939 resultados para ANTIRETROVIRAL DRUGS


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Exposure to combination antiretroviral therapy (cART) can lead to important metabolic changes and increased risk of coronary heart disease (CHD). Computerized clinical decision support systems have been advocated to improve the management of patients at risk for CHD but it is unclear whether such systems reduce patients' risk for CHD.

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We aimed to assess whether the use of non-steroidal anti-inflammatory drugs (NSAIDs) is associated with an increased risk of cardiovascular (CV) events in stable patients with established atherothrombosis or multiple risk factors.

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The central issue in organ transplantation remains suppression of allograft rejection. Immunosuppression can be achieved by depleting lymphocytes, diverting lymphocyte traffic, or blocking lymphocyte response pathways. Immunosuppressive drugs include small-molecule drugs, depleting and nondepleting protein drugs (polyclonal and monoclonal antibodies), fusion proteins, intravenous immune globulin, and glucocorticoids. Small-molecule immunosuppressive agents include calcineurin-inhibitors (cyclosporine, tacrolimus), Target-of-Rapamycin Inhibitors (Sirolimus, Everolimus), inhibitors of nucleotide synthesis and azathioprine. The review covers the mode of action of these drugs with a special focus on belatacept, a new promising fusion protein. Different immuo-suppressive strategies mean also different safety profiles. Common side effects include the consequences of diminished immuno- response, i.e. infections and cancer (mainly involving the skin). Toxic side effects of immunosuppressive drugs range in a wide spectrum that involves almost every organ. The major interest of this toxic effects is the cardiovascular tolerance (with large differences from drug to drug), that are discussed seperately. The calcineurin- and mTOR-inhibitors are both metabolized by the CYP450 3A4 enzyme, which is also involved in the metabolism of many other drugs. The review discusses the most important interactions that in- or decreases the through level of these drugs.

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The risk of Hodgkin lymphoma (HL) is increased in patients infected with HIV-1. We studied the incidence and outcomes of HL, and compared CD4⁺ T-cell trajectories in HL patients and controls matched for duration of combination antiretroviral therapy (cART). A total of 40 168 adult HIV-1-infected patients (median age, 36 years; 70% male; median CD4 cell count, 234 cells/μL) from 16 European cohorts were observed during 159 133 person-years; 78 patients developed HL. The incidence was 49.0 (95% confidence interval [CI], 39.3-61.2) per 100,000 person-years, and similar on cART and not on cART (P = .96). The risk of HL declined as the most recent (time-updated) CD4 count increased: the adjusted hazard ratio comparing more than 350 with less than 50 cells/μL was 0.27 (95% CI, 0.08-0.86). Sixty-one HL cases diagnosed on cART were matched to 1652 controls: during the year before diagnosis, cases lost 98 CD4 cells (95% CI, -159 to -36 cells), whereas controls gained 35 cells (95% CI, 24-46 cells; P < .0001). The incidence of HL is not reduced by cART, and patients whose CD4 cell counts decline despite suppression of HIV-1 replication on cART may harbor HL.

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Background The World Health Organization estimates that in sub-Saharan Africa about 4 million HIV-infected patients had started antiretroviral therapy (ART) by the end of 2008. Loss of patients to follow-up and care is an important problem for treatment programmes in this region. As mortality is high in these patients compared to patients remaining in care, ART programmes with high rates of loss to follow-up may substantially underestimate mortality of all patients starting ART. Methods and Findings We developed a nomogram to correct mortality estimates for loss to follow-up, based on the fact that mortality of all patients starting ART in a treatment programme is a weighted average of mortality among patients lost to follow-up and patients remaining in care. The nomogram gives a correction factor based on the percentage of patients lost to follow-up at a given point in time, and the estimated ratio of mortality between patients lost and not lost to follow-up. The mortality observed among patients retained in care is then multiplied by the correction factor to obtain an estimate of programme-level mortality that takes all deaths into account. A web calculator directly calculates the corrected, programme-level mortality with 95% confidence intervals (CIs). We applied the method to 11 ART programmes in sub-Saharan Africa. Patients retained in care had a mortality at 1 year of 1.4% to 12.0%; loss to follow-up ranged from 2.8% to 28.7%; and the correction factor from 1.2 to 8.0. The absolute difference between uncorrected and corrected mortality at 1 year ranged from 1.6% to 9.8%, and was above 5% in four programmes. The largest difference in mortality was in a programme with 28.7% of patients lost to follow-up at 1 year. Conclusions The amount of bias in mortality estimates can be large in ART programmes with substantial loss to follow-up. Programmes should routinely report mortality among patients retained in care and the proportion of patients lost. A simple nomogram can then be used to estimate mortality among all patients who started ART, for a range of plausible mortality rates among patients lost to follow-up.

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To measure rates and predictors of virologic failure and switch to second-line antiretroviral therapy (ART) in South Africa.

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In low-income settings, treatment failure is often identified using CD4 cell count monitoring. Consequently, patients remain on a failing regimen, resulting in a higher risk of transmission. We investigated the benefit of routine viral load monitoring for reducing HIV transmission.

