Factors associated with the continuum of care of HIV infected patients in Belgium

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Pharmacy refill adherence outperforms self-reported methods in predicting HIV therapy outcome in resource-limited settings

BACKGROUND: Optimal adherence to antiretroviral therapy is critical to prevent HIV drug resistance (HIVDR) epidemic. The objective of the study was to investigate the best performing adherence assessment method for predicting virological failure in resource-limited settings (RLS). METHOD: This study was a single-centre prospective cohort, enrolling 220 HIV-infected adult patients attending an HIV/AIDS Care and Treatment Centre in Dar es Salaam, Tanzania, in 2010. Pharmacy refill, self-report (via visual analog scale [VAS] and the Swiss HIV Cohort study-adherence questionnaire), pill count, and appointment keeping adherence measurements were taken. Univariate logistic regression (LR) was done to explore a cut-off that gives a better trade-off between sensitivity and specificity, and a higher area under the curve (AUC) based on receiver operating characteristic curve in predicting virological failure. Additionally, the adherence models were evaluated by fitting multivariate LR with stepwise functions, decision trees, and random forests models, assessing 10-fold multiple cross validation (MCV). Patient factors associated with virological failure were determined using LR. RESULTS: Viral load measurements at baseline and one year after recruitment were available for 162 patients, of whom 55 (34%) had detectable viral load and 17 (10.5%) had immunological failure at one year after recruitment. The optimal cut-off points significantly predictive of virological failure were 95%, 80%, 95% and 90% for VAS, appointment keeping, pharmacy refill, and pill count adherence respectively. The AUC for these methods ranged from 0.52 to 0.61, with pharmacy refill giving the best performance at AUC 0.61. Multivariate logistic regression with boost stepwise MCV had higher AUC (0.64) compared to all univariate adherence models, except pharmacy refill adherence univariate model, which was comparable to the multivariate model (AUC = 0.64). Decision trees and random forests models were inferior to boost stepwise model. Pharmacy refill adherence (<95%) emerged as the best method for predicting virological failure. Other significant predictors in multivariate LR were having a baseline CD4 T lymphocytes count < 200 cells/μl, being unable to recall the diagnosis date, and a higher weight. CONCLUSION: Pharmacy refill has the potential to predict virological failure and to identify patients to be considered for viral load monitoring and HIVDR testing in RLS. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2458-14-1035) contains supplementary material, which is available to authorized users

Full-genome next-generation sequencing of hepatitis C virus to assess the accuracy of genotyping by the commercial assay LiPA and the prevalence of resistance-associated substitutions in a Belgian cohort

Funding Information: This work and KTC were supported by grants from the Fonds voor Wetenschappelijk Onderzoek Vlaanderen (FWO) ( G069214 , G0B2317N , 1S38819N ). LC acknowledges FWO travel grant for a research visit at University of Oxford ( V431117N ). The authors thank the staff in Oxford in their support of the laboratory work and the donation of the probes used for enrichment of HCV. Publisher Copyright: © 2022 Elsevier B.V.Background: Although most currently used regimens for Hepatitis C virus (HCV) infections can be initiated without prior knowledge of genotype and subtype, genotyping is still useful to identify patients who might benefit from a personalized treatment due to resistance to direct-acting antivirals (DAA). Objectives: To assess the utility of full-genome next-generation sequencing (FG-NGS) for HCV genotyping. Study design: 138 HCV plasma samples previously genotyped by VERSANT HCV Genotype Assay (LiPA) were subjected to FG-NGS and phylogenetically genotyped Genome Detective. Consensuses were analysed by HCV-GLUE for resistance-associated substitutions (RASs) and their impact on treatment response was investigated. Results: 102/138 (73.9%) samples were sequenced to a genome coverage and depth of >90% of the HCV open reading frame covered by >100 reads/site. Concordant genotype and subtype results were assigned in 97.1% and 79.4% of samples, respectively. FG-NGS resolved the subtype of 13.7% samples that had ambiguous calls by LiPA and identified one dual infection and one recombinant strain. At least one RAS was found for the HCV genes NS3, NS5A, and NS5B in 2.91%, 36.98% and 27.3% samples, respectively. Irrespective of the observed RAS, all patients responded well to DAA treatment, except for HCV1b-infected patients treated with Zepatier (33.3% failure rate (5/15)). Conclusion: While LiPA and FG-NGS showed overall good concordance, FG-NGS improved specificity for subtypes, recombinant and mixed infections. FG-NGS enabled the detection of RAS, but its predictive value for treatment outcome in DAA-naïve patients remains uncertain. With additional refinements, FG-NGS may be the way forward for HCV genotyping.publishersversionpublishe

Rapidly Fatal Acanthamoeba Encephalitis and Treatment of Cryoglobulinemia

We describe a 66-year-old woman with therapy-refractory cryoglobulinemia treated with rituximab, plasmapheresis, and steroids; a case of fatal meningoencephalitis caused by Acanthamoeba spp. then developed. Such infections are rare and show an unusually rapid course (possibly related to rituximab)

Comparison of Changes in Bone Density and Turnover with Abacavir-Lamivudine versus Tenofovir-Emtricitabine in HIV-Infected Adults: 48-Week Results from the ASSERT Study

