Predicting response to antiretroviral therapy without a genotype: a treatment tool for resource-limited settings

Poster presentation at:

International AIDS Society Conference (IAS)

17th July 2011 - 20th July 2011

Rome, Italy

Larder BA1, Revell AD1, Wang D1, Ene L2, Tempelman H3, Barth RE4, Wensing AM4, Gazzard B5, DeWolf F6, Lane HC7, Montaner JSS8.

The RDI has pioneered the development of computational models that predict response to antiretroviral therapy from baseline data including genotype, typically with 80% accuracy, which are now available online. Genotyping is not generally affordable in resource-limited settings and here we describe the development of models that do not require a genotype, for use in such settings.

Treatment change episodes (TCEs) from >15 countries from the RDI database were partitioned at random into a training set of 14,964 and a test set of 800. A committee of 10 random forest models was developed to predict the probability of follow-up viral load a??400 copies using 10x leave-n out cross-validation (CV). The input variables were baseline viral load, CD4 count, treatment history, drugs in the new regimen and time to follow-up. Receiver-operator characteristic (ROC) curves were plotted during cross validation, with the 800 test set and with test sets from Romania (n=39) and South Africa (n=56). A subset of 57 TCEs from the 800 test set that had genotypes available were also tested with the RDIaEUR(TM)s models that include a genotype.

The mean area under the curve (AUC) and overall accuracy (OA) were 0.77 and 72% during CV and 0.77 and 71% with the test dataset. With the Romanian test set the AUC and OA were 0.68 and 67% and with the South African test set 0.69 and 68%. The RDI genotype models achieved an AUC and OA of 0.77 and 74% with the 57 subset, compared to 0.76 and 68% for the aEUR~no-genotypeaEUR(TM) models.

The RF modelsaEUR(TM) predictions were only slightly less accurate than models that include the genotype. They performed well with data from resource-limited clinics not represented in the training dataset suggesting they are generalisable. The models are now available via the RDIaEUR(TM)s online treatment selection tool HIV-TRePS.

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