Computational Models as Predictors of HIV Treatment Outcomes for the Phidisa Cohort in South Africa

Background: Selecting the optimal combination of HIV drugs for an individual in resourcelimited settings is challenging because of the limited availability of drugs and genotyping.Objective: The evaluation as a potential treatment support tool of computational models that predict response to therapy without a genotype; using cases from the Phidisa cohort in South Africa.Methods: Cases from Phidisa of treatment change following failure were identified that had the following data available: baseline CD4 count and viral load; details of failing and previous antiretroviral drugs; drugs in new regimen and time to follow-up. The HIV Resistance Response Database Initiative's (RDI's) models used these data to predict the probability of a viral load 50 copies/mL at follow-up. The models were also used to identify effective alternative combinations of three locally available drugs.Results: The models achieved accuracy (area under the receiver-operator characteristic curve) of 0.72 when predicting response to therapy; which is less accurate than for an independent global test set (0.80) but at least comparable to that of genotyping with rules-based interpretation. The models were able to identify alternative locally available three-drug regimens that were predicted to be effective in 69% of all cases and 62% of those whose new treatment failed in the clinic.Conclusion: The predictive accuracy of the models for these South African patients together with the results of previous studies suggest that the RDI's models have the potential to optimise treatment selection and reduce virological failure in different patient populations; without the use of a genotype

Extent to which low-level use of antiretroviral treatment could cure the AIDS epidemic in sub-Saharan Africa

Despite growing international pressure to provide HIV-1 treatment to less-developed countries; potential demographic and epidemiological impacts have yet to be characterised. We modelled the future impact of antiretroviral use in south Africa from 2000 to 2005. methods: We produced a population projection model that assumed zero antiretroviral use to estimate the future demographic impacts of the HIV-1 epidemic. We also constructed four antiretroviral-adjusted scenarios to estimate the potential effect of antiretroviral use. We modelled total drug cost; cost per life-year gained; and the proportion of pe-person health-care expenditure required to finance antiretroviral treatment in each scenario. Findings: With no antiretroviral use between 2000 and 2005; there will be about 276000 cumulative HIV-1-positive births; 2;302;000 cumulative new AIDS cases; and the life expectancy at birth will be 46.6 years by 2005. By contrast; 110;000 HIV-1-positive births could be prevented by short ourse antiretroviral prophylaxis; as well as a decline of up to 1 year of life expectancy. The direct drug costs of universal coverage for this intervention would be US$54 million - less than 0.001of the per-person health-care expenditure. In comparison; triple-combination treatment for 25of the HIV