  harbored only minority variants with drug resistance-associated mutations. This finding underscores the use of more sensitive HIV genotyping techniques, since the standard genotypic resistance testing using bulk sequencing of the viral population can only detect viral variants that constitute over 15–20% of the total viral population , and likThe clinical significance of drug-resistant minority variants is still uncertain and has been addressed by several studies, due to its potential impact on the response to future treatment schemes. Some studies point out that these resistance variants would be favored by the ARV selective pressure, leading to a substitution of the wild-type virus and consequently to therapeutic failure ,36,37,38+ T-cell counts (681 cells/mm3) compared to those at the time of enrolment (535 cells/mm3). The low prevalence observed in most of the mutations found and the high adherence reported by the clinicians of the program may have contributed to the high therapeutic success rate observed in these patients one year after inclusion in the study.Despite the high prevalence of drug resistance mutations (18/23) and of mutations associated with the current ARV therapy of the patients (6/23) in our study, only one patient had detectable viral load 15 months after inclusion in the study. Unfortunately, we failed to PCR-amplify the HIV RT polymerase domain of this patient, and only the E399D mutation was found in RT connection domain of that virus. Most of the drug resistance mutations associated with the current therapy regimens of the patients analyzed were found at low prevalence at the viral population (1.3%–8%), except for the patient harboring TAMs  and maraviroc (MVC), have been used since 2005 and 2007, respectively, in therapeutic rescue strategies for patients failing previous ARV regimens ,98. The One patient (#11) was found to harbor proviruses with two transmitted drug resistance mutations to protease inhibitors (PI), all of them as minority variants (frequency between 1.9% and 2.8% of the virus population). The treatment history of each patient was considered to define the transmitted resistance. All patients were under NRTI use, so it was not possible to characterize NRTI-associated TDRM. The prevalence of TDR found in this study  was in agreement with the moderate TDR prevalence reported by other Brazilian studies, usually ranging from 5 to 10% ,106,107.Only three patients studied herein were under PI use in their treatment schemes, and two of those harbored resistance mutations in the protease. While five patients carried integrase inhibitor-related mutations, none used integrase inhibitors, which were only approved as rescue therapy in Brazil at the time of study enrollment. In those cases, mutations were carried from the virus donor to the recipients (transmitted resistance) or arouse in the patient´s virus quasispecies.+ T-cell depletion and consequently to AIDS progression [With respect to HIV coreceptor tropism, R5 viruses usually predominate during primary HIV infection, whereas the transition to X4 viruses occurs in later stages of HIV disease, being associated with more rapid CD4gression ,111,112.gression . In thisgression ,72,73,74gression  in anothHypermutated proviral sequences were detected in only one individual (patient #26) through the identification of an excessive G→A change pattern, consistent with APOBEC3G/F signature. Several early stop codons resulted from those nucleotide substitutions were observed along that HIV genome. The evidence of hypermutation as an apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC) action was also highlighted by the Stanford HIV drug resistance database during the analysis of resistance mutations for that virus.An important limitation of the present study is that the proviral sequences have not been fully evaluated for replication ability, and may be therefore defective or inactive. We only retrieved NFLG sequences from 11 of the 23 patients studied, and no ORF intactness was evaluated for the incomplete genomes. Two of the 11 NFLG sequences had stop codons due to hypermutation or to a frameshift deletion, and are thought to be defective for replication. However, missense mutations that could render the virus defective were not evaluated, and the number of defective proviruses is likely underestimated here.+ T-cell counts), and their proviral quaisispecies population was expected to be fully established. In addition, the fact that we achieved rates of 1–2% of frequency for particular DRMs in several patients suggests that we were able to reach such sensitivity rates in our assays.We are aware that additional studies should be carried out to validate our findings in a larger population. Our strict inclusion criteria did not allow the enrollment of a large cohort for this study. Another limitation was the difficulty of PCR-amplifying the archived proviral genomes from some patients in a setting of undetectable HIV viral load and early chronic infection, where PBMC archived HIV reservoirs are thought to be small. Even with the utilization of diverse strategies, some genomic regions could not be amplified for some patients. Despite we have carried out the PCR amplifications of the virus samples in duplicates to avoid false-positive mutations introduced by PCR errors, we did not quantify the input of proviral copies subjected to the analyses, what could impact the estimates of the variation found in the quasispecies. The subjects under study were in the chronic phase of infection, yet not in advanced stage of the disease (as judged by the abscence of AIDS and the high number of CD4The analysis of resistance-associated mutations in HIV-positive patients is an important issue when considering the context of broad access to antiretroviral treatment and high rates of therapeutic success, one of the main goals to be achieved worldwide against HIV infection in the coming years. The high rate of resistance-associated mutations found in our cohort, composed of patients with undetectable viral load undergoing HAART as first-line therapy, directs attention to the selection of antiretroviral resistant variants in a context of therapeutic success and early in chroninc infection. These obervations underscore the importance of further studies in order to better correlate the presence of these drug resistanc mutations and response to ART to investigate their potential association with therapeutic failure and to establish effective public policies to decrease their prevalence and transmission.


The CD4/CD8 ratio has been associated with the risk of AIDS and non-AIDS events. We describe trends in immunological parameters in people who underwent a switch to monotherapy or dual therapy, compared to a control group remaining on triple antiretroviral therapy (ART).We included patients in Icona who started a three-drug combination ART regimen from an ART-naïve status and achieved a viral load ≤ 50 copies/mL; they were subsequently switched to another triple or to a mono or double regimen. Standard linear regression at fixed points in time (12-24 months after the switch) and linear mixed model analysis with random intercepts and slopes were used to compare CD4 and CD8 counts and their ratio over time according to regimen types .10 CD4/CD8 ratio for patients on dual therapy was −0.03  –0.05, –0.0002), and the mean change in CD8 count was +99 , taking those on triple therapy as reference. In contrast, there was no evidence for a difference in CD4 count change. When using all counts, there was evidence for a significant difference in the slope of the ratio and CD8 count between people who were switched to triple  and those to dual regimen .A total of 1241 patients were included; 1073 switched to triple regimens, 104 to dual (72 with 1 nucleoside reverse transcriptase inhibitor (NRTI), 32 NRTI-sparing), and 64 to monotherapy. At 12 months after the switch, for the multivariable linear regression the mean change in the logWe found an increase in CD8 lymphocytes in people who were switched to dual regimens compared to those who were switched to triple. Patients on monotherapy did not show significant differences. The long-term implications of this difference should be ascertained.The online version of this article (10.1186/s12916-018-1046-2) contains supplementary material, which is available to authorized users. Long-term side effects of antiretroviral therapy (ART) drugs have led to the introduction in clinical practice of nucleoside reverse transcriptase inhibitor (NRTI)-sparing regimens as double or monotherapy, and their use is now recommended in specific populations according to international guidelines , 2. IndeTherefore, we hereby aimed to compare CD4/CD8 ratio changes in a group of patients who, in the presence of undetectable plasma HIV viremia, were switched to a protease inhibitor monotherapy (mono) or a dual ART, to a control group who was switched to a standard triple drug-based treatment.The Icona Foundation Study is a cohort of HIV-infected patients; this study superseded the original I.Co.N.A.  study (a detailed description of this cohort is given elsewhere ), recruiAll analyses were restricted to patients in the cohort who did the following: started a combination ART (cART) regimen including three antiretrovirals from being ART-naïve; had reached a confirmed HIV RNA ≤ 50 copies/mL; had switched to either another triple regimen or to double or monotherapy for any reason and at any time after achieving suppression, had maintained virological suppression after 12 months on this same regimen which had been switched to and had in the time window  around 12 months from the switch at least one CD4 and CD8 measurement available (only the first switch episode after achieving suppression was included). Those who switched to triple therapies were considered as the control group in order to have a more uniform population and to avoid, if possible, biases.If more measurements were available in the defined time window, the nearest to 12 months was selected. We included only patients with a date of switch that occurred