Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa

Revell, Andrew, Khabo, Paul, Ledwaba, Lotty, Emery, Sean, Wang, Dechao, Wood, Robin, Morrow, Carl, Tempelman, Hugo, Hamers, Raph L., Reiss, Peter, van Sighem, Ard, Pozniak, Anton, Montaner, Julio, Lane, H. Clifford and Larder, Brendan (2016) Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa. Southern African Journal of HIV Medicine, 17 1: . doi:10.4102/sajhivmed.v17i1.450

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
UQ512426_OA.pdf Full text (open access) application/pdf 1.83MB 0

Author Revell, Andrew
Khabo, Paul
Ledwaba, Lotty
Emery, Sean
Wang, Dechao
Wood, Robin
Morrow, Carl
Tempelman, Hugo
Hamers, Raph L.
Reiss, Peter
van Sighem, Ard
Pozniak, Anton
Montaner, Julio
Lane, H. Clifford
Larder, Brendan
Title Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa
Journal name Southern African Journal of HIV Medicine   Check publisher's open access policy
ISSN 1608-9693
2078-6751
Publication date 2016-06-01
Year available 2016
Sub-type Article (original research)
DOI 10.4102/sajhivmed.v17i1.450
Open Access Status DOI
Volume 17
Issue 1
Total pages 7
Place of publication Rondebosch, Cape Town, South Africa
Publisher AOSIS
Collection year 2017
Language eng
Formatted abstract
Background: Selecting the optimal combination of HIV drugs for an individual in resourcelimited settings is challenging because of the limited availability of drugs and genotyping.

Objective: The evaluation as a potential treatment support tool of computational models that predict response to therapy without a genotype, using cases from the Phidisa cohort in South Africa.

Methods: Cases from Phidisa of treatment change following failure were identified that had the following data available: baseline CD4 count and viral load, details of failing and previous antiretroviral drugs, drugs in new regimen and time to follow-up. The HIV Resistance Response Database Initiative’s (RDI’s) models used these data to predict the probability of a viral load < 50 copies/mL at follow-up. The models were also used to identify effective alternative combinations of three locally available drugs.

Results: The models achieved accuracy (area under the receiver–operator characteristic curve) of 0.72 when predicting response to therapy, which is less accurate than for an independent global test set (0.80) but at least comparable to that of genotyping with rules-based interpretation. The models were able to identify alternative locally available three-drug regimens that were predicted to be effective in 69% of all cases and 62% of those whose new treatment failed in the clinic.

Conclusion: The predictive accuracy of the models for these South African patients together with the results of previous studies suggest that the RDI’s models have the potential to optimise treatment selection and reduce virological failure in different patient populations, without the use of a genotype.
Keyword Resource-Limited Settings
Therapy
Genotype
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Faculty of Medicine
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Google Scholar Search Google Scholar
Created: Sat, 18 Mar 2017, 01:00:47 EST by Web Cron on behalf of Learning and Research Services (UQ Library)