Predicting a single HIV drug resistance measure from three international interpretation gold standards.
OBJECTIVE
To investigate the possibility of combining the interpretation of three gold standard interpretation algorithms using weighted heuristics in order to produce a single resistance measure.


METHODS
The outputs of HIVdb, Rega, ANRS were combined to obtain a single resistance profile using the equally weighted voting algorithm, accuracy based weighing voting algorithm and the Bayesian based weighted voting algorithm techniques.


RESULTS
The Bayesian based voting combination increased the accuracy of the resistance profile prediction compared to phenotype, from 58% to 69%. The equal weighted voting algorithm and the accuracy based algorithm both increased the prediction accuracy to 60%.


CONCLUSIONS
From the result obtained it is evident that combining the gold standard interpretation algorithms may increase the predictive ability of the individual interpretation algorithms.
Department of Telehealth, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, 719 Umbilo Road, Umbilo, Durban, South Africa. singhy@ukzn.ac.za
Singh
S
Yashik
Mars
M
Maurice
D000465:Algorithms; D019380:Anti-HIV Agents / Q000627:therapeutic use*; D024882:Drug Resistance, Viral; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy*; D006801:Humans; D010641:Phenotype; D012015:Reference Standards; D013223:Statistics as Topic
D019380:Anti-HIV Agents