ObjectivesThe 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. MethodsThe 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. ResultsThere 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 AE standard deviation (SD) 9.1 AE 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 5 0.695, Po0.0001) with the mean quantitative estimates provided by the experts. However, the agreement among experts was only moderate (for the classification task, inter-rater k 5 0.355; for the quantitative estimation, mean AE SD coefficient of variation 5 55.9 AE 22.4%). ConclusionsWith 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.
In the absence of widespread access to individualized laboratory monitoring, which forms an integral part of HIV patient management in resource-rich settings, the roll-out of highly active antiretroviral therapy (HAART) in resource-limited settings has adopted a public health approach based on standard HAART protocols and clinical/immunological definitions of therapy failure. The cost-effectiveness of HIV-1 viral load monitoring at the individual level in such settings has been debated, and questions remain over the long-term and population-level impact of managing HAART without it. Computational models that accurately predict virological response to HAART using baseline data including CD4 count, viral load and genotypic resistance profile, as developed by the Resistance Database Initiative, have significant potential as an aid to treatment selection and optimization. Recently developed models have shown good predictive performance without the need for genotypic data, with viral load emerging as by far the most important variable. This finding provides further, indirect support for the use of viral load monitoring for the long-term optimization of HAART in resource-limited settings.
7‐11 November 2010, Tenth International Congress on Drug Therapy in HIV Infection, Glasgow, UK
The results of genotypic HIV drug-resistance testing are, typically, 60–65% predictive of response to combination antiretroviral therapy (ART) and have proven valuable for guiding treatment changes. However, genotyping is not available in many resource-limited settings (RLS). The purpose of this study was to develop computational models that can predict response to ART without a genotype and evaluate their potential as a treatment support tool in RLS. Random forest models were trained to predict the probability of response to ART (<400 copies HIV RNA/ml) using the following data from 14,891 cases of ART change following virological failure in well-resourced countries: viral load and CD4 count prior to treatment change, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. The models were assessed during cross-validation, with an independent set of 800 cases, with 231 cases from RLS in Southern Africa, 206 from India and 375 from Romania. The area under the ROC curve (AUC) was the main outcome measure of the accuracy of the model's predictions. The models were used to identify alternative regimens for those cases where the salvage regimen initiated in the clinic failed. Finally, annual therapy costs were used to determine the potential cost effectiveness of this strategy for the Indian cases. The models achieved an AUC of 0.74–0.81 during cross validation and 0.76–0.77 with the 800 test TCEs. They achieved an AUC of 0.59–0.65 with cases from Southern Africa, 0.64 for India and 0.73 for Romania. The models identified alternative, locally available drug regimens that were predicted to result in virological response for 97% of cases where the salvage regimen failed in Southern Africa, 98% of those in Romania and 100% in India. Cost-neutral or cost-saving regimens that were predicted to be effective were identified for 88% of the Indian salvage failures with a mean saving of $638 per year. We developed computational models that predict virological response to ART without a genotype with comparable accuracy to genotyping with rules-based interpretation. The models were able to identify alternative regimens that were predicted to be effective for the great majority of cases where the new regimen prescribed in the clinic failed. The models were also able to identify cost-saving alternatives for most cases of failure in India. These models are now freely available over the internet as part of the HIV Treatment Response Predictions System (HIV-TRePS), which has the potential to help optimise antiretroviral therapy in countries with limited resources where genotyping is not generally available
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