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Background Loss to follow-up (LTFU) is common in antiretroviral therapy (ART) programmes. Mortality is a competing risk (CR) for LTFU; however, it is often overlooked in cohort analyses. We examined how the CR of death affected LTFU estimates in Zambia and Switzerland. Methods and Findings HIV-infected patients aged ≥18 years who started ART 2004–2008 in observational cohorts in Zambia and Switzerland were included. We compared standard Kaplan-Meier curves with CR cumulative incidence. We calculated hazard ratios for LTFU across CD4 cell count strata using cause-specific Cox models, or Fine and Gray subdistribution models, adjusting for age, gender, body mass index and clinical stage. 89,339 patients from Zambia and 1,860 patients from Switzerland were included. 12,237 patients (13.7%) in Zambia and 129 patients (6.9%) in Switzerland were LTFU and 8,498 (9.5%) and 29 patients (1.6%), respectively, died. In Zambia, the probability of LTFU was overestimated in Kaplan-Meier curves: estimates at 3.5 years were 29.3% for patients starting ART with CD4 cells <100 cells/µl and 15.4% among patients starting with ≥350 cells/µL. The estimates from CR cumulative incidence were 22.9% and 13.6%, respectively. Little difference was found between naïve and CR analyses in Switzerland since only few patients died. The results from Cox and Fine and Gray models were similar: in Zambia the risk of loss to follow-up and death increased with decreasing CD4 counts at the start of ART, whereas in Switzerland there was a trend in the opposite direction, with patients with higher CD4 cell counts more likely to be lost to follow-up. Conclusions In ART programmes in low-income settings the competing risk of death can substantially bias standard analyses of LTFU. The CD4 cell count and other prognostic factors may be differentially associated with LTFU in low-income and high-income settings.

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Data on outcomes of antiretroviral treatment (ART) programs in rural sub-Saharan African are scarce. We describe early losses and long-term outcomes in 6 rural programs in Southern Africa with limited access to viral load monitoring and second-line ART.

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Objectives  To determine the diagnostic accuracy of World Health Organization (WHO) 2010 and 2006 as well as United States Department of Health and Human Services (DHHS) 2008 definitions of immunological failure for identifying virological failure (VF) in children on antiretroviral therapy (ART). Methods  Analysis of data from children (<16 years at ART initiation) at South African ART sites at which CD4 count/per cent and HIV-RNA monitoring are performed 6-monthly. Incomplete virological suppression (IVS) was defined as failure to achieve ≥1 HIV-RNA ≤400 copies/ml between 6 and 15 months on ART and viral rebound (VR) as confirmed HIV-RNA ≥5000 copies/ml in a child on ART for ≥18 months who had achieved suppression during the first year on treatment. Results  Among 3115 children [median (interquartile range) age 48 (20-84) months at ART initiation] on treatment for ≥1 year, sensitivity of immunological criteria for IVS was 10%, 6% and 26% for WHO 2006, WHO 2010 and DHHS 2008 criteria, respectively. The corresponding positive predictive values (PPV) were 31%, 20% and 20%. Diagnostic accuracy for VR was determined in 2513 children with ≥18 months of follow-up and virological suppression during the first year on ART with sensitivity of 5% (WHO 2006/2010) and 27% (DHHS 2008). PPV results were 42% (WHO 2010), 43% (WHO 2006) and 20% (DHHS 2008). Conclusion  Current immunological criteria are unable to correctly identify children failing ART virologically. Improved access to viral load testing is needed to reliably identify VF in children.

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Objectives: To compare outcomes of antiretroviral therapy (ART) in South Africa, where viral load monitoring is routine, with those in Malawi and Zambia, where monitoring is based on CD4 cell counts. Methods: We included 18 706 adult patients starting ART in South Africa and 80 937 patients in Zambia or Malawi. We examined CD4 responses in models for repeated measures and the probability of switching to second-line regimens, mortality and loss to follow-up in multistate models, measuring time from 6 months. Results: In South Africa, 9.8% [95% confidence interval (CI) 9.1–10.5] had switched at 3 years, 1.3% (95% CI 0.9–1.6) remained on failing first-line regimens, 9.2% (95% CI 8.5–9.8) were lost to follow-up and 4.3% (95% CI 3.9–4.8) had died. In Malawi and Zambia, more patients were on a failing first-line regimen [3.7% (95% CI 3.6–3.9], fewer patients had switched [2.1% (95% CI 2.0–2.3)] and more patients were lost to follow-up [15.3% (95% CI 15.0–15.6)] or had died [6.3% (95% CI 6.0–6.5)]. Median CD4 cell counts were lower in South Africa at the start of ART (93 vs. 132 cells/μl; P < 0.001) but higher after 3 years (425 vs. 383 cells/μl; P < 0.001). The hazard ratio comparing South Africa with Malawi and Zambia after adjusting for age, sex, first-line regimen and CD4 cell count was 0.58 (0.50–0.66) for death and 0.53 (0.48–0.58) for loss to follow-up. Conclusion: Over 3 years of ART mortality was lower in South Africa than in Malawi or Zambia. The more favourable outcome in South Africa might be explained by viral load monitoring leading to earlier detection of treatment failure, adherence counselling and timelier switching to second-line ART.

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An in-depth understanding of the different groups that make up the HIV-infected population should inform prevention and care. Using latent class analysis (LCA) we identified seven groups with similar socio-demographic and behavioral characteristics at enrolment in the Swiss HIV Cohort Study: older gay men, younger gay men, older heterosexual men, injection drug users, single migrants, migrant women in partnerships and heterosexual men and women. Outcomes of combination antiretroviral therapy (ART) were analyzed in 1,633 patients starting ART. Compared to older gay men, the probability of a virologic response to ART was reduced in single migrants, in older heterosexual men and in IDUs. Loss to follow-up was higher in single migrants and IDUs, and mortality was increased in older heterosexual men and IDUs. Socio-behavioral groups identified by LCA allow insights above what can be gleaned from traditional transmission groups, and may identify patients who could benefit from targeted interventions.