A comparison of computational models with and without genotyping for prediction of response to second-line HIV therapy.

OBJECTIVES: We compared the use of computational models developed with and without HIV genotype vs. genotyping itself to predict effective regimens for patients experiencing first-line virological failure. METHODS: Two sets of models predicted virological response for 99 three-drug regimens for patients on a failing regimen of two nucleoside/nucleotide reverse transcriptase inhibitors and one nonnucleoside reverse transcriptase inhibitor in the Second-Line study. One set used viral load, CD4 count, genotype, plus treatment history and time to follow-up to make its predictions; the second set did not include genotype. Genotypic sensitivity scores were derived and the ranking of the alternative regimens compared with those of the models. The accuracy of the models and that of genotyping as predictors of the virological responses to second-line regimens were compared. RESULTS: The rankings of alternative regimens by the two sets of models were significantly correlated in 60-69% of cases, and the rankings by the models that use a genotype and genotyping itself were significantly correlated in 60% of cases. The two sets of models identified alternative regimens that were predicted to be effective in 97% and 100% of cases, respectively. The area under the receiver-operating curve was 0.72 and 0.74 for the two sets of models, respectively, and significantly lower at 0.55 for genotyping. CONCLUSIONS: The two sets of models performed comparably well and significantly outperformed genotyping as predictors of response. The models identified alternative regimens predicted to be effective in almost all cases. It is encouraging that models that do not require a genotype were able to predict responses to common second-line therapies in settings where genotyping is unavailable.

Missed opportunities to offer HIV tests to high-risk groups during general practitioners' STI-related consultations: an observational study.

OBJECTIVES: Prior research has shown that Dutch general practitioners (GPs) do not always offer HIV testing and the number of undiagnosed HIV patients remains high. We aimed to further investigate the frequency and reasons for (not) testing for HIV and the contribution of GPs to the diagnosis of HIV infections in the Netherlands. DESIGN: Observational study. SETTING: (1) Dutch primary care network of 42-45 sentinel practices where report forms during sexually transmitted infection (STI)-related consultations were routinely collected, 2008-2013. (2) Dutch observational cohort with medical data of HIV-positive patients in HIV care, 2008-2013. OUTCOME MEASURES: The proportion of STI-related consultations in patients from high-risk groups tested for HIV, with additional information requested from GPs on HIV testing preconsultation or postconsultation for whom HIV testing was indicated, but not performed. Next, information was collected on the profile of HIV-positive patients entering specialised HIV care following diagnosis by GPs. RESULTS: Initially, an HIV test was reported (360/907) in 40% of STI-related consultations in high-risk groups. Additionally, in 26% of consultations an HIV test had been performed in previous or follow-up consultations or at different STI-care facilities. The main reasons for not testing were perceived insignificant risk; 'too' recent risk according to GPs or the reluctance of patients. The initiative of the patient was a strong determinant for HIV testing. GPs diagnosed about one third of all newly found cases of HIV. Compared with STI clinics, HIV-positive patients diagnosed in general practice were more likely to be older, female, heterosexual male or sub-Saharan African. CONCLUSIONS: In one-third of the STI-related consultations of persons from high-risk groups, no HIV test was performed in primary care, which is lower than previously reported. Risk-based testing has intrinsic limitations and implementation of new additional strategies in primary care is warranted.

Acute hepatitis C in the Netherlands: characteristics of the epidemic in 2014.

Within the Dutch Acute HCV in HIV Study, a surveillance system was initiated to estimate the incidence of hepatitis C virus (HCV) infections in 2014. Following the Dutch HIV treatment guidelines, HIV-positive men having sex with men (MSM) in 19 participating centers were screened. Ninety-nine acute HCV infections were reported, which resulted in a mean incidence of 11 per 1000 patient-years of follow-up. Unfortunately, the HCV epidemic among Dutch HIV-positive MSM is not coming to a halt.

Gender difference in HIV-1 RNA viral loads.

OBJECTIVES: To test and characterize the dependence of viral load on gender in different countries and racial groups as a function of CD4 T-cell count. METHODS: Plasma viral load data were analysed for > 30,000 HIV-infected patients attending clinics in the USA [HIV Insight (Cerner Corporation, Vienna, VA, USA) and Plum Data Mining LLC (East Meadow, NY, USA) databases] and the Netherlands (Athena database; HIV Monitoring Foundation, Amsterdam, Netherlands). Log-normal regression models were used to test for an effect of gender on viral load while adjusting for covariates and allowing the effect to depend on CD4 T-cell count. Sensitivity analyses were performed to test the robustness of conclusions to assumptions regarding viral loads below the lower limit of quantification (LLOQ). RESULTS: After adjusting for covariates, women had (nonsignificantly) lower viral loads than men (HIV Insight: -0.053 log(10) HIV-1 RNA copies/mL, P = 0.202; Athena: -0.005 log(10) copies/mL, P = 0.667; Plum: -0.072 log(10) copies/mL, P = 0.273). However, further investigation revealed that the gender effect depended on CD4 T-cell count. Women had consistently higher viral loads than men when CD4 T-cell counts were at most 50 cells/microL, and consistently lower viral loads than men when CD4 T-cell counts were greater than 350 cells/microL. These effects were remarkably consistent when estimated independently for the racial groups with sufficient data available in the HIV Insight and Plum databases. CONCLUSIONS: The consistent relationship between gender-related differences in viral load and CD4 T-cell count demonstrated here explains the diverse findings previously published.