after January 2006, because this was the first year in which the switch to mono/dual therapy in people with suppressed HIV RNA was introduced in clinical practice in Italy. We also selected a subgroup of patients for whom at least a CD4 and a CD8 count were available in the window  around 24 months from the switch and the same regimen and the virological suppression were still maintained. Four patients who switched to a dual therapy including maraviroc were excluded from the analyses, as exposure to this drug is believed to increase the CD8 count .The follow-up was interrupted at the date of discontinuation of at least one drug in the regimen started after the switch or at the date of the first of two consecutive HIV RNA levels > 50 or at the date of the CD4/CD8 ratio at 24 months, whichever occurred first.During the follow-up, the total number of single viral blips, where a blip is defined as (1) a single value of HIV RNA ≥ 50 copies/mL followed by a value < 50 with no treatment switch and (2) a single value of HIV RNA ≥ 200 copies/mL followed by a value < 200, was evaluated and described.The characteristics of the study population at the time of the switch, stratified by treatment strategy, were compared using the non-parametric Kruskal-Wallis test for continuous parameters and the chi-square test for categorical variables.Endpoints of the analysis were the change in CD4/CD8 ratio, in CD8, and in CD4 from the switch to 12 and 24 months. Multivariable linear regressions were used to evaluate the association between endpoints and type of regimen, adjusting for main confounders. A linear mixed model with random intercepts and random slopes for repeated measurements of ratio, CD8, and CD4 change was used to compare the course over time of these markers according to regimen strategies. The follow-up started at the date of ART switch and ended at first virological rebound defined by the first of two consecutive HIV RNA measures > 50 copies/mL or a stop/change of at least one drug of the regimen or the date of the last CD4/CD8 count at 24 months from the switch, whichever came first. The sample size of the mono/dual therapy group became too small after 24 months to yield accurate estimates.The comparison between mono/dual and triple switch was controlled for a number of potential time-fixed confounders measured at the time of the switch or at previous times: age, gender, nation of birth, mode of HIV transmission, hepatitis C virus (HCV) co-infection, AIDS diagnosis, CD4 counts and HIV RNA at ART initiation, years of HIV infection, duration of viral suppression before switch, CD4 and CD8 counts at switch, and reasons for switch. The analysis was not controlled for time-dependent confounders.A total of 1241 patients were included in the analysis; 1073 switched to triple regimens, 104 to dual regimens, and 64 to monotherapy. Concerning the baseline regimens, almost all patients were receiving tenofovir + emtricitabine, 935 (75.3%), while the third agents are described in Additional file Figure No differences in age, gender, nationality, duration of HIV infection, HCV co-infection, or virological condition at the time of first starting ART were observed among the three groups who switched to triple, mono, or dual therapy Table . The CD4p < 0.001).The switch to monotherapy compared to those who switched to triple or dual therapy less frequently was made for simplification and more frequently for toxicity, in particular, kidney toxicity . Over a median follow-up of 22 months (interquartile range (IQR) 21–24) the total numbers of single blips of HIV RNA > 200 and > 50 copies/mL during the follow-up period were similar for the three groups: 7.0% for the triple, 6.7% for the dual, and 5.9% for the mono regimen (p = 0.837) and 1.9%, 0%, and 0% (p = 0.120) respectively.The three groups of patients showed a similar length of follow-up after the date of switch  increase +0.12 vs. +0.04 (p = 0.017) compared to patients on the dual therapy and +0.07 (p = 0.079) compared to patients on the monotherapy (Table p = 0.328).At 12 months from the date of switching, patients on the triple regimen showed a higher mean CD4/CD8 ratio (logp = 0.024), but not compared to the monotherapy patients  (Table p = 0.696).The same comparison was performed after restricting to 724 patients who had data at 24 months available , and again in this analysis the triple regimens showed higher mean ratio increase compared to the dual therapy patients  Table . However3, and it was +34 in those who switched to monotherapy, while in the group who switched to triple therapy the mean change was −28 cells. This difference was observed and again was more pronounced at 24 months: patients on the triple regimen had a mean reduction of CD8 of −45 cells, patients switched to the dual regimen presented a mean increase of +28 cells, and there was an increase of +101 for those who switched to the monotherapy.Interestingly, the mean change of CD4 count from baseline to 12 months was not different between the groups: +66 for the switch to triple, +86 for the switch to dual, and +59 for the switch to mono at 12 months and +95, +89, and +141 at 24 months respectively. Conversely, the mean change of CD8 count was different among the three groups. In patients who were switched to dual therapy, at 12 months the mean change was +62 cells/mm10 ratio at 12 months and at 24 months lower than that seen in patients on the triple regimen. Patients switched to monotherapy showed a change not significantly different compared to the reference group on triple therapy.In the multivariable analysis, the type of switch strategy was associated with the change in CD4/CD8 ratio from the switch to (1) 12 months and (2) 24 months, when analyzed as continuous outcomes  +0.036, +0.044; p < 0.001). Points/year change in the log10 ratio were +0.042  for patients on the triple regimen and –0.013  for subjects who switched to dual therapy (Table p = 0.033).Our 1241 patients contributed 6528 CD4/CD8 ratio measurements over a period of 24 months. A median number of 5 (IQR 4–7) CD4/CD8 ratio values/patient were recorded. In the univariable analysis, the estimate of the overall increase in the logp = 0.559). In the univariable analysis, subjects on the triple regimen showed a change of −22.2  CD8 cells/year, while patients on dual therapy had a mean change of +114.9  CD8 cells/year. In the multivariable analysis the CD8 counts of patients on the triple regimen were reduced by −25.7 , while CD8 counts for the dual regimen showed a significant increase of +110.4 cells/year . There was evidence for a significant difference in slope between the triple and dual regimens (interaction test p = 0.009). Neither the CD4/CD8 ratio nor the CD8 count showed a significant linear trend in the group of patients on monotherapy.The overall observed trend for CD8 count was −7.2 cells/year  still displayed a CD8 count that remained abnormally high (> 1000 cells/uL) . MoreoveWhat could explain the CD8 increase observed in our patients who were switched to dual therapies? One possible explanation is that two drugs could not suppress HIV as well as three drugs can. A small residual viremia, probably in lymphoid tissues, could thus trigger the production of proinflammatory and/or homeostatic cytokines that, in turn, would stimulate and/or maintain CD8 T cell proliferation/activation. In our analysis, everybody had an HIV RNA ≤ 50 copies, but we could not create a finer classification below this threshold with the available information on the assays used. In addition, we have shown that viral blips > 50 copies/mL were rare, and their rate of occurrence was similar between the mono, dual, and triple regimen groups, while those > 200 copies/mL were actually more frequent among those who were in the triple regimen. Moreover, it has been previously described that even more sophisticated markers of residual HIV replication, such as HIV -DNA, did not differ between patients switched to dual or monotherapy compared to those continuing triple therapy , 27. HowThe changes in ratio and in CD8 were not detected in subjects switched to monotherapy. Indeed, although there was a trend in the univariable analysis, it was not confirmed with the multivariable linear regression and mixed models analysis. One possible explanation could be the lower number of subjects who were switched to monotherapy, but another could be that their better immunological profile was definitively better at baseline.We are well aware of the limitations of our study. First, among dual therapies we could not examine protease inhibitor (PI) and integrase strand transfer inhibitor (INSTI)-based combinations separately (not many people received dolutegravir either); second, a median follow-up of 2 years could not allow us to evaluate either the persistence of this phenomenon or the impact of the strategies on the onset of non-AIDS events; third, we did not have more detailed immunological data than the counts themselves, and it would be extremely relevant to stratify the analysis according to specific CD8 subpopulations and CD38/HLDR+ expression. Finally, since our analysis is retrospective and non-randomized, it only accounts for measured fixed confounders at the time of the therapy switch. Thus, we cannot rule out bias being introduced by unmeasured confounders or time-dependent confounders that have not been appropriately controlled for. In particular, even in the controlled setting of undetectable HIV RNA, patients switching to a reduced drug regimen (less than three) instead of another three-drug regimen might be determined by the factors that we did not measure.After more than 20 years since the introduction of cART because of the use in clinic of new potent and better tolerated drugs, there is now interest in reassessing the ideal number of antiretroviral drugs which need to be prescribed. In the absence of data from randomized studies, our results appear to be relevant to this debate. Current research on the clinical effectiveness of dual cART regimens is focused on non-inferiority of the virological outcome and on saving toxicity due to reducing drugs in the regimens. Nevertheless, it is possible that dual regimens have an unfavorable impact on the CD4/CD8 ratio, and this possibility must be thoroughly investigated before implementing novel drug treatment strategies including less than three drugs.At present, there is evidence supporting CD8 count monitoring as optional in patients with satisfactory virological and immunological control during ART , and thiIn this cohort of treated and virologically suppressed HIV-positive patients, the CD4/CD8 ratio continued to increase in those who switched to triple regimens, whereas it stabilized due to a selective increase in CD8 cells among those who switched to dual therapy. Our results suggest that, before dual therapy may eventually become a standard of care in patients with HIV infection, the impact of this strategy on the immune system should be further assessed and considered.Additional file 1:Table S1. Baseline third drugs in patients who switched to triple, dual, or monotherapy. (DOCX 12 kb)


