Predictive value of HIV-1 genotypic resistance test interpretation algorithms.
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.
Rhee
Soo-Yon
SY
Division of Infectious Diseases, Dept. of Medicine, Stanford University, Stanford, CA 94305, USA. syrhee@stanford.edu
Fessel
W Jeffrey
WJ
Liu
Tommy F
TF
Marlowe
Natalia M
NM
Rowland
Charles M
CM
Rode
Richard A
RA
Vandamme
Anne-Mieke
AM
Van Laethem
Kristel
K
Brun-Vezinet
Françoise
F
Calvez
Vincent
V
Taylor
Jonathan
J
Hurley
Leo
L
Horberg
Michael
M
Shafer
Robert W
RW
D000328:Adult; D000368:Aged; D000465:Algorithms; D019380:Anti-HIV Agents / Q000145:classification / Q000494:pharmacology* / Q000627:therapeutic use; D003627:Data Interpretation, Statistical; D024882:Drug Resistance, Viral / Q000235:genetics*; D005260:Female; D005838:Genotype; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008969:Molecular Sequence Data; D011237:Predictive Value of Tests; D055815:Young Adult
D019380:Anti-HIV Agents