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- Oette, M, et al.
(författare)
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Efficacy of antiretroviral therapy switch in HIV-infected patients: a 10-year analysis of the EuResist Cohort
- 2012
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Ingår i: Intervirology. - : S. Karger AG. - 1423-0100 .- 0300-5526. ; 55:2, s. 160-166
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Tidskriftsartikel (refereegranskat)abstract
- <i>Introduction:</i> Highly active antiretroviral therapy (HAART) has been shown to be effective in many recent trials. However, there is limited data on time trends of HAART efficacy after treatment change. <i>Methods:</i> Data from different European cohorts were compiled within the EuResist Project. The efficacy of HAART defined by suppression of viral replication at 24 weeks after therapy switch was analyzed considering previous treatment modifications from 1999 to 2008. <i>Results:</i> Altogether, 12,323 treatment change episodes in 7,342 patients were included in the analysis. In 1999, HAART after treatment switch was effective in 38.0% of the patients who had previously undergone 1–5 therapies. This figure rose to 85.0% in 2008. In patients with more than 5 previous therapies, efficacy rose from 23.9 to 76.2% in the same time period. In patients with detectable viral load at therapy switch, the efficacy rose from 23.3 to 66.7% with 1–5 previous treatments and from 14.4 to 55.6% with more than 5 previous treatments. <i>Conclusion:</i> The results of this large cohort show that the outcome of HAART switch has improved considerably over the last years. This result was particularly observed in the context after viral rebound. Thus, changing HAART is no longer associated with a high risk of treatment failure.
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- Zazzi, M, et al.
(författare)
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Predicting response to antiretroviral treatment by machine learning: the EuResist project
- 2012
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Ingår i: Intervirology. - : S. Karger AG. - 1423-0100 .- 0300-5526. ; 55:2, s. 123-127
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Tidskriftsartikel (refereegranskat)abstract
- For a long time, the clinical management of antiretroviral drug resistance was based on sequence analysis of the HIV genome followed by estimating drug susceptibility from the mutational pattern that was detected. The large number of anti-HIV drugs and HIV drug resistance mutations has prompted the development of computer-aided genotype interpretation systems, typically comprising rules handcrafted by experts via careful examination of in vitro and in vivo resistance data. More recently, machine learning approaches have been applied to establish data-driven engines able to indicate the most effective treatments for any patient and virus combination. Systems of this kind, currently including the Resistance Response Database Initiative and the EuResist engine, must learn from the large data sets of patient histories and can provide an objective and accurate estimate of the virological response to different antiretroviral regimens. The EuResist engine was developed by a European consortium of HIV and bioinformatics experts and compares favorably with the most commonly used genotype interpretation systems and HIV drug resistance experts. Next-generation treatment response prediction engines may valuably assist the HIV specialist in the challenging task of establishing effective regimens for patients harboring drug-resistant virus strains. The extensive collection and accurate processing of increasingly large patient data sets are eagerly awaited to further train and translate these systems from prototype engines into real-life treatment decision support tools.
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