Predictive Value of HIV-1 Genotypic Resistance Test Interpretation Algorithms JOURNAL OF INFECTIOUS DISEASES Rhee, S., Fessel, W. J., Liu, T. F., Marlowe, N. M., Rowland, C. M., Rode, R. A., Vandamme, A., Van Laethem, K., Brun-Vezinet, F., Calvez, V., Taylor, J., Hurley, L., Horberg, M., Shafer, R. W. 2009; 200 (3): 453-463


Interpreting human immunodeficiency virus type 1 (HIV-1) genotypic drug-resistance test results is challenging for clinicians treating HIV-1-infected patients. Multiple drug-resistance interpretation algorithms have been developed, but their predictive value has rarely been evaluated using contemporary clinical data sets.We examined the predictive value of 4 algorithms at predicting virologic response (VR) during 734 treatment-change episodes (TCEs). VR was defined as attaining plasma HIV-1 RNA levels below the limit of quantification. Drug-specific genotypic susceptibility scores (GSSs) were calculated by applying each algorithm to the baseline genotype. Weighted GSSs were calculated by multiplying drug-specific GSSs by antiretroviral (ARV) potency factors. Regimen-specific GSSs (rGSSs) were calculated by adding unweighted or weighted drug-specific GSSs for each salvage therapy ARV. The predictive value of rGSSs were estimated by use of multivariate logistic regression.Of 734 TCEs, 475 (65%) were associated with VR. The rGSSs for the 4 algorithms were the variables most strongly predictive of VR. The adjusted rGSS odds ratios ranged from 1.6 to 2.2 (P < .001). Using 10-fold cross-validation, the averaged area under the receiver operating characteristic curve for all algorithms increased from 0.76 with unweighted rGSSs to 0.80 with weighted rGSSs.Unweighted and weighted rGSSs of 4 genotypic resistance algorithms were the strongest independent predictors of VR. Optimizing ARV weighting may further improve VR predictions.

View details for DOI 10.1086/600073

View details for Web of Science ID 000267604000018

View details for PubMedID 19552527