HIV virologic failure and its predictors among HIV-infected adults on antiretroviral therapy in the African Cohort Study
Adult
Male
Risk
Adolescent
Science
HIV Infections
Cohort Studies
Young Adult
03 medical and health sciences
0302 clinical medicine
Predictive Value of Tests
Humans
Treatment Failure
Q
R
Middle Aged
Viral Load
16. Peace & justice
CD4 Lymphocyte Count
3. Good health
Anti-Retroviral Agents
Africa
Multivariate Analysis
HIV-1
Linear Models
Medicine
RNA, Viral
Female
Research Article
DOI:
10.1371/journal.pone.0211344
Publication Date:
2019-02-05T18:36:39Z
AUTHORS (26)
ABSTRACT
Introduction The 2016 WHO consolidated guidelines on the use of antiretroviral drugs defines HIV virologic failure for low and middle income countries (LMIC) as plasma HIV-RNA ≥ 1000 copies/mL. We evaluated predictors in four African countries. Materials methods included HIV-infected participants a recommended therapy (ART) regimen enrolled Cohort Study between January 2013 October 2017. Studied outcomes were (plasma copies/mL at most recent visit), viraemia 50 visit); persistent two consecutive visits). Generalized linear models used to estimate relative risks with their 95% confidence intervals. Results 2054 this analysis. Viraemia, observed 396 (19.3%), 160 (7.8%) 184 (9%) respectively. Of viraemia, only 57.5% (92/160) had confirmed failure. In multivariate analysis, attending clinical care site other than Uganda sitebeing 2nd line ART (aRR 1.8, CI 1·28–2·66); combinations not first second 3.8, 1.18–11.9), history fever past week 3.7, 1.69–8.05), CD4 count 6.9, 4.7–10.2) missing any day 1·8, 1·27–2.57) increased risk Being therapy, where one receives < 500 predicted Conclusion conclusion, these findings demonstrate that patients established more six months setting frequently experienced while continuing be ART. also show being line, count, remain important should trigger intensified adherence counselling especially absence reliable or readily available viral load monitoring. Finally, sites are different calling further analyses elucidate unique features sites.
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