This is a retrospective cohort study using observational data from anonymised primary care records. We identify and extract all patients with type 2 diabetes and associated clinical data from the Clinical Practice Research Datalink (CPRD) to inform models of disease progression and stratification of treatment.Data were extracted from CPRD on 8 August 2016. The initial data set contained all patients (n=313 485) in the database who had received a type 2 diabetes medication. Criteria were applied to identify and exclude those with type 1 diabetes, polycystic ovarian syndrome or other forms of diabetes (n=40 204), and for data quality control (n=12). We identified 251 338 patients for inclusion in future analyses of diabetes progression and treatment response.For 6-month response to treatment, measured by change in glycated haemoglobin (HbA1c), we have 91 765 patients with 119 785 treatment response episodes. The greatest impact on reduction of HbA1c occurs with first-line and second-line treatments, metformin and sulfonylurea. Patients moving to third-line treatments tend to have greater weights and higher body mass index. We have investigated the impact of non-adherence to commonly used glucose-lowering medications on HbA1c. For baseline-adjusted HbA1c change over 1 year, non-adherent patients had lower HbA1c reductions than adherent patients, with mean and 95% CI of −4.4 (−4.7 to −4.0) mmol/mol (−0.40 (−0.43 to −0.37) %).Findings from studies using these data will help inform future treatment plans and guidelines. Additional data are added with updates from CPRD. This will increase the numbers of patients on newer medications and add more data on those already receiving treatment. There are several ongoing studies investigating different hypotheses regarding differential response to treatment and progression of diabetes. For side effects, links to Hospital Episode Statistics data, where severe events such as hypoglycaemia will be recorded, will also be explored. Different treatments for type 2 diabetes have different mechanisms of action. At present it is unknown which patients will respond best to a particular therapy.The Clinical Practice Research Datalink (CPRD) is the largest validated primary care longitudinal health record database in the world and a potentially valuable resource to investigate individual response to treatment in patients with type 2 diabetes.This protocol details a method for extracting and cleaning data relating to therapy and biomarkers for studies of type 2 diabetes using CPRD.Data from the study can inform models of disease progression and response to therapy.There are small patient numbers on newer treatments, such as sodium-glucose co-transporter-2 (SGLT2) inhibitors; however, as the study is ongoing, these will increase.Understanding the clinical factors that are associated with response to glucose-lowering therapy in type 2 diabetes could form the basis of selecting subgroups of patients who are most suited to a specific treatment, an approach known as ‘stratified’ or ‘precision’ medicine. Type 2 diabetes is an area of medicine where a stratified approach to treatment may be of particular interest because there are differences in the underlying pathophysiology between patients, marked variations in drug response and diabetes progression, and a number of different glucose-lowering therapies with different mechanisms of action. Newly diagnosed patients with type 2 diabetes are usually prescribed metformin as a first therapy. There are at least six other classes of treatment available, for second-line and third-line therapies. Present treatment guidelines do not give information on which subjects will respond best to specific therapies, so decisions about which drug to prescribe are usually based on cost, possible side effects and physician preference.Using electronic databases is key to collecting evidence to inform potential stratification in type 2 diabetes. The Clinical Practice Research Datalink (CPRD) is the largest validated primary care longitudinal health record database in the world,et alThere is currently no established framework for reporting protocols for observational studies with electronic health data. There are standard and clear guidelines for reporting clinical trials and systematic reviews including preregistration. CPRD requires considerable coding to produce an analysis-ready data set, and there are many opportunities for differences in interpretation of definitions and different ways of extracting core data. Researchers have reported the need for greater transparency in planning, data quality and reporting studies of observational data.10.1136/bmjopen-2017-017989.supp1Supplementary file 1We requested CPRD data from all patients who had been prescribed one of the following diabetes medications: metformin, sulfonylurea, dipeptidyl peptidase-4 (DPP4) inhibitor, glucagon-like peptide 1 receptor (GLP1-R) agonist, thiazolidinedione or sodium-glucose co-transporter-2 (SGLT2) inhibitor, with drugs identified from the products file using British National Formulary (BNF) codes for non-insulin diabetes therapies and identifying further matches by searching both generic and trade names for these drugs. We excluded medications that were not recommended by the National Institute for Heath and Care Excellence from our final data set . Data were extracted on 8 August 2016 and contained records from 313 485 patients from up-to-standard practices. Three authors  extracted the data using different software . The final data sets and extracted values were compared to minimise errors in the data extraction process. If there were differences, the extraction coding was examined for errors or omissions.The purpose of the data extraction was to identify all patients with type 2 diabetes; we therefore wanted to remove other forms of diabetes that may still be in the data set. The study inclusion and exclusion criteria are listed in 10.1136/bmjopen-2017-017989.supp2Supplementary file 2Age at diabetes diagnosis and duration of disease are key criteria associated with severity of disease and therefore likely to affect response. Date of diabetes diagnosis is not clearly defined in CPRD, so we devised a definition based on medical codes, prescriptions and HbA1c results.The date of diabetes diagnosis was defined as the earlier of date of first prescription for a non-insulin diabetes therapy, first HbA1c result >47.5 mmol/mol (6.5%) or first diabetes diagnostic medical code. Diabetes diagnostic codes used were as defined in the inclusion medical codes list, with entries restricted to diagnosis entries only (consultation type 3). If the diagnosis date is within 91 days of the current registration date, we assume that the patient had been diagnosed prior to registration with the practice and diagnosis date is therefore unknown. To reduce historic date entry errors, we remove the medical code date if it matches patient year of birth or is of the format 01–01-XXX and the date is pre-first registration date. If the medical code date is before the first registration date and the HbA1c and therapy date is over 2 years after the current registration date, we ignore the medical code date. Ages at diagnosis and at start of treatment are calculated as appropriate given diagnosis, treatment start dates and year of birth.To ensure that we had quality control in our data, we have set criteria for data cleaning. In the therapy files of diabetes prescriptions, any records after the date of death and date of data extraction were removed. The time period of a patient in the data set was determined by transfer in/current registration date, transfer out, date of death and date of data extraction. Where possible we identified points of data entry errors, such as biomarker values outside sensible clinical limits or obvious errors in drug quantities. In our data there were 5 259 645 weight and 5 188 426 body mass index (BMI) values. Using the median of recorded patient height after the age of 18,Blood test results were extracted for use in both determining patient response to treatment (using HbA1c) and as potential predictors of response  and renal function (creatinine). The main outcome of interest in our study was change in HbA1c from baseline. A keyword search was done on the list of medical codes to identify when tests of HbA1c levels were conducted. Two researchers (BMS and SH) carried this out independently and compared results. Data were extracted from the test results file matching the HbA1c medcodes. Entries with entity 213, identifying glucose, were excluded. Our study will report HbA1c in mmol/mol, and all entries that are recorded in percentage units are converted to mmol/mol using standard formula. HbA1c entries prior to 1 January 1990 were removed from the data set; HbA1c was not used clinically before this, and the dates are more likely to be data entry errors than genuine dates.We extracted all entries with units mmol/mol, per cent and missing units. Entries with other units were deemed errors. For all missing units we assumed that values ≥3.9 and ≤20 were