Predictive value of HIV-1 genotypic resistance test interpretation algorithms
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Updated: Jun 22, 2026
An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings
Published on: March 30, 2014
Soo-Yon Rhee1, W Jeffrey Fessel, Tommy F Liu
1Division of Infectious Diseases, Dept. of Medicine, Stanford University, Stanford, CA 94305, USA. syrhee@stanford.edu
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
Unweighted and weighted rGSSs of 4 genotypic resistance algorithms were the strongest independent predictors of VR. Optimizing ARV weighting may further improve VR predictions.
