Failure of immunologic criteria to appropriately identify antiretroviral treatment failure in Uganda

National Institute of Allergy and Infectious Diseases, Bethesda, Maryland, USA.
AIDS (London, England) (Impact Factor: 5.55). 02/2009; 23(6):697-700. DOI: 10.1097/QAD.0b013e3283262a78
Source: PubMed


Most antiretroviral treatment program in resource-limited settings use immunologic or clinical monitoring to measure response to therapy and to decide when to change to a second-line regimen. Our objective was to evaluate immunologic failure criteria against gold standard virologic monitoring.
Observational cohort.
Participants enrolled in an antiretroviral treatment program in rural Uganda who had at least 6 months of follow-up were included in this analysis. Immunologic monitoring was performed by CD4 cell counts every 3 months during the first year, and every 6 months thereafter. HIV-1 viral loads were performed every 6 months.
A total of 1133 participants enrolled in the Rakai Health Sciences Program antiretroviral treatment program between June 2004 and September 2007 were followed for up to 44.4 months (median follow-up 20.2 months; IQR 12.4-29.5 months). WHO immunologic failure criteria were reached by 125 (11.0%) participants. A virologic failure endpoint defined as HIV-1 viral load more than 400 copies/ml on two measurements was reached by 112 participants (9.9%). Only 26 participants (2.3%) experienced both an immunologic and virologic failure endpoint (2 viral load > 400 copies/ml) during follow-up.
Immunologic failure criteria performed poorly in our setting and would have resulted in a substantial proportion of participants with suppressed HIV-1 viral load being switched unnecessarily. These criteria also lacked sensitivity to identify participants failing virologically. Periodic viral load measurements may be a better marker for treatment failure in our setting.

