Poster presentation at:
2nd IAS Conference on HIV Pathogenesis and Treatment
13th July 2003 - 16th July 2003
Paris, France
Larder BA1, Wang D1, Revell AD2 and Lane C3.
1: The HIV Resistance Response Database Initiative (RDI), Cambridge, UK; 2: RDI, London, UK; 3: National Institute of Allergy and Infectious Diseases, Bethesda, USA.
Background The RDI has developed Neural Network (NN) models which, when used in conjunction with the RDI clinical database, have demonstrated accuracy in predicting the trajectory and magnitude of virological response to antiretroviral therapy from genotype. Here we examine the potential of NN models to identify potentially effective alternative treatment regimens for 'salvage' patients, experiencing continued treatment failure despite having their treatment changed according to current clinical practice.
Methods Data from 511 patients from two well-characterized clinical cohorts were analyzed. All had multiple genotypes and irregular VL measurements. 747 'treatment change episodes' (TCEs) were obtained: episodes with a genotype 12 weeks and a VL 8 before treatment change and follow-up VL within 4-40 weeks. Four NN models were trained (using 690 randomly selected TCEs) and evaluated using the remaining 57 TCEs, as independent test sets. 108 salvage failure TCEs were then identified (baseline VL of 4 logs and an increase in VL following treatment change). The best NN model was then used to predict virological response to 37 common treatment regimens for each case (28,386 individual virtual response predictions).
Results The correlation between the predicted and actual VL change for the four VL models gave a mean value of 0.6 and mean correct trajectory prediction rate of 80.7%. The best model ( =0.76) identified one or more regimens that were predicted would reduce VL in all 108 salvage failure cases. The median actual change in viral load for these cases was +0.41 logs whereas the median change using the best regimens from the NN predictions was -2.86 logs.
Conclusions NN modeling identified potentially beneficial treatment regimens in all patients who experienced treatment failure, predicting substantial reductions in viral load. This demonstrates the potential utility of this approach in supporting treatment decision making in salvage therapy.
