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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., PhDa; Wang, Dechao, PhDa; Perez-Elias, Maria-Jesus, MDb; Wood, Robin, MDc; Tempelman, Hugo, MDd; Clotet, Bonaventura, MDe; Reiss, Peter, MDf,g; van Sighem, Ard I., PhDg; Alvarez-Uria, Gerardo, MDh; Nelson, Mark, MDi; Montaner, Julio S. G., MDj; Lane, H. Clifford, MDk; Larder, Brendan A., PhDa

JAIDS Journal of Acquired Immune Deficiency Syndromes: June 1, 2019 - Volume 81 - Issue 2 - p 207–215
doi: 10.1097/QAI.0000000000001989
Clinical Science
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Objective: Definitions of virological response vary from <50 up to 1000 copies of 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 may 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 (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 1000 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.

aThe HIV Resistance Response Database Initiative (RDI), London, United Kingdom;

bServicio de Enfermedades Infecciosas, Hospital Ramón y Cajal, Madrid, Spain;

cDesmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa;

dNdlovu Care Group, Elandsdoorn, South Africa;

eInstitut de Recerca de la Sida, IrsiCaixa, Hospital Universitari Germans Trias i Pujol, Barcelona, Spain;

fAmsterdam Institute for Global Health and Development, Amsterdam, the Netherlands;

gStichting HIV Monitoring, Amsterdam, the Netherlands;

hRural Development Trust (RDT) Hospital, Bathalapalli, India;

iChelsea and Westminster Hospital, London, United Kingdom;

jBC Centre for Excellence in HIV/AIDS, St. Paul's Hospital, Vancouver, British Columbia, Canada; and

kNational Institute of Allergy and Infectious Diseases, Bethesda, MD

Correspondence to: Andrew D. Revell, PhD, The HIV Resistance Response Database Initiative (RDI), 14 Union Square, London N1 7DH, United Kingdom (e-mail: andrewrevell@hivrdi.org).

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.

The authors have no funding or conflicts of interest to disclose.

Received September 11, 2018

Accepted December 27, 2018

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.