12 research outputs found
The Appeal of German Mysticism to Religious Dissenters in Sixteenth-Century Germany: Mystical Books and the Life Stories of Their Readers
Association between exposure to antiretroviral drugs and the incidence of hypertension in HIV-positive persons: the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study
Prevalence and determinants of insufficient work ability in older HIV-positive and HIV-negative workers
The ‘COmorBidity in Relation to AIDS’ (COBRA) cohort: Design, methods and participant characteristics
Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIRaben-1 subtype B and non-subtype B receiving a salvage regimen
Objectives: The objective of this studywas to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. Methods: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large European databases. HIV-1 subtype B-infected patients were used as the derivation dataset and HIV- 1 non-B-infected patients were used as the validation dataset. The adjusted association of each mutation with week 8 HIV RNA change from baseline was analysed by linear regression. A prediction model was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0). Results: TCEs were selected from 681 subtype B-infected and 199 non-B-infected adults. Accompanying drugs were NRTIs in 87%, NNRTIs in 27%and raltegravir ormaraviroc or enfuvirtide in 53%. The predictionmodel included weighted protease mutations, HIV RNA, CD4 and activity of accompanying drugs. The model's association with week 8 HIV RNA change in the subtype B (derivation) set was R2=0.47 [average squared error (ASE)=0.67, P>10-6]; in the non-B (validation) set, ASE was 0.91. Accuracy investigated by means of area under the receiver operating characteristic curves with a binary response (above the threshold value of HIV RNA reduction) showed that our finalmodel outperformed models with existing interpretation systems in both training and validation sets. Conclusions: A model with a new darunavir-weighted mutation score outperformed existing GISs in both B and non-B subtypes in predicting virological response to darunavir
Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIRaben-1 subtype B and non-subtype B receiving a salvage regimen
Objectives: The objective of this studywas to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. Methods: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large European databases. HIV-1 subtype B-infected patients were used as the derivation dataset and HIV- 1 non-B-infected patients were used as the validation dataset. The adjusted association of each mutation with week 8 HIV RNA change from baseline was analysed by linear regression. A prediction model was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0). Results: TCEs were selected from 681 subtype B-infected and 199 non-B-infected adults. Accompanying drugs were NRTIs in 87%, NNRTIs in 27%and raltegravir ormaraviroc or enfuvirtide in 53%. The predictionmodel included weighted protease mutations, HIV RNA, CD4 and activity of accompanying drugs. The model's association with week 8 HIV RNA change in the subtype B (derivation) set was R2=0.47 [average squared error (ASE)=0.67, P>10-6]; in the non-B (validation) set, ASE was 0.91. Accuracy investigated by means of area under the receiver operating characteristic curves with a binary response (above the threshold value of HIV RNA reduction) showed that our finalmodel outperformed models with existing interpretation systems in both training and validation sets. Conclusions: A model with a new darunavir-weighted mutation score outperformed existing GISs in both B and non-B subtypes in predicting virological response to darunavir