percentage and values >20 and ≤195 were mmol/mol. We also assumed that percentage values above 20 were misreported mmol/mol and mmol/mol values <20 were misreported percentages and adjusted the values accordingly.To investigate the effects of new therapies, we defined periods of stable treatment regimens by identifying breaks in medication both of new and existing therapies. Defining start, stop and treatment breakpoints was crucial for consistency for time periods on treatment across the different drugs. For some patients there could be a long break between prescriptions of a given drug, and therefore clinically this would be considered stopping and restarting the drug.Gaps of >6 months (183 days) were used to indicate stopping a drug. Start dates for a drug were defined if there was no other prescription for that drug in the previous 6 months. Patients must have at least two prescriptions for the drug in the 6 months from the first prescription. A patient was considered to have stopped taking a drug if there was no new prescription of that drug in the 6 months following the last prescription.An important outcome is initial response to therapy measured by change in HbA1c. We extracted the first instance of a new therapy with start and stop dates as defined earlier. To ensure that the response was due to a new therapy, we defined a period of stable therapy as no changes in treatment regimen in the 3 months (91 days) prior to the first prescription of the new therapy (starting or stopping any other diabetes medication), and the end of the period as the earliest of either the last prescription date or the last prescription date before another diabetes medication is started or stopped (a break in treatment regimen). Most patients will have a therapy added to their existing treatment regimen and will be on polytherapy; this is standard treatment protocol for patients with type 2 diabetes. The addition or change in therapy is because the existing medication regimen has failed. Defining a stable period of treatment prior to adding a new therapy will ensure that response is attributed to the therapy of interest and not confounded by influence due to a reaction, addition or subtraction of other diabetes medications.Patients on insulin as one of their other diabetes medications were excluded from analyses. Patients on DPP4 inhibitors and thiazolidinedione as monotherapy were excluded as these were considered unusual treatment regimens. We excluded any therapies starting within 3 months of the current registration date. This ensured that we were not missing any prescriptions that may have occurred before registration. We cannot be sure that registration is the start of their treatment; we may be missing prescriptions due to a break in registration with the practice or a new patient at the practice who has previously been on the treatment. Patients who had a duration of diabetes of less than 1 year are excluded from analyses; their behaviour during this first year is expected to be different from those with established diabetes. Those on thiazolidinedione or gliptin as monotherapy were also excluded.Baseline HbA1c was taken as the closest value to the first prescription date of a particular therapy between 6 months prior and +7 days of that date. Longer time periods showed changes in the biomarker values. Six-month and 12-month responses were the closest HbA1c±3 months (91 days) of either 6 months or 12 months after the first prescription date for that drug. A 3-month window was chosen as we would see an effect on HbA1c after 3 months, and the effect at 6 and 12 months would still be seen in the 3 months after the 6-month and 12-month points. If there was a change in treatment regimen (starting or stopping another diabetes medication) between 6 and 12 months, we did not extract HbA1c values past the date of treatment regimen change. This was to ensure that any change in HbA1c was due to the therapy of interest and not to other changes in glucose-lowering therapies.Our ±3 months’ allowance for HbA1c response means that potentially we would have an HbA1c response from patients after 3 months and 9 months for 6-month and 12-month responses, respectively. We therefore also included time on therapy for 6-month response as at least 3 months on therapy and for 12-month response at least 9 months (274 days) on therapy. Where the patient was on therapy for less than 6 or 12 months, their response HbA1c values were taken between 3 months from the start of therapy and 3 months after their last prescription or up to a change in treatment regimen, whichever is the earlier.Baseline, 6-month and 12-month measures for other biomarkers as listed in The medication possession ratio (MPR) is used to assess adherence to medication.Some data cleaning is required prior to calculating MPR or dose. The number of days of available tablets can be calculated using either the number of days field, or by dividing the daily dose by total quantity prescribed. Missing values can be calculated using any two of the three values. Daily dose is defined as the number of tablets taken each day. Daily dose is sometimes recorded as milligram rather than dose. Where dosage is recorded as 40, 80, 160 or 240 and the product name contains 80 mg, we convert to a number of tablets per day (daily dose). Similarly for daily doses of 500, 1000, 1500 and 2000 where the product name contains 500 mg, the daily dose is calculated. Total quantities of treatment prescribed over 365 (0.1% of 33 671 394 prescriptions) were assumed to be data entry errors and changed to 0 (missing). Quantities of 556 for metformin prescriptions are also considered data entry errors and corrected to 56. For multiple prescriptions of the same drug class on the same day, we took the average of the number of days, quantity and dosage.MPR and dose are calculated only where we had an uninterrupted stable treatment period of at least 12 months. The time period for MPR was drug start (first prescription) to the date of the first prescription after 365 days on the drug. The adherence measurement was the number of days of available tablets divided by the number of days between the start of treatment and the first prescription after 365 days, expressed as a percentage.The daily dose was recorded as 0 in 30% of the 33 671 394 diabetes prescription records. These were often initial prescriptions where patients’ dosage instructions may be titrated. The number of days of tablets cannot be calculated without a daily dose. Where this occurred, we removed the prescription from the adherence calculation, and the time between the start of the prescription with zero daily dose and the next was removed from the denominator. At least three prescriptions with non-zero daily doses were required to calculate the adherence. This was deemed to be sufficient information to perform a calculation of dose. Patients were considered adherent to their medication if the adherence value is between 80% and 120% (inclusive). Examination of the data shows that adherence above 120% is likely caused by unusual prescription regimens and incorrect quantities entered.Similar to the MPR data, the time period for dose calculations was drug start date to the last prescription before the 12-month HbA1c response value. For each prescription the percentage of maximum dose was calculated by multiplying dosage (drug strength) by the daily dose and then dividing by the maximum possible dosage. The maximum dose was taken from the BNF. We extracted the maximum dose and a weighted mean dose (weighted by days of tablets issued) over the time period. As with adherence we required at least three non-zero daily dose values to calculate dose information.In our model of initial response to medication as measured by change in HbA1c, 91 765 patients had information on 119 785 first instances of therapy . PatientThe impact of taking glucose-lowering medications intermittently, rather than as prescribed, on HbA1c has not been previously studied. We found that low adherence to commonly used glucose-lowering medications, as measured by the MPR, was associated with smaller reductions in HbA1c.The CPRD is a superb resource reflecting everyday clinical practice and is an ideal data set to identify factors associated with drug response in a common condition like type 2 diabetes. However, considerable data cleaning is required and rules developed to extract non-entered data, like date of diabetes diagnosis. The work on response to glucose-lowering therapy mainly requires prescription information and biochemistry such as HbA1c; both of these are directly downloaded, greatly reducing errors and increasing coverage. However the potential for errors still exists especially for fields that are entered by hand and different individuals may use different codes for the same clinical event. This means cleaning and manipulation of the data are required, and this paper aims to outline our rationale and methods.The main purpose defining our cohort data selection is to ensure transparency and clarity in extracting response data on patients with type 2 diabetes in CPRD. We have laid out clear guidelines for extracting and refining data, as illustrated in Electronic healthcare databases lack the randomisation to therapy of a randomised control trial, but real-world effectiveness and utilisation of therapies can be assessed.Results from the studies using CPRD data will be compared with other observational data (GoDARTS database) and trial data, now available through online portals such as Clinical Data Study Request and Yale University Open Data Access YODA. Where possible, models developed in one population will be fitted to data from other populations. The direct and indirect comparisons across different data sets will help to validate any findings.We have detailed our method for defining and extracting initial response to diabetes therapies from patients with type 2 diabetes to inform stratification. This provides a clear protocol for other researchers to reproduce, evaluate and extend work on type 2 diabetics in electronic healthcare databases.


