Addressing adherence improves the accuracy of neural networks' predictions of virologic treatment response

Poster presentation at:

3rd IAS Conference on HIV Pathogenesis and Treatment

24th July 2005 - 27th July 2005

Rio de Janeiro, Brazil

Montaner J1 , Larder BA2 , Wang D2 , Revell AD2 , Wegner S3 , Harrigan R1 and Lane C4.

1: The BC Centre for Excellence in HIV/AIDS Vancouver, BC, Canada (BC-CfE); 2: The HIV Resistance Response Database Initiative (RDI), London, UK; 3: US Military HIV Research Program, Rockville, MD, USA; 4: National Institute of Allergy and Infectious Diseases (NIAID), Bethesda, MD, USA.

Objectives: A shortcoming of genotypic resistance tests is their limited sensitivity for detection of minority populations of resistant virus. Artificial Neural Networks (ANN) can successfully predict virological response to combination therapy from genotype but their accuracy may be limited by this shortcoming. This study was designed to address whether inclusion of treatment history data, as a 'surrogate' for minority mutant populations, can increase the accuracy of ANN in predicting response to combination therapy.

Methods Two 'committees' of 10 ANN were trained using 2,559 TCEs. The 'basic' committee was trained using input variables of genotype, baseline viral load (VL), drugs in the new combination regimen, time to follow-up and the output variable of VL response. The 'treatment history' models were trained with these variables plus four additional treatment history input variables: AZT, 3TC, any NNRTI and any PI. The two committees were tested using 51 TCEs from 23 patients, partitioned at random as an independent test set . During testing the ANN models were given the input variables of the test TCEs and required to predict the VL response. The models' performance was assessed in terms of the 'committee average' (the average VL prediction of all 10 models for each test TCE).

Results Correlations between predicted and actual VL change for the basic and treatment history committees gave r@ values of 0.30 and 0.45 respectively (p<0.01). The mean absolute difference between predicted and actual VL response was 0.88 and 0.78 respectively (p=0.05).

Conclusions The addition of treatment history data significantly improved the accuracy of ANN confirming that historical exposure to antiretroviral drugs can influence response to a new regimen. This suggests that treatment history may act as a surrogate for minority mutant populations and can enable ANN to overcome this shortcoming of current genotyping.

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