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Predicting virological response to HIV treatment over time

a tool for settings with different definitions of virological response

Revell, Andrew D, PhD*,1; Wang, Dechao, PhD1; Perez-Elias, Maria-Jesus, MD2; Wood, Robin, MD3; Tempelman, Hugo, MD4; Clotet, Bonaventura, MD5; Reiss, Peter, MD6,7; van Sighem, Ard I, PhD7; Alvarez-Uria, Gerardo, MD8; Nelson, Mark, MD9; Montaner, Julio Sg, MD10; Lane, H Clifford, MD11; Larder, Brendan A, PhD1

JAIDS Journal of Acquired Immune Deficiency Syndromes: February 14, 2019 - Volume Publish Ahead of Print - Issue - p
doi: 10.1097/QAI.0000000000001989
Original Article: PDF Only

Objective: Definitions of virological response vary from <50 up to 1,000 copies HIV RNA/mL. Our previous models estimate the probability of HIV drug combinations reducing the viral load to <50 copies/mL with no indication of whether higher thresholds of response might be achieved. Here we describe the development of models that predict absolute viral load over time.

Methods: Two sets of Random Forest models were developed using 50,270 treatment change episodes from more than 20 countries. The models estimated viral load at different time points following the introduction of a new regimen from variables including baseline viral load, CD4 count and treatment history. One set also used genotypes in their predictions. Independent data sets were used for evaluation.

Results: Both models achieved highly significant correlations between predicted and actual viral load changes with (r= 0.67-0.68, mean absolute error of 0.73-0.74 log10 copies/mL). The models produced curves of virological response over time. Using failure definitions of <100, 400 or 1,000 copies/mL, but not 50 copies/mL, both models were able to identify alternative regimens they predicted to be effective for the majority of cases where the new regimen prescribed in the clinic failed.

Conclusions: These models could be useful for selecting the optimum combination therapy for patients requiring a change in therapy in settings using any definition of virological response. They also give an idea of the likely response curve over time. Given that genotypes are not required, these models could be a useful addition to the HIV-TRePS system for those in resource-limited settings.

1The HIV Resistance Response Database Initiative (RDI), London UK

2Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain

3Desmond Tutu HIV Centre, University of Cape Town, South Africa

4Ndlovu Care Group, Elandsdoorn, South Africa

5Institut de Recerca de la Sida, IrsiCaixa. Barcelona, Spain

6Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands

7Stichting HIV Monitoring, Amsterdam, The Netherlands

8Rural Development Trust (RDT) Hospital, Bathalapalli, AP, India

9Chelsea and Westminster Hospital, London, UK

10BC Centre for Excellence in HIV/AIDS, Vancouver, Canada

11National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA

Correspondence to Andrew D. Revell, 14 Union Square, London N1 7DH, UK. Tel: +44 207 226 7314; fax: +44 207 226 7314; e-mail:

The authors report no conflicts of interest related to this work.

† Members are listed in the Acknowledgements section.

Funding: RDI’s participation in this project is through a subcontract with Leidos Biomedical Research, the prime contractor for the Frederick National Laboratory for Cancer Research, sponsored by the National Cancer Institute.

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