As HIV enters the human body, its main target is the CD4 cell which it turns into a factory that produces millions of other HIV particles. These HIV particles target new CD4 cells resulting in the progression of HIV infection to AIDS. A continuous depletion of CD4 cells results in opportunistic infections, for example tuberculosis (TB). The purpose of this study is to model and describe the progression of HIV/AIDS disease in an individual on antiretroviral therapy (ART) follow up using a continuous time homogeneous Markov process. A cohort of 319 HIV infected patients on ART follow up at a Wellness Clinic in Bela Bela, South Africa is used in this study. Though Markov models based on CD4 cell counts is a common approach in HIV/AIDS modelling, this paper is unique clinically in that tuberculosis (TB) co-infection is included as a covariate.The method partitions the HIV infection period into five CD4-cell count intervals followed by the end points; death, and withdrawal from study. The effectiveness of treatment is analysed by comparing the forward transitions with the backward transitions. The effects of reaction to treatment, TB co-infection, gender and age on the transition rates are also examined. The developed models give very good fit to the data.The results show that the strongest predictor of transition from a state of CD4 cell count greater than 750 to a state of CD4 between 500 and 750 is a negative reaction to drug therapy. Development of TB during the course of treatment is the greatest predictor of transitions to states of lower CD4 cell count. Transitions from good states to bad states are higher on male patients than their female counterparts. Patients in the cohort spend a greater proportion of their total follow-up time in higher CD4 states.From some of these findings we conclude that there is need to monitor adverse reaction to drugs more frequently, screen HIV/AIDS patients for any signs and symptoms of TB and check for factors that may explain gender differences further. The life cycle of HIV starts as it enters the human body, its major target being a white blood cell called T-helper cells or CD4 cells . Once thAs the immune system is compromised, the individual is now prone to opportunistic infections like tuberculosis (TB). TB may occur at any stage of HIV disease and is frequently the first recognised presentation of underlying HIV . HIV andThe need to address the challenges associated with HIV/AIDS progression in the presence of TB coinfection has prompted this study and also to analyse HIV disease history based CD4 multi-states and death/loss to follow-up in a single model. However, most studies use Kaplan-Meier analysis and Cox proportional hazards regression models –5. KaplaContinuous-time homogeneous Markov models have been used since early in the epidemic to model disease progression of HIV/AIDS patients, and there has been some recent renewed interest in the use of these models. In 1989 Longini et al. used a 5-state Markov model based on the clinical indicators of the HIV disease progression . In 2011In this study, we use 7-staged continuous-time Markov model to assess the disease progression of HIV/AIDS patients receiving ART from a clinic in Bela-Bela, South Africa. The first 5 stages are based on CD4 cell counts and the end points are either death or withdrawal from study. In addition to the gender and age differences in HIV/AIDS progression, we further assess the effects of having TB as the initial marker of HIV/AIDS, developing TB during the course of treatment, developing some adverse effects to treatment (Reaction), CD4 baseline and viral load baseline. Though Markov models based on CD4 cell counts is a common approach in HIV/AIDS modelling, this paper is unique clinically in that tuberculosis (TB) co-infection is included as a covariate.In medical research, the state of the patient at observation time is the only thing known with certainty. The researcher may know the time interval in which a transition has occurred, but not the exact time. Thus, homogeneous Markov models which are interval censored can handle such data . The trat ≥ 0 the probability of moving from state i to state j is given by:Transition probabilities for continuous-time homogeneous models only depend on the difference between the two observation times. That is, for all Ft is the natural Filtration of the stochastic process. P[Xt = j| Ft], therefore, represents the probability that the stochastic process Xt is in state j at time t given the history of the process up to time t. The Markov property implies that all the history of the process is contained in the state currently occupied, Xs = i. The transition probabilities of a continuous time homogeneous Markov process Xt, t ≥ 0 is given by:This is the Markov property, where The equations obey the Chapman-Kolmogorov equations:In this paper we describe, using the theory of continuous time Markov processes, and using real data on an evolving disease such as AIDS. Also, the effects of covariates, including TB, on baseline transition rates is considered. Models with and without covariates are fitted and compared using the likelihood ratio test.The next section explores the methods of Markov modelling and an illustrative case study on HIV progression is given. In this section, data used in the analysis is described and we explain formulation of the model based on the data. This is followed by a section on the results and discussions. The final section concludes on the findings.∆t. In this study pij(t) are continuously differentiable and are subject to the initial condition:Formulation of the continuous-time homogeneous model is done by considering transition probabilities over narrow interval of time δij is a Kronecker delta, pii(0) = 1 means that at t = 0 the system maintains its original state and pij(0) = 0 means that there is no change of state when no time elapses. The force of transition from state i to j is defined as:αij, for i = 1, …, 5 and j = 1, …, 7, does not vary over time and satisfies the following conditions; Once the transition intensities are known, the transition probabilities can be obtained by solving a system of differential equations known as the Kolmogorov’s forward equation subject to the initial conditions stated in eq. (2). The Kolmogorov’s forward equation is as follows:k is a state that the system can pass through as it makes a transition from state i to state j. The time homogeneous models are fitted for this data to assess effectiveness of the treatment by comparing the forward transition and the reverse transitions. This then lead to building of models that allow transitions in both directions.where t = 0). After 3 years of treatment uptake, 173 females and 71 males were remaining in the study. Thirty-eight females had died and 16 withdrew and their status was not known after 3 years of treatment up take. Nineteen of the males died during the first three years and two had withdrawn and it was not known whether they were alive or dead. A 2-year old (subject 81) together with subject 82 were detected by the residuals plot as an outlier and it was removed from the analysis meaning that the remaining 317 patients were used for analysis. About 50 and 65% of the female and male deaths respectively occurred during the first 6 months of treatment uptake. The interquartile range of patient ages is  years with a mean and median age of 39.53 and 40 years respectively. The ages were negatively skewed (skew = −0.24) which means that there were more younger patients than older patients in this cohort.The model is initially applied on 319 HIV patients on anti-retroviral therapy (ART) from a Wellness clinic in Bela Bela, South Africa, from year 2005 to year 2009. Two hundred and twenty-seven of these patients were females and 92 were males at treatment commencement  cell count below 200, 59 individuals with CD4 cell count between 200 and 350, 11 individuals with CD4 cell count between 350 and 500, 6 individuals with CD4 cell count between 500 and 750 and 1 individual with CD4 cell count above 750. At t = 0 the CD4 cell count had mean of 156 cells/mm3, a median of 116 cell/mm3 and the maximum CD4 cell count was 1202 cells/mm3. The mean viral load baseline (VLBL) for these patients was 105,573.35 copies/mm3 and it ranged from 56 to 818,600 copies/mm3. The median viral load was 58,523.00 copies/mm3. From these individuals 155 had a WHO stage baseline (WSBL) of 4 which is related to severe HIV symptoms. WSBL is the categorisation of HIV/AIDS at baseline basing on the clinical markers as defined by World Health Organisation (WHO).At time 3, 20 had a CD4 baseline between 200 and 350cells/mm3, 2 had CD4 baseline between 350 and 500cells/mm3, 2 between 500 and 750cells/mm3 and 19 had unknown CD4 baseline. These patients completed their TB treatment before commencement of ART. Fifty-two patients developed TB during the treatment period and 12 of these patients had TB before commencement of treatment. During the first 6 months of treatment uptake, 35 patients died and from these deaths, only five were attributed to having TB before commencement of ART.Although some individuals developed TB (DTB) during the course of treatment, 109 patients had TB as an initial marker of HIV. From the individuals who had TB before (TBB4) commencement of antiretroviral therapy (ART), 66 had a CD4 baseline below 200cells/mmt = 0 were mainly a combination of d4T-3TC-EFV (administered to 207 patients) and d4T-3TC-NVP (administered to 83 patients). The second line regimens were mainly a combination of AZT-3TC-EFV/NVP and were given to patients who developed some adverse reaction. These second line regimens were frequently used from 2 to 4 years post-treatment commencement. The therapeutic intervention lowers the number of infectious free virus particles in the circulation, and in some cases to beyond detection. This results in a reduction on the density of infected cells, causing a rise on the CD4 cell count of infected individuals. So generally the CD4 