Clinical Evaluation of the Potential Utility of Computational Modeling as an HIV Treatment Selection Tool by Physicians with Considerable HIV Experience

Abstract

The HIV Resistance Response Database Initiative (RDI), which comprises a small research team in the United Kingdom and collaborating clinical centers in more than 15 countries, has used antiretroviral treatment and response data from thousands of patients around the world to develop computational models that are highly predictive of virologic response. The potential utility of such models as a tool for assisting treatment selection was assessed in two clinical pilot studies: a prospective study in Canada and Italy, which was terminated early because of the availability of new drugs not covered by the system, and a retrospective study in the United States. For these studies, a Web-based user interface was constructed to provide access to the models. Participating physicians entered baseline data for cases of treatment failure and then registered their treatment intention. They then received a report listing the five alternative regimens that the models predicted would be most effective plus their own selection, ranked in order of predicted virologic response. The physicians then entered their final treatment decision. Twenty-three physicians entered 114 cases (75 unique cases with 39 entered twice by different physicians). Overall, 33% of treatment decisions were changed following review of the report. The final treatment decisions and the best of the RDI alternatives were predicted to produce greater virologic responses and involve fewer drugs than the original selections. Most physicians found the system easy to use and understand. All but one indicated they would use the system if it were available, particularly for highly treatment-experienced cases with challenging resistance profiles. Despite limitations, the first clinical evaluation of this approach by physicians with substantial HIV-experience suggests that it has the potential to deliver clinical and economic benefits.

Bioinformatical Assistance of Selecting Anti-HIV Therapies: Where Do We Stand?

“…By assessing a whole suite of candidate therapies at a time, such a system can rank these with respect to their estimated probability of success and suggest highly promising drug combinations. The first such system that became generally available was THEO (Therapy Optimization) from the geno2pheno suite [15,16], followed by the EuResist interpretation engine [17] and, more recently, by an offer from the HIV Resistance Response Database Initiative [18]. …”

Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa

“…The application of the models as a treatment decision-making aid has been assessed in prospective clinical pilot studies involving highly experienced HIV physicians in well-resourced settings and found to be a useful clinical tool. 9 The RDI models are made freely available as a treatment-support tool, the HIV Treatment Response Prediction System (HIV-TRePS), via http://www.hivrdi.org .…”

Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting

“…The models have been used to power a free experimental web-based HIV treatment response prediction system (HIV-TRePS) assessed by experienced HIV physicians in two clinical pilot studies as a useful aid to clinical practice [12]. …”