Abstract
BackgroundSelecting the optimal combination of HIV drugs for an individual in resource-limited settings is challenging because of the limited availability of drugs and genotyping.ObjectiveThe 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.MethodsCases 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.ResultsThe 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.ConclusionThe 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.
Highlights
The models consistently achieve accuracy in the region of 75%–80% in their predictions of virological response to therapy
The original test data set of 1000 TCEs had a median baseline viral load of 3.97 and the 100 southern African TCEs amongst them had a median viral load of 4.32
We challenged the Response Database Initiative (RDI) models that do not require a genotype to predict virological response for patients in the Phidisa military cohort in South Africa, most of whom were moving from first- to second-line therapy
Summary
The selection of a new combination of antiretroviral drugs when therapy fails in well-resourced countries is made on an individual basis using an extensive range of information that is at the physician’s disposal, usually including viral load values, CD4 counts, treatment history and, of particular relevance in the salvage situation, a viral genotype.[1,2] genotyping with interpretation by one of the many rules-based interpretation systems that are in widespread use is regarded by many as a foundation stone of individualised antiretroviral therapy, and has been demonstrated to be moderately predictive of virological response.[3,4] Selecting the best combination of antiretrovirals in resource-limited settings with a limited range of drugs available and where a lack of funds, infrastructure and technical expertise make genotyping impractical, can be much more challenging.In response to this challenge, the HIV Resistance Response Database Initiative (RDI) has developed computational models to assist in the selection of the most effective combinations of drugs from those available.[5,6] The models are able to predict accurately virological response to combination antiretroviral therapy, with or without genotypic information, the latter basing their predictions on viral loads, CD4 counts, treatment history and time to follow-up.[7,8]The RDI models are trained using longitudinal data from clinical cases where the HIV treatment has been changed and followed up. The selection of a new combination of antiretroviral drugs when therapy fails in well-resourced countries is made on an individual basis using an extensive range of information that is at the physician’s disposal, usually including viral load values, CD4 counts, treatment history and, of particular relevance in the salvage situation, a viral genotype.[1,2] genotyping with interpretation by one of the many rules-based interpretation systems that are in widespread use is regarded by many as a foundation stone of individualised antiretroviral therapy, and has been demonstrated to be moderately predictive of virological response.[3,4] Selecting the best combination of antiretrovirals in resource-limited settings with a limited range of drugs available and where a lack of funds, infrastructure and technical expertise make genotyping impractical, can be much more challenging. Selecting the optimal combination of HIV drugs for an individual in resourcelimited settings is challenging because of the limited availability of drugs and genotyping
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