Predicting a single HIV drug resistance measure from three international interpretation gold standards

Abstract

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%.

Conclusion: From the result obtained it is evident that combining the gold standard interpretation algorithms may increase the predictive ability of the individual interpretation algorithms.