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Available from: Thomas C Quinn, Jul 10, 2014
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    • "The successful roll-out of ART was achieved by adopting a public health approach to HIV care and treatment involving cost minimization strategies, delegation of tasks from highly skilled to less skilled health workers and simplification of the routine laboratory tests that are used to monitor ongoing efficacy of ART [1, 2]. Due to cost constraints, laboratory monitoring of ART efficacy is often limited to the CD4 cell count - a test that has low accuracy for identifying patients experiencing treatment failure to ART [3]. The gold standard for treatment failure, viral load testing, is largely unavailable because of its complexity and costs. "
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    ABSTRACT: Background In resource limited settings access to laboratory monitoring of HIV treatment is limited and therapeutic drug monitoring is generally unavailable. This study aimed to evaluate nevirapine concentrations in saliva using low-cost thin-layer chromatography (TLC) and nevirapine concentrations in plasma and saliva using high performance liquid chromatography (HPLC) methods; and to correlate nevirapine plasma concentrations to HIV treatment outcomes in Ugandan patients. Methods Paired plasma and stimulated saliva samples were obtained from Ugandan, HIV-infected adults on nevirapine-based ART. Nevirapine concentrations were measured using a validated HPLC method and a novel TLC method. Plasma nevirapine concentrations <3.0 mg/L using HPLC were considered subtherapeutic. Negative/positive predictive values of different thresholds for subtherapeutic nevirapine concentrations in saliva were determined. Virologic testing and, if applicable, HIV drug resistance testing was performed. Results Median (interquartile range, IQR) age of 297 patients was 39.1 (32.8-45.2) years. Three hundred saliva and 287 plasma samples were available for analysis. Attempts failed to determine nevirapine saliva concentrations by TLC. Using HPLC, median (IQR) nevirapine concentrations in saliva and plasma were 3.40 (2.59-4.47) mg/L and 6.17 (4.79-7.96) mg/L, respectively. The mean (coefficient of variation,%) nevirapine saliva/plasma ratio was 0.58 (62%). A cut-off value of 1.60 mg/L nevirapine in saliva was associated with a negative/positive predictive value of 0.99/0.72 and a sensitivity/specificity of 87%/98% for predicting subtherapeutic nevirapine plasma concentrations, respectively. Only 5% (15/287) of patients had subtherapeutic nevirapine plasma concentrations, of which 3 patients had viral load results > 400 copies/mL. Patients with nevirapine concentrations in plasma <3.0 mg/L had an Odds Ratio of 3.29 (95% CI: 1.00 – 10.74) for virological failure (viral load >400 copies/mL). Conclusions The low-cost TLC technique for monitoring nevirapine in saliva was unsuccessful but monitoring nevirapine saliva and plasma concentrations using HPLC was shown to be feasible in the research/specialist context in Uganda. Further optimization and validation is required for the low-cost TLC technique.
    BMC Infectious Diseases 09/2014; 14(1):473. DOI:10.1186/1471-2334-14-473 · 2.61 Impact Factor
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    • "As the global ART cohort continues to expand and mature, the need for ongoing monitoring is becoming increasingly important to ensure treatment efficacy and minimize the risk of HIV drug resistance. Clinical and immunological monitoring techniques have poor sensitivity and specificity for detecting virologic failure, leading to a substantial misclassification of treatment responses, resulting in delayed switching in some cases and inappropriate switching from first line regimens in others [2]–[7]. "
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    ABSTRACT: Viral load (VL) monitoring is the standard of care in developing country settings for detecting HIV treatment failure. Since 2010 the World Health Organization has recommended a phase-in approach to VL monitoring in resource-limited settings. We conducted a systematic review of the accuracy and precision of HIV VL technologies for treatment monitoring. A search of Medline and Embase was conducted for studies evaluating the accuracy or reproducibility of commercially available HIV VL assays. 37 studies were included for review including evaluations of the Amplicor Monitor HIV-1 v1.5 (n = 25), Cobas TaqMan v2.0 (n = 11), Abbott RealTime HIV-1 (n = 23), Versant HIV-1 RNA bDNA 3.0 (n = 15), Versant HIV-1 RNA kPCR 1.0 (n = 2), ExaVir Load v3 (n = 2), and NucliSens EasyQ v2.0 (n = 1). All currently available HIV VL assays are of sufficient sensitivity to detect plasma virus levels at a lower detection limit of 1,000 copies/mL. Bias data comparing the Abbott RealTime HIV-1, TaqMan v2.0 to the Amplicor Monitor v1.5 showed a tendency of the Abbott RealTime HIV-1 to under-estimate results while the TaqMan v2.0 overestimated VL counts. Compared to the Amplicor Monitor v1.5, 2-26% and 9-70% of results from the Versant bDNA 3.0 and Abbott RealTime HIV-1 differed by greater than 0.5log10. The average intra and inter-assay variation of the Abbott RealTime HIV-1 were 2.95% (range 2.0-5.1%) and 5.44% (range 1.17-30.00%) across the range of VL counts (2log10-7log10). This review found that all currently available HIV VL assays are of sufficient sensitivity to detect plasma VL of 1,000 copies/mL as a threshold to initiate investigations of treatment adherence or possible treatment failure. Sources of variability between VL assays include differences in technology platform, plasma input volume, and ability to detect HIV-1 subtypes. Monitoring of individual patients should be performed on the same technology platform to ensure appropriate interpretation of changes in VL. Prospero registration # CD42013003603.
    PLoS ONE 02/2014; 9(2):e85869. DOI:10.1371/journal.pone.0085869 · 3.23 Impact Factor
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    • "A few studies have been done in the area of immune recovery in the Sub-Saharan Africa (SSA) but there are no specific data for patients with sustained viral suppression [3,6] since viral load testing is not routinely performed due to the high cost [14]. We sought to investigate long-term patterns of immune reconstitution after ART for up to 7 years, and factors associated with higher CD4 count increase in patients with documented sustained viral suppression. "
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    ABSTRACT: There is conflicting data on long-term CD4 immune recovery after combination antiretroviral therapy (ART) in resource-limited settings. Virologic suppression is rarely documented in cohorts from sub-Saharan Africa so objective evidence of adherence is biologically unsubstantiated. We sought to investigate long-term patterns of immune recovery in Ugandan patients on ART with sustained viral suppression. A prospective cohort of patients starting ART between April, 2004 and April, 2005 at the Infectious Diseases Institute with sustained viral suppression (viral load ≤400 copies/ml at month 6 and 12) while on first-line ART. Propensity scores were used to adjust for treatment allocation (nevirapine or efavirenz) at ART initiation. Data were analyzed using Kaplan Meier methods and cross-sectional time series regression. Three hundred and fifty-six patients were included in the analysis.71.6% were female, 87% in WHO stage 3 or 4, median age was 37 years, (IQR:32-43), and median CD4 count was 108 cells/µL, (IQR:35-174) at ART start. At multivariable analysis, lower immune recovery (measured by change in CD4 from ART start at each time interval) was associated with male-gender (-59, 95% CI: 90, -28, P<0.001), baseline CD4 count of 101-200 cells/µL (-35, 95% CI: 62, -9, P=0.009) and >200 (-64, 95% CI: 101, -26, P=0.001), and use of AZT at baseline (-47, 95% CI: -74, -20, P=0.001). Median time to reach >400 cells/µL was longer in males (197.4 weeks, IQR:119.9-312.0), compared to females (144.7 weeks, IQR:96.6-219.7, P<0.001). The cumulative probability of attaining CD4 >400 cells/µL over 7 years was higher in females compared to males (P<0.001). There was long-term, continuous, immunologic recovery up to 7 years after ART initiation in an urban Ugandan cohort. Virologically suppressed women had better sustained immune recovery than men. Men take longer to immune reconstitute and have a lower probability of reaching a CD4 cell count >400 cells/µL. The biologic mechanisms of these gender differences need further exploration.
    PLoS ONE 08/2013; 8(8):e73190. DOI:10.1371/journal.pone.0073190 · 3.23 Impact Factor
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