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Predicting the Response to Combination Antiretroviral Therapy: Retrospective Validation of geno2pheno-THEO on a Large Clinical Database
André Altmann, Martin Däumer, Niko Beerenwinkel, Yardena Peres, Eugen Schülter, Joachim Büch, Soo-Yon Rhee, Anders Sönnerborg, W. Jeffrey Fessel, Robert W. Shafer, Maurizio Zazzi, Rolf Kaiser and Thomas Lengauer
The Journal of Infectious Diseases
Vol. 199, No. 7 (Apr. 1, 2009), pp. 999-1006
Published by: Oxford University Press
Stable URL: http://www.jstor.org/stable/40254537
Page Count: 8
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Background. Expert-based genotypic interpretation systems are standard methods for guiding treatment selection for patients infected with human immunodeficiency virus type 1. We previously introduced the software pipeline geno2pheno-THEO (g2p-THEO), which on the basis of viral sequence predicts the response to treatment with a combination of antiretroviral compounds by applying methods from statistical learning and the estimated potential of the virus to escape from drug pressure. Methods. We retrospectively validated the statistical model used by g2p-THEO in ~ 7600 independent treatment-sequence pairs extracted from the EuResist integrated database, ranging from 1990 to 2007. Results were compared with the 3 most widely used expert-based interpretation systems: Stanford HIVdb, ANRS, and Rega. Results. The difference in receiver operating characteristic curves between g2p-THEO and expert-based approaches was significant (P < .001; paired Wilcoxon test). Indeed, at 80% specificity, g2p-THEO found 16.2%–19.8% more successful regimens than did the expert-based approaches. The increased performance of g2p-THEO was confirmed in a 2001-2007 data set from which most obsolete therapies had been removed. Conclusion. Finding drug combinations that increase the chances of therapeutic success is the main reason for using decision support systems. The present analysis of a large data set derived from clinical practice demonstrates that g2p-THEO solves this task significantly better than state-of-the-art expert-based systems. The tool is available at http://www. geno2pheno. org.
The Journal of Infectious Diseases © 2009 Oxford University Press