United Kingdom
Research Article
Modelling Treatment Response Could Reduce Virological Failure in Different Patient Populations
Author(s): Andrew D Revell, Dechao Wang, Gabriella dââ¬â¢Ettorre, Frank DE Wolf, Brian Gazzard, Giancarlo Ceccarelli, Jose Gatell, María Jésus Pérez-elías, Vincenzo Vullo, Julio S Montaner, H Clifford Lane and Brendan A LarderAndrew D Revell, Dechao Wang, Gabriella d’Ettorre, Frank DE Wolf, Brian Gazzard, Giancarlo Ceccarelli, Jose Gatell, María Jésus Pérez-elías, Vincenzo Vullo, Julio S Montaner, H Clifford Lane and Brendan A Larder
Background: HIV drug resistance can cause viral re-bound in patients on combination antiretroviral therapy, requiring a change in therapy to re-establish virological control. The RDI has developed computational models that predict response to combination therapy based on the viral genotype, viral load, CD4 count and treatment history. Here we compare two sets of models developed with different levels of treatment history information and test their generalisability to new patient populations.
Methods: Two sets of five random forest models were trained to predict the probability of virological response (follow-up viral load <50 copies/ml viral RNA) following a change in antiretroviral therapy using the baseline viral load, CD4 count, genotype and treatment history from 7,263 treatment change episodes. One set used six treatment history variables and the other 18 - one for eac.. Read More»
DOI:
10.4172/2155-6113.S5-008
Journal of AIDS & Clinical Research received 5264 citations as per Google Scholar report