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Revell AD, Wang D, Perez-Elias MJ, Wood R, Cogill D, Tempelman H, Hamers RL, Reiss P, van Sighem A, Rehm CA, Agan B, Alvarez-Uria G, Montaner JSG, Lane HC, Larder BA. 2021 update to HIV-TRePS: a highly flexible and accurate system for the prediction of treatment response from incomplete baseline information in different healthcare settings. J Antimicrob Chemother 2021; 76:1898-1906. [PMID: 33792714 DOI: 10.1093/jac/dkab078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 02/23/2021] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVES With the goal of facilitating the use of HIV-TRePS to optimize therapy in settings with limited healthcare resources, we aimed to develop computational models to predict treatment responses accurately in the absence of commonly used baseline data. METHODS Twelve sets of random forest models were trained using very large, global datasets to predict either the probability of virological response (classifier models) or the absolute change in viral load in response to a new regimen (absolute models) following virological failure. Two 'standard' models were developed with all baseline variables present and 10 others developed without HIV genotype, time on therapy, CD4 count or any combination of the above. RESULTS The standard classifier models achieved an AUC of 0.89 in cross-validation and independent testing. Models with missing variables achieved AUC values of 0.78-0.90. The standard absolute models made predictions that correlated significantly with observed changes in viral load with a mean absolute error of 0.65 log10 copies HIV RNA/mL in cross-validation and 0.69 log10 copies HIV RNA/mL in independent testing. Models with missing variables achieved values of 0.65-0.75 log10 copies HIV RNA/mL. All models identified alternative regimens that were predicted to be effective for the vast majority of cases where the new regimen prescribed in the clinic failed. All models were significantly better predictors of treatment response than genotyping with rules-based interpretation. CONCLUSIONS These latest models that predict treatment responses accurately, even when a number of baseline variables are not available, are a major advance with greatly enhanced potential benefit, particularly in resource-limited settings. The only obstacle to realizing this potential is the willingness of healthcare professions to use the system.
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Affiliation(s)
- Andrew D Revell
- The HIV Resistance Response Database Initiative (RDI), London, UK
| | - Dechao Wang
- The HIV Resistance Response Database Initiative (RDI), London, UK
| | | | - Robin Wood
- Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa
| | - Dolphina Cogill
- Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa
| | | | - Raph L Hamers
- Departments of Internal Medicine and Global Health, Academic Medical Centre of the University of Amsterdam, Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands
| | - Peter Reiss
- Departments of Internal Medicine and Global Health, Academic Medical Centre of the University of Amsterdam, Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands.,Stichting HIV Monitoring, Amsterdam, The Netherlands
| | | | - Catherine A Rehm
- National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA
| | - Brian Agan
- Uniformed Services University of the Health Sciences and Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | | | | | - H Clifford Lane
- National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA
| | - Brendan A Larder
- The HIV Resistance Response Database Initiative (RDI), London, UK
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