Background The monitoring of patients with human immunodeficiency virus (HIV) infection who are treated with antiretroviral medications in resource-limited settings is typically performed by use of clinical and immunological criteria. The early identification of first-line antiretroviral treatment failure is critical to prevent morbidity, mortality, and drug resistance. Misclassification of failure may result in premature switching to second-line therapy. Methods Adult patients in western Kenya had their viral loads (VLs) determined if they had adhered to first-line therapy for >6 months and were suspected of experiencing immunological failure (ie, their CD4 cell count decreased by ⩾25% in 6 months). Misclassification of treatment failure was defined as a ⩾25% decrease in CD4 cell count with a VL of <400 copies/mL. Logistic and tree regressions examined relationships between VL and 4 variables: CD4 T cell count (hereafter CD4 cell count), percentage of T cells expressing CD4 (hereafter CD4 cell percentage), percentage decrease in the CD4 T cell count (hereafter CD4 cell count percent decrease), and percentage decrease in the percentage of T cells expressing CD4 (hereafter CD4% percent decrease). Results There were 149 patients who were treated for 23 months; they were identified as having a ⩾25% decrease in CD4 cell count (from 375 to 216 cells/μL) and a CD4% percent decrease (from 19% to 15%); of these 149 patients, 86 (58%) were misclassified as having experienced treatment failure. Of 42 patients who had a ⩾50% decrease in CD4 cell count, 18 (43%) were misclassified. In multivariate logistic regression, misclassification odds were associated with a higher CD4 cell count, a shorter duration of therapy, and a smaller CD4% percent decrease. By combining these variables, we may be able to improve our ability to predict treatment failure. Conclusions Immunological monitoring as a sole indicator of virological failure would lead to a premature switch to valuable second-line regimens for 58% of patients who experience a ⩾25% decrease in CD4 cell count and for 43% patients who experience a ⩾50% decrease in CD4 cell count, and therefore this type of monitoring should be reevaluated. Selective virological monitoring and the addition of indicators like trends CD4% percent decrease and duration of therapy may systematically improve the identification of treatment failure. VL testing is now mandatory for patients suspected of experiencing first-line treatment failure within the Academic Model Providing Access to Healthcare (AMPATH) in western Kenya, and should be considered in all resource-limited settings.

This is the first longitudinal study to assess the relationship between M. genitalium and HIV-1 acquisition. If findings from this research are confirmed, M. genitalium screening and treatment among women at high risk for HIV-1 infection may be warranted as part of an HIV-1 prevention strategy.

Background Antiretroviral treatment interruptions (TIs) cause suboptimal clinical outcomes. Data on TIs during social disruption are limited. Methods We determined effects of unplanned TIs after the 2007–2008 Kenyan postelection violence on virological failure, comparing viral load (VL) outcomes in HIV-infected adults with and without conflict-induced TI. Results Two hundred and one patients were enrolled, median 2.2 years after conflict and 4.3 years on treatment. Eighty-eight patients experienced conflict-related TIs and 113 received continuous treatment. After adjusting for preconflict CD4, patients with TIs were more likely to have detectable VL, VL >5,000 and VL >10,000. Conclusions Unplanned conflict-related TIs are associated with increased likelihood of virological failure.

IntroductionAntiretroviral resistance leads to treatment failure and resistance transmission. Resistance data in western Kenya are limited. Collection of non-plasma analytes may provide additional resistance information.MethodsWe assessed HIV diversity using the REGA tool, transmitted resistance by the WHO mutation list and acquired resistance upon first-line failure by the IAS–USA mutation list, at the Academic Model Providing Access to Healthcare (AMPATH), a major treatment programme in western Kenya. Plasma and four non-plasma analytes, dried blood-spots (DBS), dried plasma-spots (DPS), ViveSTTM-plasma (STP) and ViveST-blood (STB), were compared to identify diversity and evaluate sequence concordance.ResultsAmong 122 patients, 62 were treatment-naïve and 60 treatment-experienced; 61% were female, median age 35 years, median CD4 182 cells/µL, median viral-load 4.6 log10 copies/mL. One hundred and ninety-six sequences were available for 107/122 (88%) patients, 58/62 (94%) treatment-naïve and 49/60 (82%) treated; 100/122 (82%) plasma, 37/78 (47%) attempted DBS, 16/45 (36%) attempted DPS, 14/44 (32%) attempted STP from fresh plasma and 23/34 (68%) from frozen plasma, and 5/42 (12%) attempted STB. Plasma and DBS genotyping success increased at higher VL and shorter shipment-to-genotyping time. Main subtypes were A (62%), D (15%) and C (6%). Transmitted resistance was found in 1.8% of plasma sequences, and 7% combining analytes. Plasma resistance mutations were identified in 91% of treated patients, 76% NRTI, 91% NNRTI; 76% dual-class; 60% with intermediate-high predicted resistance to future treatment options; with novel mutation co-occurrence patterns. Nearly 88% of plasma mutations were identified in DBS, 89% in DPS and 94% in STP. Of 23 discordant mutations, 92% in plasma and 60% in non-plasma analytes were mixtures. Mean whole-sequence discordance from frozen plasma reference was 1.1% for plasma-DBS, 1.2% plasma-DPS, 2.0% plasma-STP and 2.3% plasma-STB. Of 23 plasma-STP discordances, one mutation was identified in plasma and 22 in STP (p<0.05). Discordance was inversely significantly related to VL for DBS.ConclusionsIn a large treatment programme in western Kenya, we report high HIV-1 subtype diversity; low plasma transmitted resistance, increasing when multiple analytes were combined; and high-acquired resistance with unique mutation patterns. Resistance surveillance may be augmented by using non-plasma analytes for lower-cost genotyping in resource-limited settings.