A D Revell

Summary

Publications

  1. pmc 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
    Andrew D Revell
    The HIV Resistance Response Database Initiative RDI, London, UK
    J Antimicrob Chemother 71:2928-37. 2016
  2. pmc Potential impact of a free online HIV treatment response prediction system for reducing virological failures and drug costs after antiretroviral therapy failure in a resource-limited setting
    Andrew D Revell
    The HIV Resistance Response Database Initiative RDI, 14 Union Square, London N1 7DH, UK
    Biomed Res Int 2013:579741. 2013
  3. pmc Modelling response to HIV therapy without a genotype: an argument for viral load monitoring in resource-limited settings
    A D Revell
    The HIV Resistance Response Database Initiative, London, UK
    J Antimicrob Chemother 65:605-7. 2010
  4. doi request reprint The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool
    Andrew D Revell
    RDI, London, UK
    AIDS 25:1855-63. 2011
  5. pmc Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings
    A D Revell
    The HIV Resistance Response Database Initiative RDI, London, UK
    J Antimicrob Chemother 68:1406-14. 2013
  6. doi request reprint A comparison of computational models with and without genotyping for prediction of response to second-line HIV therapy
    A D Revell
    The HIV Resistance Response Database Initiative RDI, London, UK
    HIV Med 15:442-8. 2014

Detail Information

Publications6

  1. pmc 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
    Andrew D Revell
    The HIV Resistance Response Database Initiative RDI, London, UK
    J Antimicrob Chemother 71:2928-37. 2016
    ..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...
  2. pmc Potential impact of a free online HIV treatment response prediction system for reducing virological failures and drug costs after antiretroviral therapy failure in a resource-limited setting
    Andrew D Revell
    The HIV Resistance Response Database Initiative RDI, 14 Union Square, London N1 7DH, UK
    Biomed Res Int 2013:579741. 2013
    ..The objective of this retrospective study was to examine if the HIV-TRePS online treatment prediction tool could help reduce treatment failure and drug costs in such settings...
  3. pmc Modelling response to HIV therapy without a genotype: an argument for viral load monitoring in resource-limited settings
    A D Revell
    The HIV Resistance Response Database Initiative, London, UK
    J Antimicrob Chemother 65:605-7. 2010
    ..This finding provides further, indirect support for the use of viral load monitoring for the long-term optimization of HAART in resource-limited settings...
  4. doi request reprint The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool
    Andrew D Revell
    RDI, London, UK
    AIDS 25:1855-63. 2011
    ..Here we describe the development and testing of random forest models to power an online treatment selection tool...
  5. pmc Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings
    A D Revell
    The HIV Resistance Response Database Initiative RDI, London, UK
    J Antimicrob Chemother 68:1406-14. 2013
    ..Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART without a genotype and evaluated their potential as a treatment support tool in RLSs...
  6. doi request reprint A comparison of computational models with and without genotyping for prediction of response to second-line HIV therapy
    A D Revell
    The HIV Resistance Response Database Initiative RDI, London, UK
    HIV Med 15:442-8. 2014
    ..We 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...