An update to the HIV-TRePS system: the development of new computational models that do not require a genotype to predict HIV treatment outcomes

Revell, Andrew D.; Wang, Dechao; Wood, Robin; Morrow, Carl; Tempelman, Hugo; Hamers, Raph L; Alvarez-Uria, Gerardo; Streinu-Cercel, Adrian; Ene, Luminiţa; Wensing, Annemarie M. J.; Reiss, Peter; Sighem, A.I. van; Nelson, Mark; Emery, Sean; Montaner, Julio S. G.; Lane, H. Clifford; Larder, Brendan A.; Sighem, Ard van; Julio, Montaner; Harrigan, Richard; Wit, Tobias Rinke de; Sigaloff, Kim C.E.; Agan, Brian K.; Marconi, Vincent C.; Wegner, Scott A.; Sugiura, Wataru; Zazzi, Maurizio; Gatell, José M.; Lazzari, Elisa de; Gazzard, Brian; Pozniak, Anton; Mandalia, Sundhiya; Ruiz, Lı́dia; Clotet, Bonaventura; Staszewski, Schlomo; Torti, Carlo; Lane, Cliff; Metcalf, Julie; Perez-Elias, Maria-Jesus; Carr, Andrew; Norris, Richard H.; Hesse, Karl; Vlahakis, Emanuel; Barth, Roos E.; Dragović, Gordana; Cooper, David J.; Baxter, John D.; Monno, Laura; Clotet, B; Picchio, Gastón; deBethune, Marie-Pierre

doi:10.1093/jac/dkt447

Cited by 12 publications

(16 citation statements)

References 11 publications

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“…Both sets of models were significantly more accurate predictors of virological response to second‐line therapy comprising ritonavir‐boosted lopinavir plus two or three NtRTIs than genotyping with Stanford rules‐based interpretation. It is encouraging that the NG models were able to achieve this level of performance and this is consistent with the results of other studies . It should be acknowledged that treatment decisions are not made on the basis of a genotype with rules‐based interpretation alone; physicians include other information in their decisions, such as the genetic barrier associated with different inhibitors.…”

Section: Discussionsupporting

confidence: 84%

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A comparison of computational models with and without genotyping for prediction of response to second‐line HIV therapy

Revell¹,

Boyd

Wang³

et al. 2014

HIV Medicine

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ObjectivesWe compared the use of computational models developed with and without HIV genotype vs. genotyping itself to predict effective regimens for patients experiencing first-line virological failure. MethodsTwo sets of models predicted virological response for 99 three-drug regimens for patients on a failing regimen of two nucleoside/nucleotide reverse transcriptase inhibitors and one nonnucleoside reverse transcriptase inhibitor in the Second-Line study. One set used viral load, CD4 count, genotype, plus treatment history and time to follow-up to make its predictions; the second set did not include genotype. Genotypic sensitivity scores were derived and the ranking of the alternative regimens compared with those of the models. The accuracy of the models and that of genotyping as predictors of the virological responses to second-line regimens were compared. ResultsThe rankings of alternative regimens by the two sets of models were significantly correlated in 60−69% of cases, and the rankings by the models that use a genotype and genotyping itself were significantly correlated in 60% of cases. The two sets of models identified alternative regimens that were predicted to be effective in 97% and 100% of cases, respectively. The area under the receiver-operating curve was 0.72 and 0.74 for the two sets of models, respectively, and significantly lower at 0.55 for genotyping. ConclusionsThe two sets of models performed comparably well and significantly outperformed genotyping as predictors of response. The models identified alternative regimens predicted to be effective in almost all cases. It is encouraging that models that do not require a genotype were able to predict responses to common second-line therapies in settings where genotyping is unavailable.

