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

作者：Revell Andrew D^*; Wang Dechao; Wood Robin; Morrow Carl; Tempelman Hugo; Hamers Raph; Alvarez Uria Gerardo; Streinu Cercel Adrian; Ene Luminita; Wensing Annemarie; Reiss Peter; van Sighem Ard I; Nelson Mark; Emery Sean; Montaner Julio S G; Lane H Clifford; Larder Brendan A

来源：Journal of Antimicrobial Chemotherapy, 2014, 69(4): 1104-1110.

DOI：10.1093/jac/dkt447

• antiretroviral therapy
• resource-limited settings
• genotyping

The optimal individualized selection of antiretroviral drugs in resource-limited settings is challenging because of the limited availability of drugs and genotyping. Here we describe the development of the latest computational models to predict the response to combination antiretroviral therapy without a genotype, for potential use in such settings.
Random forest models were trained to predict the probability of a virological response to therapy (50 copies HIV RNA/mL) following virological failure using the following data from 22567 treatment-change episodes including 1090 from southern Africa: baseline viral load and CD4 cell count, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. The models were assessed during cross-validation and with an independent global test set of 1000 cases including 100 from southern Africa. The models accuracy [area under the receiver-operating characteristic curve (AUC)] was evaluated and compared with genotyping using rules-based interpretation systems for those cases with genotypes available.
The models achieved AUCs of 0.790.84 (mean 0.82) during cross-validation, 0.80 with the global test set and 0.78 with the southern African subset. The AUCs were significantly lower (0.560.57) for genotyping.
The models predicted virological response to HIV therapy without a genotype as accurately as previous models that included a genotype. They were accurate for cases from southern Africa and significantly more accurate than genotyping. These models will be accessible via the online treatment support tool HIV-TRePS and have the potential to help optimize antiretroviral therapy in resource-limited settings where genotyping is not generally available.

• 出版日期2014-4