cell count of an individual receiving therapeutic intervention is expected to rise to well above 500 cell/mm3, assuming a proper adherence to treatment. Hence the use of increase in CD4 cell count as the marker of efficacy of treatment.A combination therapy was administered to all HIV-infected individual in the cohort. The therapeutic intervention inhibits the actions of reverse transcriptase enzyme and/or protease of new infectious free HIV by the HIV-infected cell. The drug regimens at During the course of treatment, some individuals developed some adverse reaction (React) to treatment. For these individuals the adverse reactions were treated and drugs administered to them were changed. Change of treatment was also based on the viral load monitoring.For the purpose of analysis, the variables are coded into the model as follows:t + ∆t, the state of an HIV-infected individual is defined basing on the CD4 cell count level or whether the individual is dead or has withdrawn as follows:At any time Basing on these seven states, progression of HIV positive individuals on treatment is defined by the state diagram on Fig. The information in Fig. Basing on the classification above, Table i to i ± j are higher for all the values in which j = 1 than for j > 1 where i, j ∈ { 1, …, 5}. As a result a bidirectional model is proposed which defines transitions from state i to i ± 1 or from i to j = 6; 7.Table t, t + ∆ t), where ∆t is a very small value, there is a transition from any one of the states i = 1, 2, …, 5 (transient states) to state j = 1, 2, …, 7 defined as follows:αij,  where j = i − 1;CD4 cell count of an individual is expected to rise due to efficacy of treatment at a rate of αij,  where j = i + 1;Some individuals fail to adhere to treatment therapy. These individuals can move to a state of lower CD4 cell count at a rate of i = 1, 2, …, 5 an infected individual can die (state 6) at a rate of αi6;From any state i = 1, 2, …, 5 can decide to withdraw (state 7) at a rate of αi7;An individual in state αii =  − λi =  − . This is based on the fact that the sum of transition rates from any state is equal to zero.An individual can remain in the same state at a rate of The model is formulated basing on the assumptions that between times (Q(t):These assumptions can be represented by the following transition rate matrix Once the transition rate matrix has been obtained, the matrix of transition probabilities can be obtained using Kolmogorov’s forward differential equations defined in (3). This yields the following differential equations for the Markov jump processes:i, for i = 1, 2, ...5, to state j = 1, …, 7. pij(t) represents the probability that a patient in state i makes a transition to state j and its coefficients represent the transition rates. For example, in equation (α12 + α16 + α17) = α11. These states denoted by i are defined based on the CD4 cell count grouping. So there is a possibility of a backward or forward movement transition between transient states due to failure or efficacy of treatment respectively. There is no possible transition from state i = 6 and state i = 7 because these states are absorbing states where i = 6 represents death of an infected individual and state i = 7 represents withdrawal from treatment by an infected individual. All the analysis is done using the package ‘msm’ for multistate modelling in R software. The package was developed by Jackson in 2011  is a k = 8-dimensional vector of covariates and βij is a vector of k regression parameters relating the instantaneous rate of transitions from state i to state j to the covariates Z and baseline intensities i to state j as shown in Table βij’s, regression coefficients, were calculated and the results are shown in where The -2xLL for the model fitted in Table The strongest predictor of immune deterioration from a CD4 level between 350 and 500 to a CD4 level between 200 and 350 (3 to 4) is developing TB during treatment, with a hazard ratio of over 2. Developing TB is also the strongest predictor of immune deterioration from 4 to 5, with a hazard ratio also greater than 2. This means that TB is the major cause of further immune deterioration when the immune system is too weak. Hence the recommendation that HIV patients should continuously have their TB status checked. Those individuals who had TB before enrolment had the strongest predictor for the transition from state 3 to state 6. These patients had a hazard ratio of over 2 times more likely to die from state 3 than those who were enrolled without having TB. However, for these individuals, transitions to better states were generally higher than transitions to worse states for almost all states.A hazard ratio of 6.46 for the predictor variable male shows that males were over 6 times more likely to transit from state 2 to 3 than their female counterparts. The hazard ratios of males from a bad state to a better state are less than 1, which is an indication that males are less likely to respond to treatment compared to females.The hazard ratios for the transitions to a better state for patients who were enrolled with CD4 counts below 350 are less than one, but hazards to worse states are greater than one, an indication that starting treatment when the CD4 levels are below 350 retards immune recovery. The transitions to the death state for individuals who started treatment when they were on the WHO stage of 4 are all more than one, meaning that starting treatment with a WHO stage of 4 is a leading cause of being absorbed in the death state.The prevalence for the model with covariates were plotted to examine areas of poor fit of the time-homogeneous model with covariates. The plots are shown in Fig. Figure In this paper a continuous-time homogeneous Markov model is fitted to explore predictors of HIV/AIDS progression for patients on antiretroviral therapy. A continuous-time homogeneous model is fitted with and without covariates and comparison of these two models is done using the likelihood ratio test. Parameters that define progression of HIV/AIDS were estimated and these include transition intensities, mean sojourn times and probability of each state being next or jump chains. The fitted model is used to analyse the effects of the covariates on the transition intensities. These covariates were reaction to treatment, development of TB during treatment and gender among others.p-value =10{−4}. The results show that transition rates to immune recovery are generally higher than the transition rates to immune deterioration. However, the results show that the strongest predictor of immune deterioration from state 1 (CD4 cell count greater than 750) to state 2 (CD4 cell count between 500 and 750) is reaction to treatment. These patients are 4 times more likely to transit from state 1 to state 2 than those who did not react to treatment.Results from the likelihood ratio test show that the model with covariates provides a better fit than the model with no covariates with a 3 are two times higher than that of patients who were not initially diagnosed with TB.Patients who developed TB during the course of treatment have higher chances of immune deterioration than immune recovery compared to those who did not develop any TB co-infection. These incidences are quite high for transition from state 4 (CD4 cell count between 200 and 350) to state 5 (CD4 cell count below 200). For these states the immune system is still weak. As a result patients on antiretroviral drugs should consistently be screened for TB co-infection. Patients who had initially been diagnosed with TB before commencement of ART recover better from HIV/AIDS disease except that transitions to death for patients with CD4 cell count between 350 and 500 cells/mmFrom this cohort, transitions to bad states are higher for males than for their female counterparts. This is quite pronounced on transitions from state 2 (CD4 cell count between 500 and 750) to state 3 (CD4 cell count between 350 and 500) where the hazards for males are 6 times that of females. This result is consistent with the findings from Maskew and others, they discovered that men gain fewer CD4 cell counts than did women . An asse3 returned to nearly normal CD4 cell count after 6 years [The results from the fitted model show that the rates of immune recovery were much higher than the rates of immune deterioration which is an indication of effectiveness of treatment. Patients who started treatment when their CD4 baseline was at least 350 had higher rates of immune recovery than those who had a lower CD4 baseline. This result is commensurate with the findings from Moore and Keruly who also discovered that patients with baseline CD4 cell count above 350 cells/mm 6 years . The pro 6 years –25, who The mean sojourn times revealed that patients take longer time in the AIDS defining states (CD4 cell count below 200) before they move to the other states. Research has also shown that CD4 cell count rises gradually despite the suppressed viral load particularly in older patients. Hence, there is need to use both CD4 cell count and viral load in monitoring the efficacy of treatment. The younger people below the age of 40 have higher chances of immune recovery than the older ones. This finding is supported by some previous studies who concluded lower mean CD4 increases for older patients than younger patients . Alioum Although continuous time Markov models can handle multiple or recurrent outcomes compared to the Kaplan Meier analysis and Cox proportional hazards models, the assumption of constant hazard function that is frequently unrealistic  and puts


