title: The use of computational models to predict response to HIV therapy for clinical cases in Romania.
abstract: BACKGROUND
  A major challenge in Romania is the optimisation of antiretroviral therapy for the many HIV-infected adults with, on average, a decade of treatment experience. The RDI has developed computational models that predict virological response to therapy but these require a genotype, which is not routinely available in Romania. Moreover the models, which were trained without any Romanian data, have proved most accurate for patients from the healthcare settings that contributed the training data. Here we develop and test a novel model that does not require a genotype, with test data from Romania.
  
  
  METHODS
  A random forest (RF) model was developed to predict the probability of the HIV viral load (VL) being reduced to <50 copies/ml following therapy change. The input variables were baseline VL, CD4 count, treatment history and time to follow-up. The model was developed with 3188 treatment changes episodes (TCEs) from North America, Western Europe and Australia. The model's predictions for 100 independent TCEs from the RDI database were compared to those of a model trained with the same data plus genotypes and then tested using 39 TCEs from Romania in terms of the area under the ROC curve (AUC).
  
  
  RESULTS
  When tested with the 100 independent RDI TCEs, the AUC values for the models with and without genotypes were 0.88 and 0.86 respectively. For the 39 Romanian TCEs the AUC was 0.60. However, when 14 cases with viral loads that may have been between 50 and 400 copies were removed, the AUC increased to 0.83.
  
  
  CONCLUSIONS
  Despite having been trained without data from Romania, the model predicted treatment responses in treatment-experienced Romanian patients with clade F virus accurately without the need for a genotype. The results suggest that this approach might be generalisable and useful in helping design optimal salvage regimens for treatment-experienced patients in countries with limited resources where genotyping is not always available.
authors.affiliation: PhD, RDI, London, UK.
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Revell
authors.affiliation: MD, 'Dr. Victor Babeş' Hospital for Infectious and Tropical Diseases, Bucharest, Romania.
authors.forename: Luminiţa
authors.initials: L
authors.lastname: Ene
authors.affiliation: MD, PhD, 'Dr. Victor Babeş' Hospital for Infectious and Tropical Diseases, Bucharest, Romania.
authors.forename: Dan
authors.initials: D
authors.lastname: Duiculescu
authors.affiliation: PhD, RDI, London, UK.
authors.forename: Dechao
authors.initials: D
authors.lastname: Wang
authors.affiliation: MD, HIV Training and Resource Initiative, London, UK.
authors.forename: Mike
authors.initials: M
authors.lastname: Youle
authors.affiliation: MS, Chelsea and Westminster Hospital, London, UK.
authors.forename: Anton
authors.initials: A
authors.lastname: Pozniak
authors.affiliation: MD, BC Centre for Excellence in HIV and AIDS, Vancouver, Canada.
authors.forename: Julio
authors.initials: J
authors.lastname: Montaner
authors.affiliation: PhD, RDI, London, UK.
authors.forename: Brendan A
authors.initials: BA
authors.lastname: Larder
keywords: HIV; computational models; treatment response prediction