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Section: Discussionsupporting

confidence: 84%

“…with the results of other studies [5,8]. It should be acknowledged that treatment decisions are not made on the basis of a genotype with rules-based interpretation alone; physicians include other information in their decisions, such as the genetic barrier associated with different inhibitors.…”

Section: Discussionmentioning

confidence: 99%

A comparison of computational models with and without genotyping for prediction of response to second‐line HIV therapy

Revell¹,

Boyd

Wang³

et al. 2014

HIV Medicine

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“… 39 ATHENA collaborates with other research groups and observational cohorts in western Europe, the USA and Canada. Data sharing and collaboration has taken place or is ongoing with the Collaboration of Observational HIV Epidemiological Research Europe (COHERE), 40 EuroSIDA, 41 the European Pregnancy and Paediatric HIV Cohort Collaboration (EPPICC) 42 within the Paediatric European Network for the Treatment of AIDS (PENTA), the European Coordinating Committee for the Integration of Ongoing Coordination Actions Related to Clinical and Epidemiological HIV Research (EuroCoord); A Collaboration on HIV-2 infection (ACHI E V 2E ) 43 ; Antiretroviral Therapy Cohort Collaboration (ART-CC) 44 ; Data collection on Adverse events of anti-HIV Drugs (D:A:D) 45 ; HIV Cohorts Analysed Using Structural Approaches to Longitudinal data (HIV-CAUSAL) Collaboration 46 ; Bridging the Evolution and Epidemiology of HIV in Europe (BEEHIVE) 47 ; HIV Resistance Response Database Initiative (RDI) 48 49 ; and International epidemiology Databases to Evaluate AIDS (IeDEA). 50 …”

Section: Strengths and Limitationsmentioning

confidence: 99%

AIDS Therapy Evaluation in the Netherlands (ATHENA) national observational HIV cohort: cohort profile

et al. 2018

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PurposeIn 1998, the AIDS Therapy Evaluation in the Netherlands (ATHENA) national observational HIV cohort was established to demonstrate the lifesaving effectiveness of triple combination antiretroviral therapy, including HIV-protease inhibitors, that had recently been made available for clinical use. Subsequently, the HIV Monitoring Foundation was established by the Dutch Ministry of Health, Welfare and Sport to continue ATHENA as an open cohort in order to continue the registration and monitoring of all HIV-positive people as an integral part of HIV care in all 26 HIV treatment centres in the Netherlands.ParticipantsTo date, a total of 25 036 participants have been enrolled in the cohort, with 263 600 person-years of follow-up. As of 1 January 2017, 19 035 HIV-1-positive participants were known to be in care: 18 824 adults (81% men and 19% women) and 211 children (47% boys and 53% girls). The remaining 6001 participants had either died (46%), were lost to care (29%) or had moved abroad (25%).Findings to dateToday, with over 20 years of follow-up, the ATHENA cohort has provided extensive knowledge on HIV treatment, comorbidities and coinfections and created insight into the transmission dynamics of the HIV epidemic.Future plansATHENA continues to enrol and monitor HIV positive people entering HIV care in the Netherlands. Future research will continue to provide tangible input into HIV care and prevention policies in the Netherlands and internationally.

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“…These predications are based on inputted clinical and demographical data which include most recent VL, CD4 cell count, and history of previous treatment. 41 This technology has the potential to improve clinical decision making by health care providers who are not required to attain high level of professional expertise. 6 …”

Section: Laboratory Technologies For Testing Hiv Resistancementioning

confidence: 99%

Human Immunodeficiency Virus Resistance Testing Technologies and Their Applicability in Resource-Limited Settings of Africa

et al. 2017

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There has been tremendous breakthrough in the development of technologies and protocols for counselling, testing, and surveillance of resistant human immunodeficiency virus strains for efficient prognosis and clinical management aimed at improving the quality of life of infected persons. However, we have not arrived at a point where services rendered using these technologies can be made affordable and accessible to resource-limited settings. There are several technologies for monitoring antiretroviral resistance, each with unique merits and demerits. In this study, we review the strengths and limitations of prospective and affordable technologies with emphasis on those that could be used in resource-limited settings.

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An update to the HIV-TRePS system: the development of new computational models that do not require a genotype to predict HIV treatment outcomes

Cited by 12 publications

References 11 publications

A comparison of computational models with and without genotyping for prediction of response to second‐line HIV therapy

A comparison of computational models with and without genotyping for prediction of response to second‐line HIV therapy

AIDS Therapy Evaluation in the Netherlands (ATHENA) national observational HIV cohort: cohort profile

Human Immunodeficiency Virus Resistance Testing Technologies and Their Applicability in Resource-Limited Settings of Africa

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