Prediction of Virologic Outcome of Salvage Antiretroviral Treatment by Different Systems for Interpreting Genotypic HIV Drug Resistance

The authors assessed the predictive capacity of 3 rule-based algorithms (Bergamo, Stanford University, Rega Institute) for HIV genotypic interpretation. A total of 1132 postgenotypic regimens in 533 patients were considered. The genotypic sensitivity score (GSS) was strongly associated (P < .0001) with the virologic outcome (1 log HIV-RNA reduction). The 3 algorithms had a highly significant prediction efficiency. The Bergamo algorithm receiver-operating characteristic curve area under the curve (AUC) for the prediction of ≥1 log HIV-RNA reduction was 0.753 (95% confidence interval, 0.725-0.781), testifying that the prediction was significantly different (P < .0001) from simple chance. The AUCs obtained by the 2 other systems were similar (0.752 Stanford; 0.741 Rega). The predictive capacity of the algorithms was not influenced by the type of antiviral drugs used. The 3 considered rule-based algorithms for the interpretation of HIV genotypic resistance yield congruent results and may effectively predict the virologic outcome of rescue therapy. Their use may help clinicians in interpreting mutational patterns and in making therapeutic choices.

[1]  A. Blaxhult,et al.  Concomitant Use of an Active Boosted Protease Inhibitor with Enfuvirtide in Treatment-Experienced, HIV-Infected Individuals: Recent Data and Consensus Recommendations , 2006, HIV clinical trials.

[2]  J. Montaner,et al.  Durable Efficacy of Enfuvirtide Over 48 Weeks in Heavily Treatment-Experienced HIV-1-Infected Patients in the T-20 Versus Optimized Background Regimen Only 1 and 2 Clinical Trials , 2005, Journal of acquired immune deficiency syndromes.

[3]  F. Chiodo,et al.  Variability in the Interpretation of Transmitted Genotypic HIV-1 Drug Resistance and Prediction of Virological Outcomes of the Initial Haart by Distinct Systems , 2004, Antiviral therapy.

[4]  C. Tinelli,et al.  Comparison between rules-based human immunodeficiency virus type 1 genotype interpretations and real or virtual phenotype: concordance analysis and correlation with clinical outcome in heavily treated patients. , 2003, The Journal of infectious diseases.

[5]  P. Narciso,et al.  Variable prediction of antiretroviral treatment outcome by different systems for interpreting genotypic human immunodeficiency virus type 1 drug resistance. , 2003, Journal of Infectious Diseases.

[6]  J. Montaner,et al.  Discrepant results in the interpretation of HIV‐1 drug‐resistance genotypic data among widely used algorithms , 2003, HIV medicine.

[7]  F. Brun-Vézinet,et al.  Predictors of the Virological Response to a Change in the Antiretroviral Treatment Regimen in HIV-1-Infected Patients Enrolled in a Randomized Trial Comparing Genotyping, Phenotyping and Standard of Care (Narval Trial, Anrs 088) , 2002, Antiviral therapy.

[8]  B. Schmidt,et al.  Genotypic drug resistance interpretation systems--the cutting edge of antiretroviral therapy. , 2002, AIDS reviews.

[9]  S. Staszewski,et al.  Comparison of Nine Resistance Interpretation Systems for HIV-1 Genotyping , 2002, Antiviral therapy.

[10]  S. Hammer,et al.  HIV-1 genotype and phenotype correlate with virological response to abacavir, amprenavir and efavirenz in treatment-experienced patients , 2002, AIDS.

[11]  A. Vandamme,et al.  A Genotypic Drug Resistance Interpretation Algorithm that Significantly Predicts Therapy Response in HIV-1-Infected Patients , 2001, Antiviral therapy.

[12]  V. Arendt,et al.  Genotypic Correlates of Resistance to HIV-1 Protease Inhibitors on Longitudinal Data: The Role of Secondary Mutations , 2000, Antiviral therapy.

[13]  F. Hecht,et al.  Adherence to protease inhibitors, HIV-1 viral load, and development of drug resistance in an indigent population , 2000, AIDS.

[14]  T. Perneger,et al.  Impact of drug resistance mutations on virologic response to salvage therapy. Swiss HIV Cohort Study. , 1999, AIDS.

[15]  D G Seymour,et al.  Making better decisions: construction of clinical scoring systems by the Spiegelhalter-Knill-Jones approach. , 1990, BMJ.

[16]  F. Maggiolo,et al.  Similar adherence rates favor different virologic outcomes for patients treated with nonnucleoside analogues or protease inhibitors. , 2005, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.