Publication

Title: 2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings
Authors: Revell AD, Wang D, Perez-Elias MJ, Wood R, Cogill D, Tempelman H, Hamers RL, Reiss P,, van Sighem AI, Rehm CA, Pozniak A, Montaner JSG, Lane HC, Larder BA, RDI Data and Study Group Collaborators: Reiss P, van Sighem A, Montaner J, Harrigan R, Rinke de Wit T, Hamers R, Sigaloff K, Agan B, Marconi V, Wegner S, Sugiura W, Zazzi M, Kaiser R, Schuelter E, Streinu-Cercel A, Alvarez-Uria G, Perez-Elias MJ, de Oliveira T, Gatell J, Lazzari E, Gazzard B, Nelson M, Pozniak A, Mandalia S, Smith C, Ruiz L, Clotet B, Staszewski S, Torti C, Lane C, Metcalf J, Rehm CA, Perez-Elias MJ, Vella S, Dettorre G, Carr A, Norris R, Hesse K, Vlahakis E, Tempelman H, Barth R, Wood R, Morrow C, Cogill D, Hoffmann C, Ene L, Dragovic G, Diaz R, Sucupira C, Sued O, Cesar C, Madero JS, Balavskrishnan P, Saravanan S, Emery S, Cooper D, Torti C, Baxter J, Monno L, Torti C, Gatell J, Clotet B, Picchio G, deBethune MP, Perez-Elias MJ, Emery S, Khabo P, Ledwaba L .
Journal: J Antimicrob Chemother,73(8):2186–2196 (2018)

Journal Impact Factor (I.F.): 8
Number of citations (Google Scholar): 20

Abstract

OBJECTIVES: Optimizing antiretroviral drug combination on an individual basis can be challenging, particularly in settings with limited access to drugs and genotypic resistance testing. Here we describe our latest computational models to predict treatment responses, with or without a genotype, and compare their predictive accuracy with that of genotyping.

METHODS: Random forest models were trained to predict the probability of virological response to a new therapy introduced following virological failure using up to 50?000 treatment change episodes (TCEs) without a genotype and 18?000 TCEs including genotypes. Independent data sets were used to evaluate the models. This study tested the effects on model accuracy of relaxing the baseline data timing windows, the use of a new filter to exclude probable non-adherent cases and the addition of maraviroc, tipranavir and elvitegravir to the system.

RESULTS: The no-genotype models achieved area under the receiver operator characteristic curve (AUC) values of 0.82 and 0.81 using the standard and relaxed baseline data windows, respectively. The genotype models achieved AUC values of 0.86 with the new non-adherence filter and 0.84 without. Both sets of models were significantly more accurate than genotyping with rules-based interpretation, which achieved AUC values of only 0.55-0.63, and were marginally more accurate than previous models. The models were able to identify alternative regimens that were predicted to be effective for the vast majority of cases in which the new regimen prescribed in the clinic failed.

CONCLUSIONS: These latest global models predict treatment responses accurately even without a genotype and have the potential to help optimize therapy, particularly in resource-limited settings.