The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool

Revell, Andrew D., Wang, Dechao, Boyd, Mark A., Emery, Sean, Pozniak, Anton L., De Wolf, Frank, Harrigan, Richard, Montaner, Julio S. G., Lane, Clifford and Larder, Brendan A. (2011) The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool. AIDS, 25 15: 1855-1863. doi:10.1097/QAD.0b013e328349a9c2


Author Revell, Andrew D.
Wang, Dechao
Boyd, Mark A.
Emery, Sean
Pozniak, Anton L.
De Wolf, Frank
Harrigan, Richard
Montaner, Julio S. G.
Lane, Clifford
Larder, Brendan A.
Title The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool
Journal name AIDS   Check publisher's open access policy
ISSN 0269-9370
1473-5571
Publication date 2011-09-24
Sub-type Article (original research)
DOI 10.1097/QAD.0b013e328349a9c2
Open Access Status Not yet assessed
Volume 25
Issue 15
Start page 1855
End page 1863
Total pages 9
Place of publication Philadelphia, PA, United States
Publisher Lippincott Williams & Wilkins
Language eng
Formatted abstract
Objective: The optimum selection and sequencing of combination antiretroviral therapy to maintain viral suppression can be challenging. The HIV Resistance Response Database Initiative has pioneered the development of computational models that predict the virological response to drug combinations. Here we describe the development and testing of random forest models to power an online treatment selection tool.

Methods: Five thousand, seven hundred and fifty-two treatment change episodes were selected to train a committee of 10 models to predict the probability of virological response to a new regimen. The input variables were antiretroviral treatment history, baseline CD4 cell count, viral load and genotype, drugs in the new regimen, time from treatment change to follow-up and follow-up viral load values. The models were assessed during cross-validation and with an independent set of 50 treatment change episodes by plotting receiver-operator characteristic curves and their performance compared with genotypic sensitivity scores from rules-based genotype interpretation systems.

Results: The models achieved an area under the curve during cross-validation of 0.77-0.87 (mean=0.82), accuracy of 72-81% (mean=77%), sensitivity of 62-80% (mean=67%) and specificity of 75-89% (mean=81%). When tested with the 50 test cases, the area under the curve was 0.70-0.88, accuracy 64-82%, sensitivity 62-80% and specificity 68-95%. The genotypic sensitivity scores achieved an area under the curve of 0.51-0.52, overall accuracy of 54-56%, sensitivity of 43-64% and specificity of 41-73%.

Conclusion: The models achieved a consistent, high level of accuracy in predicting treatment responses, which was markedly superior to that of genotypic sensitivity scores. The models are being used to power an experimental system now available via the Internet.
Keyword Antiretroviral therapy
Computer models
HIV drug resistance
Predictions
Treatment outcome
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Medicine Publications
 
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