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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
Prediction of response to antiretroviral therapy by human experts and by the EuResist data-driven expert system (the EVE study)
HIV Medicine, Volume 12, No. 4, Year 2011
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Description
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. © 2010 British HIV Association.
Authors & Co-Authors
Zazzi, Maurizio
Italy, Siena
Università Degli Studi Di Siena
Kaiser, Rolf
Germany, Koln
Universität zu Köln
Sönnerborg, Anders B.
Sweden, Stockholm
Karolinska Institutet
Struck, Daniel
Luxembourg, Strassen
Luxembourg Institute of Health
Altmann, A.
Germany, Saarbrucken
Max Planck Institute for Informatics
Prosperi, Mattia C.F.
Italy, Rome
Università Cattolica Del Sacro Cuore, Campus Di Roma
Rosen-Zvi, M.
Israel, Haifa
Ibm Research - Haifa
Petróczi, Andrea
United Kingdom, Kingston Upon Thames
Kingston University
Peres, Y.
Israel, Haifa
Ibm Research - Haifa
Schülter, E.
Germany, Koln
Universität zu Köln
Boucher, C. A.B.
Netherlands, Rotterdam
Erasmus Mc
Brun-Vézinet, Françoise
France, Paris
Hôpital Bichat-claude-bernard Ap-hp
Harrigan, P. Richard
Canada, Vancouver
The University of British Columbia
Morris, Lynn
South Africa, Johannesburg
National Institute for Communicable Diseases
Obermeier, Martin
Germany, Munich
Ludwig-maximilians-universität München
Perno, Carlo Federico
Italy, Rome
Università Degli Studi Di Roma Tor Vergata
Phanuphak, Praphan
Thailand, Bangkok
Thai Red Cross Agency
Morris, Lynn G.
United Kingdom, London
University College London
Shafer, Robert William
United States, Stanford
Stanford University School of Medicine
Vandamme, Anne Mieke
Belgium, Leuven
Rega Institute for Medical Research
van Laethem, Kristel V.
Belgium, Leuven
Rega Institute for Medical Research
Wensing, Am M.J.
Netherlands, Utrecht
University Medical Center Utrecht
Lengauer, Thomas
Germany, Saarbrucken
Max Planck Institute for Informatics
Incardona, Francesca
Italy, Rome
Informa S.r.l.
Statistics
Citations: 50
Authors: 24
Affiliations: 20
Identifiers
Doi:
10.1111/j.1468-1293.2010.00871.x
ISSN:
14642662
e-ISSN:
14681293
Research Areas
Genetics And Genomics
Health System And Policy
Infectious Diseases
Study Approach
Qualitative
Quantitative