OBJECTIVES: The development of a genotypic drug resistance interpretation algorithm, and the evaluation of its power to predict therapy outcome. DESIGN: A rule-based algorithm was established by an individual expert and was based on published and in-house results, independently from the data of the patients used in this evaluation. The predictive value of the algorithm for virological outcomes was retrospectively evaluated using the baseline genotype observed in patients on highly active antiretroviral therapy, failing virologically and subsequently starting a salvage regimen. METHODS: The independent association between the susceptibility score (calculated according to the algorithm) and the virological response at 3 months, was analysed using multivariable logistic regression and multiple linear regression models. RESULTS: In two clinical centres 240 patients were studied. At 3 months 35% had a viral load of <500 RNA copies/ml. Using multivariable logistic regression, the odds ratio of achieving a viral load <500 RNA copies/ml at month 3 per unit increase of susceptibility score was 2.0 (95% CI 1.3-3.1; P=0.002) after adjusting for baseline viral load, genotype-driven salvage therapy, number of new drugs in the regimen, use of a new drug class in the regimen, nelfinavir-containing salvage therapy and history of prior viral load <500 RNA copies/ml. Using multiple linear regression, the susceptibility score showed a significant linear correlation with the log viral load change (slope=-0.27 log10 RNA copies/ml; 95% CI -0.11 to -0.43; P=0.001) after adjusting for history of prior viral load <500 RNA copies/ml, number of new drugs in the salvage therapy, use of a new drug class in the salvage therapy and baseline viral load. CONCLUSIONS: This algorithm proved to be a significant independent predictor of therapy response at 3 months in this cohort of HIV-1-infected patients on salvage therapy. However, it should be subject to regular updates as is needed in this fast developing field.
OBJECTIVES: The development of a genotypic drug resistance interpretation algorithm, and the evaluation of its power to predict therapy outcome. DESIGN: A rule-based algorithm was established by an individual expert and was based on published and in-house results, independently from the data of the patients used in this evaluation. The predictive value of the algorithm for virological outcomes was retrospectively evaluated using the baseline genotype observed in patients on highly active antiretroviral therapy, failing virologically and subsequently starting a salvage regimen. METHODS: The independent association between the susceptibility score (calculated according to the algorithm) and the virological response at 3 months, was analysed using multivariable logistic regression and multiple linear regression models. RESULTS: In two clinical centres 240 patients were studied. At 3 months 35% had a viral load of <500 RNA copies/ml. Using multivariable logistic regression, the odds ratio of achieving a viral load <500 RNA copies/ml at month 3 per unit increase of susceptibility score was 2.0 (95% CI 1.3-3.1; P=0.002) after adjusting for baseline viral load, genotype-driven salvage therapy, number of new drugs in the regimen, use of a new drug class in the regimen, nelfinavir-containing salvage therapy and history of prior viral load <500 RNA copies/ml. Using multiple linear regression, the susceptibility score showed a significant linear correlation with the log viral load change (slope=-0.27 log10 RNA copies/ml; 95% CI -0.11 to -0.43; P=0.001) after adjusting for history of prior viral load <500 RNA copies/ml, number of new drugs in the salvage therapy, use of a new drug class in the salvage therapy and baseline viral load. CONCLUSIONS: This algorithm proved to be a significant independent predictor of therapy response at 3 months in this cohort of HIV-1-infectedpatients on salvage therapy. However, it should be subject to regular updates as is needed in this fast developing field.
Authors: Jaideep Ravela; Bradley J Betts; Francoise Brun-Vézinet; Anne-Mieke Vandamme; Diane Descamps; Kristel van Laethem; Kate Smith; Jonathan M Schapiro; Dean L Winslow; Caroline Reid; Robert W Shafer Journal: J Acquir Immune Defic Syndr Date: 2003-05-01 Impact factor: 3.731
Authors: Robert W Shafer; Soo-Yon Rhee; Deenan Pillay; Veronica Miller; Paul Sandstrom; Jonathan M Schapiro; Daniel R Kuritzkes; Diane Bennett Journal: AIDS Date: 2007-01-11 Impact factor: 4.177
Authors: Joke Snoeck; Rami Kantor; Robert W Shafer; Kristel Van Laethem; Koen Deforche; Ana Patricia Carvalho; Brian Wynhoven; Marcelo A Soares; Patricia Cane; John Clarke; Candice Pillay; Sunee Sirivichayakul; Koya Ariyoshi; Africa Holguin; Hagit Rudich; Rosangela Rodrigues; Maria Belen Bouzas; Françoise Brun-Vézinet; Caroline Reid; Pedro Cahn; Luis Fernando Brigido; Zehava Grossman; Vincent Soriano; Wataru Sugiura; Praphan Phanuphak; Lynn Morris; Jonathan Weber; Deenan Pillay; Amilcar Tanuri; Richard P Harrigan; Ricardo Camacho; Jonathan M Schapiro; David Katzenstein; Anne-Mieke Vandamme Journal: Antimicrob Agents Chemother Date: 2006-02 Impact factor: 5.191
Authors: Rajin Shahriar; Soo-Yon Rhee; Tommy F Liu; W Jeffrey Fessel; Anthony Scarsella; William Towner; Susan P Holmes; Andrew R Zolopa; Robert W Shafer Journal: Antimicrob Agents Chemother Date: 2009-08-31 Impact factor: 5.191