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 pretreated 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 (n5765) and 24 week (n5634) 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.
|Titolo:||Rules-based HIV-1 genotypic resistance interpretation systems predict 8 week and 24 week virological antiretroviral treatment outcome and benefit from drug potency weighting|
|Parole Chiave:||Algorithm; Drug resistance; Genotype|
|Settore Scientifico Disciplinare:||Settore MED/17 - Malattie Infettive|
Settore MED/07 - Microbiologia e Microbiologia Clinica
|Data di pubblicazione:||2009|
|Digital Object Identifier (DOI):||10.1093/jac/dkp252|
|Appare nelle tipologie:||01 - Articolo su periodico|