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
| العنوان: | 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 |
|---|---|
| المؤلفون: | Luminita Ene, Shanmugam Saravanan, Andrew D. Revell, E Schuelter, Julio S. G. Montaner, Catherine A Rehm, Brian K. Agan, Peter Reiss, Tobias F. Rinke de Wit, Robin Wood, Maurizio Zazzi, David A. Cooper, Elisa de Lazzari, Carina Cesar, Ricardo Sobhie Diaz, Brendan Larder, Gerardo Alvarez-Uria, Rdi Data, Scott Wegner, J A Metcalf, Gordana Dragovic, H. Clifford Lane, Carlo Torti, Roos Barth, Gaston Picchio, Dechao Wang, Omar Sued, Richard Harrigan, Stefano Vella, John D. Baxter, Richard Norris, Marie-Pierre deBethune, Ard I van Sighem, Lidia Ruiz, Julio Montaner, Anton Pozniak, Schlomo Staszewski, Brian Gazzard, Bonaventura Clotet, Sundhiya Mandalia, Juan Sierra Madero, Bonventura Clotet, Lotty Ledwaba, Cliff Lane, Wataru Sugiura, Adrian Streinu-Cercel, Andrew Carr, Kim C. E. Sigaloff, José M. Gatell, Dolphina Cogill, Hugo Tempelman, Karl Hesse, Vincent C. Marconi, Raph L Hamers, Colette Smith, Rolf Kaiser, Gabrielle Dettorre, Carl Morrow, Pachamuthu Balavskrishnan, Tulio de Oliveira, Sean Emery, Cecilia Sucupira, Laura Monno, Emanuel Vlahakis, Raph L. Hamers, Paul Khabo, Chris Hoffmann, Ard van Sighem, Mark T. Nelson, Maria-Jesus Perez-Elias |
| المساهمون: | AII - Infectious diseases, Global Health, APH - Personalized Medicine, APH - Quality of Care, APH - Aging & Later Life, Infectious diseases |
| المصدر: | Journal of antimicrobial chemotherapy, 73(8), 2186-2196. Oxford University Press |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | 0301 basic medicine, Microbiology (medical), Adult, Male, Sustained Virologic Response, Computer science, Anti-HIV Agents, Pyridines, 030106 microbiology, HIV Infections, Quinolones, Maraviroc, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Statistics, medicine, Humans, Pharmacology (medical), Computer Simulation, 030212 general & internal medicine, Genotyping, Developing Countries, Original Research, Pharmacology, Computational model, Sulfonamides, Receiver operating characteristic, Elvitegravir, Drug Substitution, Random forest, Regimen, Infectious Diseases, Treatment Outcome, chemistry, Pyrones, Female, Tipranavir, medicine.drug |
| الوصف: | 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. |
| تدمد: | 1460-2091 0305-7453 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb0b2b20ef76c760d2eb547fed68117d https://pubmed.ncbi.nlm.nih.gov/29889249 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....bb0b2b20ef76c760d2eb547fed68117d |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14602091 03057453 |
|---|