OBJECTIVES: Determining the relationship between antiretroviral adherence and resistance accumulation is important for the design and evaluation of adherence interventions. Our objective was to explain heterogeneity observed in this relationship. METHODS: We first conducted a systematic review to locate published reports describing the relationship between adherence and resistance. We then used a validated computer simulation to simulate the patient populations in these reports, exploring the impact of changes in individual patient characteristics (age, CD4, viral load, prior antiretroviral experience) on the shape of the adherence-resistance (A-R) curve. RESULTS: The search identified 493 titles, of which 3 contained relevant primary data and 2 had sufficient follow-up for inclusion (HOMER and REACH cohorts). When simulating HOMER, the A-R curve had a high peak with a greatly increased hazard ratio (HR) of accumulating mutations at partial compared to complete adherence (simulation, HR 2.9; HOMER, HR 2.7). When simulating REACH, the A-R curve had a shallow peak with a slightly increased hazard of accumulating mutations at partial adherence (simulation, HR 1.2; REACH, HR 1.4). This heterogeneity was primarily attributable to differences in antiretroviral experience between the cohorts. CONCLUSIONS: Our computer simulation was able to explain much of the heterogeneity in observed A-R curves.
OBJECTIVES: Determining the relationship between antiretroviral adherence and resistance accumulation is important for the design and evaluation of adherence interventions. Our objective was to explain heterogeneity observed in this relationship. METHODS: We first conducted a systematic review to locate published reports describing the relationship between adherence and resistance. We then used a validated computer simulation to simulate the patient populations in these reports, exploring the impact of changes in individual patient characteristics (age, CD4, viral load, prior antiretroviral experience) on the shape of the adherence-resistance (A-R) curve. RESULTS: The search identified 493 titles, of which 3 contained relevant primary data and 2 had sufficient follow-up for inclusion (HOMER and REACH cohorts). When simulating HOMER, the A-R curve had a high peak with a greatly increased hazard ratio (HR) of accumulating mutations at partial compared to complete adherence (simulation, HR 2.9; HOMER, HR 2.7). When simulating REACH, the A-R curve had a shallow peak with a slightly increased hazard of accumulating mutations at partial adherence (simulation, HR 1.2; REACH, HR 1.4). This heterogeneity was primarily attributable to differences in antiretroviral experience between the cohorts. CONCLUSIONS: Our computer simulation was able to explain much of the heterogeneity in observed A-R curves.
Authors: Amin Khademi; Denis Saure; Andrew Schaefer; Kimberly Nucifora; R Scott Braithwaite; Mark S Roberts Journal: Value Health Date: 2015-11-12 Impact factor: 5.725
Authors: Stephen J Gange; Mari M Kitahata; Michael S Saag; David R Bangsberg; Ronald J Bosch; John T Brooks; Liviana Calzavara; Steven G Deeks; Joseph J Eron; Kelly A Gebo; M John Gill; David W Haas; Robert S Hogg; Michael A Horberg; Lisa P Jacobson; Amy C Justice; Gregory D Kirk; Marina B Klein; Jeffrey N Martin; Rosemary G McKaig; Benigno Rodriguez; Sean B Rourke; Timothy R Sterling; Aimee M Freeman; Richard D Moore Journal: Int J Epidemiol Date: 2007-01-08 Impact factor: 7.196
Authors: R Scott Braithwaite; David A Fiellin; Kimberly Nucifora; Kendall Bryant; Mark Roberts; Nancy Kim; Amy C Justice Journal: Value Health Date: 2010-03-22 Impact factor: 5.725
Authors: R Scott Braithwaite; Mark S Roberts; Matthew Bidwell Goetz; Cynthia L Gibert; Maria C Rodriguez-Barradas; Kimberly Nucifora; Amy C Justice Journal: Clin Infect Dis Date: 2009-03-15 Impact factor: 9.079