Background. Abacavir-lamivudine and tenofovir DF-emtricitabine fixed-dose combinations are commonly used as first-line antiretroviral therapies. However, few studies have comprehensively compared their relative safety profiles. Methods. In this European, multicenter, open-label, 96-week study, antiretroviral-naive adult subjects with human immunodeficiency virus (HIV) infection were randomized to receive either abacavir-lamivudine or tenofovir- emtricitabine with efavirenz. Primary analyses were conducted after 48 weeks of treatment. Bone mineral density (BMD), a powered secondary end point, was assessed by dual energy x-ray absorptiometry. Bone turnover markers (osteocalcin, procollagen 1 N-terminal propeptide, bone specific alkaline phosphatase, and type 1 collagen cross-linked C telopeptide [CTx]) were assessed in an exploratory analysis. Results. A total of 385 subjects were enrolled in the study. BMD loss was observed in both treatment groups, with a significant difference in the change from baseline in both total hip (abacavir-lamivudine group, -1.9%; tenofovir-emtricitabine group, -3.6%; P= 6% was more common in the tenofovir-emtricitabine group (13% of the tenofovir-emtricitabine group vs 3% of the abacavir-lamivudine group had a loss of >= 6% in the hip; 15% vs 5% had a loss of >= 6% in the spine). Bone turnover markers increased in both treatment groups over the first 24 weeks, stabilizing or decreasing thereafter. Increases in all markers were significantly greater in the tenofovir-emtricitabine treatment group than in the abacavir-lamivudine group at week 24. All but CTx remained significantly different at week 48 (eg, osteocalcin: abacavir-lamivudine group, +8.07 mg/L; tenofovir-emtricitabine group, +11.92 mg/L; P Conclusions. This study demonstrated the impact of first-line treatment regimens on bone. Greater increases in bone turnover and decreases in BMD were observed in subjects treated with tenofovir-emtricitabine than were observed in subjects treated with abacavir-lamivudine

HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment. In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND). Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms. In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP. The fitness landscapes have similar predictive power for treatment response under indinavir-containing regimen as standard rules-based algorithms, and additionally allow predicting genetic evolution under indinavir selective pressure

Trends and predictors of transmitted drug resistance (TDR) and clusters with TDR in a local Belgian HIV-1 epidemic

We aimed to study epidemic trends and predictors for transmitted drug resistance (TDR) in our region, its clinical impact and its association with transmission clusters. We included 778 patients from the AIDS Reference Center in Leuven (Belgium) diagnosed from 1998 to 2012. Resistance testing was performed using population-based sequencing and TDR was estimated using the WHO-2009 surveillance list. Phylogenetic analysis was performed using maximum likelihood and Bayesian techniques. The cohort was predominantly Belgian (58.4%), men who have sex with men (MSM) (42.8%), and chronically infected (86.5%). The overall TDR prevalence was 9.6% (95% confidence interval (CI): 7.7-11.9), 6.5% (CI: 5.0-8.5) for nucleoside reverse transcriptase inhibitors (NRTI), 2.2% (CI: 1.4-3.5) for non-NRTI (NNRTI), and 2.2% (CI: 1.4-3.5) for protease inhibitors. A significant parabolic trend of NNRTI-TDR was found (p = 0.019). Factors significantly associated with TDR in univariate analysis were male gender, Belgian origin, MSM, recent infection, transmission clusters and subtype B, while multivariate and Bayesian network analysis singled out subtype B as the most predictive factor of TDR. Subtype B was related with transmission clusters with TDR that included 42.6% of the TDR patients. Thanks to resistance testing, 83% of the patients with TDR who started therapy had undetectable viral load whereas half of the patients would likely have received a suboptimal therapy without this test. In conclusion, TDR remained stable and a NNRTI up-and-down trend was observed. While the presence of clusters with TDR is worrying, we could not identify an independent, non-sequence based predictor for TDR or transmission clusters with TDR that could help with guidelines or public health measures

Estimating the individualized HIV-1 genetic barrier to resistance using a nelfinavir fitness landscape

<p>Abstract</p> <p>Background</p> <p>Failure on Highly Active Anti-Retroviral Treatment is often accompanied with development of antiviral resistance to one or more drugs included in the treatment. In general, the virus is more likely to develop resistance to drugs with a lower genetic barrier. Previously, we developed a method to reverse engineer, from clinical sequence data, a fitness landscape experienced by HIV-1 under nelfinavir (NFV) treatment. By simulation of evolution over this landscape, the individualized genetic barrier to NFV resistance may be estimated for an isolate.</p> <p>Results</p> <p>We investigated the association of estimated genetic barrier with risk of development of NFV resistance at virological failure, in 201 patients that were predicted fully susceptible to NFV at baseline, and found that a higher estimated genetic barrier was indeed associated with lower odds for development of resistance at failure (OR 0.62 (0.45 - 0.94), per additional mutation needed, p = .02).</p> <p>Conclusions</p> <p>Thus, variation in individualized genetic barrier to NFV resistance may impact effective treatment options available after treatment failure. If similar results apply for other drugs, then estimated genetic barrier may be a new clinical tool for choice of treatment regimen, which allows consideration of available treatment options after virological failure.</p

RegaDB: Community-driven data management and analysis for infectious diseases

Summary: RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB pr