Missing data on the estimation of the prevalence of accumulated human immunodeficiency virus drug resistance in patients treated with antiretroviral drugs in north america. - Abraham, Alison G; Lau, Bryan; Deeks, Steven; Moore, Richard D; Zhang, Jinbing; Eron, Joseph; Harrigan, Richard; Gill, M John; Kitahata, Mari; Klein, Marina; Napravnik, Sonia; Rachlis, Anita; et al. (American journal of epidemiology, 2011)
Determination of the prevalence of accumulated antiretroviral drug resistance among persons infected with human immunodeficiency virus (HIV) is complicated by the lack of routine measurement in clinical care. By using data from 8 clinic-based cohorts from the North American AIDS Cohort Collaboration on Research and Design, drug-resistance mutations from those with genotype tests were determined and scored using the Genotypic Resistance Interpretation Algorithm developed at Stanford University. For each year from 2000 through 2005, the prevalence was calculated using data from the tested subset, assumptions that incorporated clinical knowledge, and multiple imputation methods to yield a complete data set. A total of 9,289 patients contributed data to the analysis; 3,959 had at least 1 viral load above 1,000 copies/mL, of whom 2,962 (75%) had undergone at least 1 genotype test. Using these methods, the authors estimated that the prevalence of accumulated resistance to 2 or more antiretroviral drug classes had increased from 14% in 2000 to 17% in 2005 (P < 0.001). In contrast, the prevalence of resistance in the tested subset declined from 57% to 36% for 2 or more classes. The authors' use of clinical knowledge and multiple imputation methods revealed trends in HIV drug resistance among patients in care that were markedly different from those observed using only data from patients who had undergone genotype tests.
Keywords: Adult; Anti-HIV Agents; Drug Resistance, Multiple, Viral; Female; Follow-Up Studies; Genotype; HIV Infections; HIV-1; Humans; Male; Middle Aged; Mutation; North America; Prevalence; Retrospective Studies; Risk Assessment; RNA, Viral; Time Factors