JAC Advance Access originally published online on July 19, 2009
Journal of Antimicrobial Chemotherapy 2009 64(3):616-624; doi:10.1093/jac/dkp252
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Original research |
Rules-based HIV-1 genotypic resistance interpretation systems predict 8 week and 24 week virological antiretroviral treatment outcome and benefit from drug potency weighting
1 Department of Molecular Biology, University of Siena, Siena, Italy 2 INMI Lazzaro Spallanzani, Rome, Italy 3 Informa SRL, Rome, Italy 4 Institute of Clinical Infectious Diseases, Catholic University ‘Sacro Cuore’, Rome, Italy 5 Microbiology and Virology Laboratory, Ospedali Riuniti, Bergamo, Italy 6 Microbiology and Virology Laboratory, San Martino Hospital, Genoa, Italy 7 Unit of Virology, IRCCS San Matteo Hospital, Pavia, Italy 8 Division of Infectious Disease, Siena University Hospital, Siena, Italy 9 Diagnostica & Ricerca San Raffaele, Milan, Italy 10 Unit of Immunohematology and Transfusional Medicine, Cremona Hospital, Cremona, Italy 11 Section of Infectious Diseases and Immunopathology, Department of Clinical Sciences ‘Luigi Sacco’, Milan, Italy 12 Institute of Clinical Infectious Diseases, Ancona University Hospital, Ancona, Italy 13 Division of Infectious Diseases, Ospedali Riuniti, Bergamo, Italy 14 Institute of Microbiology and Biomedical Sciences, Marche Polytechnical University, Ancona, Italy
Received 16 April 2009; returned 1 June 2009; revised 15 June 2009; accepted 21 June 2009
* Corresponding author. Section of Microbiology, Department of Molecular Biology, University of Siena, Policlinico S. Maria alle Scotte, Viale Bracci 16, I-53100 Siena, Italy. Tel: +39-0577-233863; Fax: +39-0577-233870; E-mail: zazzi{at}unisi.it
Objectives: To test retrospectively the ability of four freely available rules-based expert systems to predict short- and medium-term virological outcome following an antiretroviral treatment switch in pre-treated HIV-1 patients.
Methods: The HIV-1 genotype interpretation systems (GISs) HIVdb, ANRS, Rega and AntiRetroScan were tested for their accuracy in predicting response to highly active antiretroviral therapy using 8 week (n = 765) and 24 week (n = 634) follow-up standardized treatment change episodes extracted from the Italian Antiretroviral Resistance Cohort Analysis (ARCA) database. A genotypic sensitivity score (GSS) was derived for each genotype–treatment pair for the different GISs and tested as a predictor of virological treatment outcome by univariable and multivariable logistic regression as well as by receiver operating characteristic curve analysis. The two systems implementing drug potency weights (AntiRetroScan and Rega) were evaluated with and without this correction factor.
Results: All four GSSs were strong predictors of virological treatment outcome at both 8 and 24 weeks after adjusting for baseline viro-immunological parameters and previous drug exposure (odds ratios ranging from 2.04 to 2.43 per 1 unit GSS increase; P < 0.001 for all the systems). The accuracy of AntiRetroScan and Rega was significantly increased by drug potency weighting with respect to the unweighted versions (P 
0.001). HIVdb and ANRS also increased their performance with the same drug potency weighting adopted by AntiRetroScan and Rega, respectively (P < 0.001 for both analyses).
Conclusions: Currently available GISs are valuable tools for assisting antiretroviral treatment choices. Drug potency weighting can increase the accuracy of all systems.
Keywords: genotype , drug resistance , algorithm
Members are listed in the Acknowledgements section.