Andrew D Revell1, Dechao Wang1, Maria-Jesus Perez-Elias2, Robin Wood3, Hugo Tempelman4, Bonaventura Clotet5, Peter Reiss6,7, Ard I van Sighem7, Gerardo Alvarez-Uria8, Mark Nelson9, Julio S G Montaner10, H Clifford Lane11, Brendan A Larder1. 1. The HIV Resistance Response Database Initiative (RDI), London, United Kingdom. 2. Servicio de Enfermedades Infecciosas, Hospital Ramón y Cajal, Madrid, Spain. 3. Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa. 4. Ndlovu Care Group, Elandsdoorn, South Africa. 5. Institut de Recerca de la Sida, IrsiCaixa, Hospital Universitari Germans Trias i Pujol, Barcelona, Spain. 6. Amsterdam Institute for Global Health and Development, Amsterdam, the Netherlands. 7. Stichting HIV Monitoring, Amsterdam, the Netherlands. 8. Rural Development Trust (RDT) Hospital, Bathalapalli, India. 9. Chelsea and Westminster Hospital, London, United Kingdom. 10. BC Centre for Excellence in HIV/AIDS, St. Paul's Hospital, Vancouver, British Columbia, Canada. 11. National Institute of Allergy and Infectious Diseases, Bethesda, MD.
Abstract
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.
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.
Authors: Andrew D Revell; Dechao Wang; Maria-Jesus Perez-Elias; Robin Wood; Dolphina Cogill; Hugo Tempelman; Raph L Hamers; Peter Reiss; Ard van Sighem; Catherine A Rehm; Brian Agan; Gerardo Alvarez-Uria; Julio S G Montaner; H Clifford Lane; Brendan A Larder Journal: J Antimicrob Chemother Date: 2021-06-18 Impact factor: 5.790