UNAIDS recently defined the 90-90-90 target as a way to end the HIV epidemic. However, the proportion of virological success following antiretroviral therapy (ART) may not be as high as the anticipated 90%, and may in fact be highly heterogeneous. We aimed to describe the proportion of virological success in sub-Saharan Africa and to identify factors associated with the proportion of virological success.We performed a systematic review and meta-analysis focusing on the proportion of patients in sub-Saharan Africa who demonstrate virological success at 12 and 24 months since ART initiation, as well as at 6 and 36 months, where possible. Programme factors associated with the proportion of virological success were identified using meta-regression. Analyses were conducted using both on-treatment (OT) and intention-to-treat (ITT) approaches.Eighty-five articles were included in the meta-analysis, corresponding to 125 independent study populations. Using an on-treatment approach, the proportions ) of virological success at 12 (n = 64) and at 24 (n = 32) months since ART initiation were 87.7% (81.3–91.0) and 83.7% (79.8–87.6), respectively. Univariate analysis indicated that the proportion of virological success was not different by study design. Multivariate analysis at 24 months showed that the proportion of virological success was significantly larger in studies conducted in public sector sites than in other sites (p = 0.045). Using an ITT approach, the proportions (95% CI) of virological success at 12 (n = 50) and at 24 (n = 20) months were 65.4% (61.8–69.1) and 56.8% (51.3–62.4), respectively. At 12 months, multivariate analysis showed that the proportion of success was significantly lower in cohort studies than in trials . At 24 months, univariate analysis demonstrated that the proportion of success was also lower in cohorts.Regardless of the time following ART initiation, and of the threshold, proportions of virological success were highly variable. Evidence from this review suggests that the new international target of 90% of patients controlled is not yet being achieved, and that in order to improve the virological outcome, efforts should be made to improve retention in care. At the end of 2015, the World Health Organization (WHO) estimated that about 36.7 million people worldwide were living with HIV, with Sub-Saharan Africa the most affected region in the world with 70% of the HIV burden ))) OR HIV/AIDS[Title/Abstract]) OR ((hiv OR AIDS OR HIV/AIDS[MeSH Terms])))) AND (Africa[Title/Abstract] OR Africa[MeSH Terms]) AND )) OR ))) AND ) OR )) AND (("2009/01/01"[PDat]: "2014/05/24"[PDat])). We also manually searched the references of relevant articles to identify studies that might have been missed.The outcomes of interest were the proportions of patients demonstrating virological success at 6, 12, 24 and 36 months since ART initiation, using on-treatment (OT) and/or intention-to-treat (ITT) analysis.st line ART, corresponding to the WHO recommendations at that time in Africa.We considered studies conducted in sub-Saharan Africa that reported virological outcomes for at least one of the time points of interest, strictly in HIV-1 infected treatment-naïve adults (with the definition of the start of adulthood varying between studies: from 14 to 20 years) on 1nd line ART, or in which more than 20% of patients received non-conventional ART regimen, were not included. Studies reporting virological outcomes on less than 20 patients were also excluded, as such small samples were considered unlikely to provide accurate information and were likely subject to significant biases.Studies that did not report virological outcomes at a specific time point or that only included HIV-2 infected populations, paediatric populations, patients on 2All articles were independently reviewed by two of the authors (FT and YM). The titles and abstracts of all identified articles were reviewed to determine the eligibility of full text papers for inclusion. The remaining full-length articles were retrieved and read independently by the two authors to determine whether to include them in the meta-analysis. Results were compared and discrepancies in opinion between authors as to whether studies should be included were resolved by discussion.To extract relevant data from the selected articles, a standardized collection form was prepared. Data extraction and validity assessment were carried out independently and in duplicate by two of the authors (FT and YM), and any discrepancies resolved by discussion. The authors contacted the corresponding authors of individual studies to retrieve any missing data. The completed forms and resulting electronic database were checked by a third author (AC) for data accuracy and quality.For clinical trials, each study arm was considered as an independent study and outcomes were reported independently –17. For For all studies, we recorded the threshold at which virological success had been defined, the number of patients evaluated, and the number/proportion of patients demonstrating virological success, enabling OT analysis. For ITT analysis, death, loss to follow-up (LTFU), and the switching to second-line ART prior to the virological evaluation were considered as virological failures, while patients who withdrew from the study or who transferred out of the study prior to the virological evaluation were not considered. The ITT result was not included if the proportion of patients not considered for unknown reasons exceeded 40%.The quality of the studies included in this meta-analysis was assessed based on the STROBE  checklist . Availabmetan command in Stata . Heterogeneity was indicated if the Cochran test was significant at the level of 0.05 and/or if the I-square statistic exceeded 0.50.The overall proportion of patients demonstrating virological success was estimated using a random effect model, following the method of DerSimonian and Laird , withoutnd line ART before the evaluation time-point was regarded as virological failure. The standard deviation for all proportions was obtained using a Gaussian approximation.The proportion of patients demonstrating virological success was estimated as the number of patients in virological success divided by the number of patients with a viral load measurement , or as the number of patients in virological success divided by the number of patients who should have reached the evaluation time point minus the number of transfers/withdrawal before the evaluation time point . Substitution to 2Due to the variability in reporting of the virological thresholds, virological success at <50 copies/mL included all results at 20, 40 and 50 copies/mL thresholds. In the same way, reporting of results at 100, 150, 200, 400 and 500 copies/mL thresholds were combined and considered as virological success at 400 copies/mL. Virological failure was defined as >1000 copies/mL, in accordance with the latest WHO recommendations .The effect of the following factors on the level of virological success was investigated: beginning of enrolment (i.e. ART initiation) ≤2005, ≥2006), end of enrolment , study design, study region  . Again, Using an ITT approach, 15 evaluations of virological success were available at 40 copies/mL threshold, 12 evaluations of virological success were available at 1000 copies/mL and 3 evaluations of virological success were available at 5000 copies/mL threshold. The overall proportions (95% CI) of virological success at these thresholds were 54.9% (40.9–69.0), 66.5% (60.8–72.2) and 76.9% (71.2–82.6), respectively .Using an OT approach, 32 evaluations of virological success at 24 months after ART initiation at the threshold of 400 copies/mL were available. The overall proportion of virological success (95% CI) was 83.7% (79.8–87.6) . The CocIn univariate analysis, we did not find evidence that the proportions differed by study design . The proUsing an OT approach, 7 evaluations of virological success were available at 40 copies/mL threshold, 14 evaluations of virological success were available at 1000 copies/mL threshold and 8 evaluations of virological success were available at 5000 copies/mL. The overall proportions (95% CI) of virological success at these thresholds were 83.1% (75.5–90.6), 84.1% (79.2–88.9) and 91.1% (87.3–94.9), respectively .Using an ITT approach, 20 evaluations of virological success at 24 months after ART initiation at the threshold of 400 copies/mL were considered. The overall proportion of virological success (95% CI) was 56.8% (51.3–62.4) . Again, Using an ITT approach, 8 evaluations of virological success at 24 months after ART initiation were available at 40 copies/mL threshold and 6 evaluations of virological success were available at 1000 copies/mL threshold. The overall proportions of virological success (95% CI) were 57.6% (49.6–65.5) and 47.0% (36.1–58.0), respectively. Only one evaluation was performed at the threshold of 5000 copies/mL. This indicated a 67.3% proportion of virological success (95% CI: 63.4–71.2) [Using an OT approach, 13 evaluations of virological success at 6 months after ART initiation were available at the threshold of 40 copies/mL, 44 evaluations of virological success were available at the threshold of 400 copies/mL, 11 evaluations of virological success were available at the threshold of 1000 copies/mL, and 3 evaluations of virological success were available at the threshold of 5000 copies/mL. The overall proportions (95% CI) of virological success at these thresholds were 71.7% (62.8–80.7), 83.8% (81.0–86.6), 85.5% (81.7–89.3), and 92.7% (87.6–97.8), respectively.Using an ITT approach, 7 evaluations of virological success at 6 months after ART initiation were available at the threshold of 40 copies/mL, 31 evaluations of virological success were available at the threshold of 400 copies/mL, 10 evaluations of virological success were available at the threshold of 1000 copies/mL and 2 evaluations of virological success were available at the threshold of 5000 copies/mL. The overall proportions (95% CI) of virological success at these thresholds were 65.0% (48.4–81.6), 68.5% (62.8–74.2), 70.9% (62.7–79.2) and 87.1% (83.6–90.5), respectively .Using an OT approach, 12 evaluations of virological success at 36 months after ART initiation were available at the threshold of 400 copies/mL, 4 evaluations of virological success were available at the threshold of 1000 copies/mL, and 3 evaluations of virological success