Browsing by Author "Sanne, Ian"

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    Dose-ranging, randomized, clinical trial of atazanavir with lamivudine and stavudine in antiretroviral-naive subjects
    (2003-12-05) Murphy, Robert L; Sanne, Ian; Cahn, Pedro; Phanuphak, Praphan; Percival, Lisa; Kelleher, Thomas; Giordano, Michael
    Objective: To compare the efficiency and safety of atazanavir and nelfinavir in antiretroviral-naive patients. Design: Randomization to atazanavir 400 mg or 600 mg once daily; nelfinavir 1250 mg twice a day, plus lamivudine and stavudine. Methods: A blinded (to the atazanavir dose), 48-week trial in patients with HIV-1 RNA ≥ 2000 copies/ml, CD4 cell count ≥ 100 × 106cells/l. Primary end-point: change in HIV-1 RNA from baseline at 48 weeks. Secondary end-point: subjects with HIV-1 RNA < 400, and < 50 copies/ml, CD4 cell count changes, adverse events. Results: The 467 randomized subjects had comparable baseline characteristics across treatments. With atazanavir 400 mg, 600 mg and nelfinavir, respectively, mean changes in HIV-1 RNA (log10 copies/ml) from baseline to 48 weeks were −2.51, −2.58, −2.31; HIV-1 RNA < 400 copies/ml [intent-to-treat population (ITT), non-completion = failure (NC = F)], 64%, 67%, 53%; HIV-1 RNA < 50 copies/ml (ITT NC = F), 35%, 36%, 34%; mean CD4 cell count increased comparably at 48 weeks (234 × 106, 243 × 106, 211 × 106cells/l). Adverse events were similar across treatments with the exception of diarrhea (more frequent with nelfinavir) and jaundice (more frequent with atazanavir). Mean changes from baseline to 48 weeks were: fasting low density lipoprotein cholesterol, +5.2%, +7.1% and +23.2% (at 56 weeks) and fasting triglycerides (48 weeks), +7.2%, +7.6% and +49.5%, in the atazanavir 400 mg, 600 mg, and nelfinavir groups, respectively (P < 0.01, atazanavir versus nelfinavir). Conclusions: Atazanavir is a potent, safe, well tolerated, and effective once-daily protease inhibitor with low pill burden (two capsules/day). Lipid changes with atazanavir were significantly less than with nelfinavir, however, clinical significance of these finding in terms of decreased cardiovascular risk is unknown.
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    Prioritizing CD4 Count Monitoring in Response to ART in Resource-Constrained Settings: A Retrospective Application of Prediction-Based Classification
    (2012) Azzoni, Livio; Foulkes, Andrea S.; Liu, Yan; Li, Xin; Johnson, Mark; Smith, Charles; Kamarulzaman, Adeeba; Montaner, Julio; Mounzer, Joseph; Saag, Michael; Cahn, Pedro; Cesar, Carina; Krolewiecki, Alejandro J.; Sanne, Ian; Montaner, Luis J.
    Background Global programs of anti-HIV treatment depend on sustained laboratory capacity to assess treatment initiation thresholds and treatment response over time. Currently, there is no valid alternative to CD4 count testing for monitoring immunologic responses to treatment, but laboratory cost and capacity limit access to CD4 testing in resource-constrained settings. Thus, methods to prioritize patients for CD4 count testing could improve treatment monitoring by optimizing resource allocation. Methods and Findings Using a prospective cohort of HIV-infected patients (n=1,956) monitored upon antiretroviral therapy initiation in seven clinical sites with distinct geographical and socio-economic settings, we retrospectively apply a novel prediction-based classification (PBC) modeling method. The model uses repeatedly measured biomarkers (white blood cell count and lymphocyte percent) to predict CD4+ T cell outcome through first-stage modeling and subsequent classification based on clinically relevant thresholds (CD4+ T cell count of 200 or 350 cells/µl). The algorithm correctly classified 90% (cross-validation estimate=91.5%, standard deviation [SD]=4.5%) of CD4 count measurements <200 cells/µl in the first year of follow-up; if laboratory testing is applied only to patients predicted to be below the 200-cells/µl threshold, we estimate a potential savings of 54.3% (SD=4.2%) in CD4 testing capacity. A capacity savings of 34% (SD=3.9%) is predicted using a CD4 threshold of 350 cells/µl. Similar results were obtained over the 3 y of follow-up available (n=619). Limitations include a need for future economic healthcare outcome analysis, a need for assessment of extensibility beyond the 3-y observation time, and the need to assign a false positive threshold. Conclusions Our results support the use of PBC modeling as a triage point at the laboratory, lessening the need for laboratory-based CD4+ T cell count testing; implementation of this tool could help optimize the use of laboratory resources, directing CD4 testing towards higher-risk patients. However, further prospective studies and economic analyses are needed to demonstrate that the PBC model can be effectively applied in clinical settings.