Prediction of response to antiretroviral therapy by human experts and by the EuResist data-driven expert system (the EVE study).
OBJECTIVE
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.
Zazzi
M
M
Department of Molecular Biology, University of Siena, Siena, Italy. zazzi@unisi.it
Kaiser
R
R
Sönnerborg
A
A
Struck
D
D
Altmann
A
A
Prosperi
M
M
Rosen-Zvi
M
M
Petroczi
A
A
Peres
Y
Y
Schülter
E
E
Boucher
C A
CA
Brun-Vezinet
F
F
Harrigan
P R
PR
Morris
L
L
Obermeier
M
M
Perno
C-F
CF
Phanuphak
P
P
Pillay
D
D
Shafer
R W
RW
Vandamme
A-M
AM
van Laethem
K
K
Wensing
A M J
AM
Lengauer
T
T
Incardona
F
F
D016208:Databases, Factual; D005103:Expert Systems; D005260:Female; D015658:HIV Infections / Q000188:drug therapy* / Q000235:genetics / Q000821:virology; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics; D006801:Humans; D008297:Male; D011336:Probability; D016896:Treatment Outcome; D019562:Viral Load