were available at the threshold of 5000 copies/mL. The overall proportions (95% CI) of virological success at these thresholds were 85.8% (82.2–89.3), 81.7% (77.9–85.5) and 92.8% (89.4–96.2), respectively.Using an ITT approach, 5 evaluations of virological success at 36 months after ART initiation were available at the threshold of 400 copies/mL, and 2 evaluations provided proportions of virological success at the thresholds of 1000 copies/mL. The overall proportions (95% CI) of virological success at these thresholds were 37.8% (22.7–52.9) and 31.6% (0.03–59.8) , respectThe funnel plots of the individual studies showed an asymmetry, with smaller studies reporting higher proportions of virological success more often . ApplyinIn this meta-analysis, we reported the proportion of patients in virological success while on first-line ART in sub-Saharan Africa, based on 85 recently published articles that led to 125 independent study populations comprised of 156,798 individuals. To our knowledge, this is the most recent and largest meta-analysis to compare virological success at specific time points in sub-Saharan Africa.We did not observe an improvement in the proportion of virological success as compared to previous meta-analyses , 15, 17.In all the studies considered in this meta-analysis, the most common threshold for the evaluation of virological success was 400 copies/mL. Using this threshold and an OT approach, , the proportion of virological success was around 85%. Interestingly, this proportion of virological success remained stable regardless of the time point between M6 and M36. This result is likely due to the fact that only those who remain alive and in care are considered, which induces a healthy survivor bias–a bias which has been reported previously .Several of the studies included in the meta-analysis also reported virological success at the threshold of 1000 copies/mL. This is the threshold for virological failure as defined by the latest WHO recommendations . Using aThe results of our meta-analysis on the proportions of virological success at 6, 12, 24 and 36 months after ART initiation defined at the thresholds of 400 and 1000 copies/mL are comparable to the results from another meta-analysis performed in resource limited settings , despitend line ART, transfers/withdrawals, and for the number of patients who did not have a virological evaluation. This enabled us to estimate the proportion of patients in virological success using an ITT approach in an additional 38 studies.Of the 125 study populations considered, only 28 (22.4%) directly reported the proportion of patients in virological success using an ITT approach. To better estimate the proportions of success, we used results when reported using an ITT approach, but also searched the articles for the number of deaths, loss to follow-up, switches to 2Regardless of the time point following ART initiation and the threshold used to define virological success, results using an ITT approach were lower than those using an OT approach. OT results evaluate ART efficacy for those still on ART, while ITT results evaluate the effectiveness of the programmes delivering ART, as those who died or were lost to follow-up are considered as failures, and these numbers increase with time. The difference in estimated proportions between the two approaches was substantial and as large as 15% at 6 months after ART initiation, reaching 20% at 12 months, and 25% at 24 months. This difference can be explained by death and patients LTFU, but also by the number of patients who did not have a virological evaluation performed. Studies in which the proportion of non-evaluated patients was >40% , 118 werWe wanted to investigate whether clinical trials, cohort studies, and cross-sectional studies provide similar quality information on proportions of virological success.The fact that the proportions of virological success, using an OT approach, compare well across the various study designs indicates the effectiveness of ART in Africa. It also seems to indicate that patients in routine care are as adherent to ART as those enrolled in trials, even though the latter group may be more sensitised to this issue.Cross-sectional studies, if based on a representative sample of the targeted population, have fewer logistical and financial constraints, allowing results to be obtained more quickly than cohort studies. Such studies are of special interest for national HIV treatment programmes, as they provide valuable data for the planning of future activities. They are also recommended by WHO for the surveillance of resistance in patients on ART .On the other hand, using an ITT approach, the proportion of virological success was significantly higher in randomised trials than in cohorts at 12 months after ART initiation, and tended to be higher at 24 months. This could be explained by the selection of healthier participants, but also by more frequent monitoring of patients, more systematic clinical and biological examinations and by reinforced clinical teams in trials as compared to routine practice. All of these factors may have reduced attrition in clinical trials, and may explain the higher proportion of virological success as compared to cohort studies. Indeed, a relatively high mortality in the first 6 to 12 months after ART initiation and substantial loss to follow-up have been widely reported in resource limited settings , 121. ThUnexpectedly, in ITT analysis there was no evidence of an effect of tracking of LTFU patients on the proportions of virological success at 12 months after ART initiation, and at 24 months the proportion of virological success was even lower in studies that tracked LTFU patients than in studies that did not. However, this result is likely influenced by two studies that implemented tracking, but which had very low proportions of virological success. In one of these studies, there was a very large (32%) proportion of deaths . The othThe proportion of patients in virological success in OT analysis was found to be larger in public health centres compared to other sites . Public centres may have been selected based on their capacities to participate in such studies , but these are not necessarily representative of all health centres in the country. Faith-based and NGO supported centres may treat lower socio-economic populations and data from these centres may be under-reported, leading to a publication bias. Motivations to publish may differ between centres, while the funnel plots used in this meta-analysis indicated that some small study bias may be present.Some limitations of this meta-analysis should be noted. Studies considered were heterogeneous in their design but also in the populations they considered. Most studies were based on the general population with limited selection criteria, but some were restricted to specific populations, such as patients co-infected with hepatitis or tuberculosis, or women who had previously been exposed to ART for PMTCT, etc. Studies on these specific populations had a limited sample size, and were therefore thought to contribute to the heterogeneity among estimations. However, the effect of these specific populations was investigated and was not found to be significant.The studies also differed in terms of their definition of virological success, partly due to the variability of the techniques used to measure viral load, which do not all have the same thresholds. To control for this variability, we performed estimations of virological success at various thresholds. In only a small number of studies was virological success based on the result of two viral load measurements. This may also have contributed to the heterogeneity of our results. However, the limited number of such studies meant that this effect on the meta-analysis could not be evaluated.To explain the heterogeneity of virological success across various study populations, programme characteristics were investigated . However, no specific characteristics were identified. This may be explained by characteristics not being mentioned in the individual studies, resulting in the pooling of studies in the meta-analysis that were not in fact similar.By definition, only studies with virological evaluation were considered in our meta-analysis. Many programs in Africa, where no viral load monitoring is available have thus not been considered in our meta-analysis. However, we do not anticipate that this is a source of bias in our results. Indeed, some of the studies we considered were virological evaluations at a specific time point, without routine viral load monitoring. This was particularly true for cross-sectional evaluations, nested in cohort studies, and we did not find evidence that results from cross-sectional evaluations differed from the other studies.In conclusion, this is the largest, most up-to-date meta-analysis on proportions of virological success at specific time points after ART initiation and at multiple thresholds, based on both OT and ITT analysis and the first comparison between clinical trials, cohort studies, and cross-sectional studies. Evidence from this review suggests that the current international target to have 90% of patients on ART in virological success will require intensive strategies to improve adherence to ART, and retention in care. It is also important to improve access to viral load monitoring in order to detect those at risk of virological failure, as early as possible, offering them effective ART.S1 Checklist(DOCX)Click here for additional data file.S1 FigEstimation of the rate of virological success based on different threshold (in copies/mL) on on-treatment (full box) and intention-to treat (empty box) at: A) at 6 months of ART, B) 12 months of ART, c) 24 months of ART, and D) 36 months of ART.(DOCX)Click here for additional data file.S2 FigA) OT analysis at 12 months; B) ITT analysis at 12 months; C) OT analysis at 24 months; D) ITT analysis at 24 months.(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.