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.