Prediction of response to antiretroviral therapy by human experts and by the EuResist data‐driven expert system (the EVE study) |
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| Authors: | M Zazzi R Kaiser A Sönnerborg D Struck A Altmann M Prosperi M Rosen‐Zvi A Petroczi Y Peres E Schülter CA Boucher F Brun‐Vezinet PR Harrigan L Morris M Obermeier C‐F Perno P Phanuphak D Pillay RW Shafer A‐M Vandamme K van Laethem AMJ Wensing T Lengauer F Incardona |
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| Affiliation: | 1. Department of Molecular Biology, University of Siena, Siena, Italy;2. Institute of Virology, University of Cologne, Cologne, Germany;3. Department of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden;4. CRP‐Santé, Laboratory of Retrovirology, Luxembourg, Luxembourg;5. Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany;6. Clinic of Infectious Diseases, Catholic University of Rome, Rome, Italy;7. Machine Learning and Data Mining group, IBM Research Labs, Haifa, Israel;8. School of Life Sciences, Kingston University, Kingston upon Thames, UK;9. Knowledge Management Group, IBM Research Labs, Haifa, Israel;10. Department of Virology, Erasmus Medical Center, Erasmus University, Rotterdam, The Netherlands;11. Laboratoire de Virologie, H?pital Bichat Claude Bernard, Paris, France;12. BC Centre for Excellence in HIV/AIDS, University of British Columbia, Vancouver, Canada;13. AIDS Virus Research Unit, National Institute for Communicable Diseases, Johannesburg, South Africa;14. Department of Virology, Ludwig‐Maximilians‐University Munich, Munich, Germany;15. Department of Experimental Medicine, University of Rome Tor Vergata, Rome, Italy;16. HIV‐NAT/Thai Red Cross AIDS Research Centre, Bangkok, Thailand;17. Department of Infection and Immunity, University College London, London, UK;18. Division of Infectious Diseases, Department of Medicine, Stanford University, Stanford, CA, USA;19. Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium;20. Department of Medical Microbiology, University Medical Center Utrecht, Utrecht, The Netherlands;21. R&D, Informa S.R.L., Rome, Italy |
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| Abstract: | ![]()
Objectives The EuResist expert system is a novel data‐driven online system for computing the probability of 8‐week success for any given pair of HIV‐1 genotype and combination antiretroviral therapy regimen plus optional patient information. The objective of this study was to compare the EuResist system vs. human experts (EVE) for the ability to predict response to treatment. Methods The EuResist system was compared with 10 HIV‐1 drug resistance experts for the ability to predict 8‐week response to 25 treatment cases derived from the EuResist database validation data set. All current and past patient data were made available to simulate clinical practice. The experts were asked to provide a qualitative and quantitative estimate of the probability of treatment success. Results There were 15 treatment successes and 10 treatment failures. In the classification task, the number of mislabelled cases was six for EuResist and 6–13 for the human experts [mean±standard deviation (SD) 9.1±1.9]. The accuracy of EuResist was higher than the average for the experts (0.76 vs. 0.64, respectively). The quantitative estimates computed by EuResist were significantly correlated (Pearson r=0.695, P<0.0001) with the mean quantitative estimates provided by the experts. However, the agreement among experts was only moderate (for the classification task, inter‐rater κ=0.355; for the quantitative estimation, mean±SD coefficient of variation=55.9±22.4%). Conclusions With this limited data set, the EuResist engine performed comparably to or better than human experts. The system warrants further investigation as a treatment‐decision support tool in clinical practice. |
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| Keywords: | antiretroviral therapy drug resistance genotype HIV type 1 prediction systems |
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