An update to the HIV-TRePS system: The development and evaluation of new global and local computational models to predict HIV treatment outcomes, with or without a genotype
| Autor: | Revell, A. D., Wang, D., Wood, R., Morrow, C., Tempelman, H., Hamers, R. L., Reiss, P., van Sighem, A. I., Nelson, M., Montaner, J. S. G., Lane, H. C., Larder, B. A., Harrigan, R., de Wit, T. R., Hamers, R., Sigaloff, K., Agan, B., Marconi, V., Wegner, S., Sugiura, W., Zazzi, M., Kaiser, R., Schuelter, E., Streinu-Cercel, A., Alvarez-Uria, G., Gatell, J., Lazzari, E., Gazzard, B., Pozniak, A., Mandalia, S., Webster, D., Smith, C., Ruiz, L., Clotet, B., Staszewski, S., Torti, C., Lane, C., Metcalf, J., Perez-Elias, M. -J., Vella, S., Dettorre, G., Carr, A., Norris, R., Hesse, K., Vlahakis, E., Barth, R., Hoffmann, C., Ene, L., Dragovic, G., Diaz, R., Sucupira, C., Sued, O., Cesar, C., Madero, J. S., Emery, S., Cooper, D., Baxter, J., Monno, L., Picchio, G., Debethune, M. -P., Khabo, P., Ledwaba, L. |
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| Přispěvatelé: | Global Health, Infectious diseases, Amsterdam institute for Infection and Immunity, Amsterdam Public Health, Internal medicine |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Microbiology (medical) Genotype Anti-HIV Agents 030106 microbiology Human immunodeficiency virus (HIV) Antiretroviral Therapy HIV Infections Biology medicine.disease_cause Bioinformatics Algorithms Health Resources Humans Models Statistical ROC Curve Software South Africa Treatment Outcome Viral Load Antiretroviral Therapy Highly Active Computer Simulation 03 medical and health sciences 0302 clinical medicine Models Statistics medicine Pharmacology (medical) Highly Active 030212 general & internal medicine Hiv treatment Genotyping Original Research Pharmacology Computational model Statistical Random forest Regimen Infectious Diseases Test set |
| Zdroj: | on behalf of the RDI Data and Study Group 2016, ' An update to the HIV-TRePS system : The development and evaluation of new global and local computational models to predict HIV treatment outcomes, with or without a genotype ', Journal of Antimicrobial Chemotherapy, vol. 71, no. 10, dkw217, pp. 2928-2937 . https://doi.org/10.1093/jac/dkw217 Journal of antimicrobial chemotherapy, 71(10), 2928-2937. Oxford University Press Journal of Antimicrobial Chemotherapy, 71(10):dkw217, 2928-2937. Oxford University Press |
| ISSN: | 0305-7453 |
| DOI: | 10.1093/jac/dkw217 |
| Popis: | Objectives: Optimizing antiretroviral drug combination on an individual basis in resource-limited settings is challenging because of the limited availability of drugs and genotypic resistance testing. Here, we describe our latest computational models to predict treatment responses, with or without a genotype, and compare the potential utility of global and local models as a treatment tool for South Africa. Methods: Global random forest models were trained to predict the probability of virological response to therapy following virological failure using 29 574 treatment change episodes (TCEs) without a genotype, 3179 of which were from South Africa and were used to develop local models. In addition, 15 130 TCEs including genotypes were used to develop another set of models. The 'no-genotype' models were tested with an independent global test set (n = 1700) plus a subset from South Africa (n = 222). The genotype models were tested with 750 independent cases. Results: The global no-genotype models achieved area under the receiver-operating characteristic curve (AUC) values of 0.82 and 0.79 with the global and South African tests sets, respectively, and the South African models achieved AUCs of 0.70 and 0.79. The genotype models achieved an AUC of 0.84. The global no-genotype models identified more alternative, locally available regimens that were predicted to be effective for cases that failed their new regimen in the South African clinics than the local models. Both sets of models were significantly more accurate predictors of outcomes than genotyping with rules-based interpretation. Conclusions: These latest global models predict treatment responses accurately even without a genotype, out-performed the local South African models and have the potential to help optimize therapy, particularly in resource-limited settings. |
| Databáze: | OpenAIRE |
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