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Background: Less frequent follow-up visits may reduce the burden on people living with HIV (PLHIV) and health care facilities. We aimed to assess trends in follow-up visits and survival outcomes among PLHIV in Asia and Australasia. Settings: PLHIV enrolled in TREAT Asia HIV Observational Database (TAHOD) or Australian HIV Observational Database (AHOD) from 2008 to 2017 were included. Methods: Follow-up visits included laboratory testing and clinic visit dates. Visit rates and survival were analyzed using repeated measure Poisson regression and competing risk regression, respectively. Additional analyses were limited to stable PLHIV with viral load ,1000 copies/mL and self-reported adherence $95%. Results: We included 7707 PLHIV from TAHOD and 3289 PLHIV from AHOD. Visit rates were 4.33 per person-years (/PYS) in TAHOD and 3.68/PYS in AHOD. Both TAHOD and AHOD showed decreasing visit rates in later calendar years compared with that in years 2008-2009 (P, 0.001 for both cohorts). Compared with PLHIV with 2 visits, those with ≥4 visits had poorer survival: TAHOD ≥4 visits, subhazard ratio (SHR) = 1.88, 95% confidence interval (CI): 1.16 to 3.03, P = 0.010; AHOD ≥4 visits, SHR = 1.80, 95% CI: 1.10 to 2.97, P = 0.020; whereas those with ≤1 visit showed no differences in mortality. The association remained evident among stable PLHIV: TAHOD ≥4 visits, SHR = 5.79, 95% CI: 1.84 to 18.24, P = 0.003; AHOD ≥4 visits, SHR = 2.15, 95% CI: 1.20 to 3.85, P = 0.010, compared with 2 visits. Conclusions: Both TAHOD and AHOD visit rates have declined. Less frequent visits did not affect survival outcomes; however, poorer health possibly leads to increased follow-up and higher mortality. Reducing visit frequency may be achievable among PLHIV with no other medical complications. [ABSTRACT FROM AUTHOR]

To investigate the association between the UGT1A1∗6 (G71R) and UGT1A1∗28 (promoter (TA)7-repeat) genotypes and hyperbilirubinaemia in Thai patients treated with indinavir, and characterize the inhibition of human UGTs by indinavir in vitro.Ninety-six Thai HIV patients receiving indinavir, 800 mg t.i.d. or 800 mg b.i.d. ‘boosted’ with ritonavir (100 mg b.i.d.), had serum bilirubin levels measured to 24 weeks post-treatment and were genotyped for UGT1A1∗6 and UGT1A1∗28. The inhibition selectivity and kinetics of indinavir were determined using a panel of recombinant human UGTs.UGT1A1∗6 and UGT1A1∗28 frequencies in the Thai patients were 10.4% and 15.6%, respectively. Total, conjugated (direct) and unconjugated (indirect) serum bilirubin concentrations increased significantly at 24 weeks of indinavir treatment for all four genotypes, with a trend towards higher levels depending on the number of UGT1A1 mutant alleles; ∗6/∗28 > ∗6 > ∗28 > reference. The hazards ratio (HR) for serious hyperbilirubinaemia (total bilirubin > 2.5 mg/dl) at week 24 was statistically significant only in those patients carrying the UGT1A1∗6 (HR 2.87) and UGT1A1∗6/∗28 (HR 11.42) genotypes. The Ki values for indinavir inhibition of UGT1A1 and UGT1A1∗6 were 4.1 and 10.7 μmol/l respectively. However, indinavir was also shown to inhibit other human UGTs, notably UGT1A3 and UGT1A7.In contrast to Caucasian HIV-infected patients treated with indinavir, the promoter polymorphism (UGT1A1∗28) is of less significance than the coding region (UGT1A1∗6) mutation as a risk factor for hyperbilirubinaemia. The Ki values determined for indinavir inhibition of UGT1A1 are consistent with an interaction in vivo, with an additive effect in patients with already impaired bilirubin glucuronidation activity. [ABSTRACT FROM AUTHOR]

Studies the pharmacokinetics of indinavir/ritonariv in combination with efavirenz in HIV-1-infected subjects. Nucleoside-sparing combination; Advantage of one-daily dosage.

Objective: The optimum selection and sequencing of combination antiretroviral therapy to maintain viral suppression can be challenging. The HIV Resistance Response Database Initiative has pioneered the development of computational models that predict the virological response to drug combinations. Here we describe the development and testing of random forest models to power an online treatment selection tool., Methods: Five thousand, seven hundred and fifty-two treatment change episodes were selected to train a committee of 10 models to predict the probability of virological response to a new regimen. The input variables were antiretroviral treatment history, baseline CD4 cell count, viral load and genotype, drugs in the new regimen, time from treatment change to follow-up and follow-up viral load values. The models were assessed during cross-validation and with an independent set of 50 treatment change episodes by plotting receiver-operator characteristic curves and their performance compared with genotypic sensitivity scores from rules-based genotype interpretation systems., Results: The models achieved an area under the curve during cross-validation of 0.77-0.87 (mean = 0.82), accuracy of 72-81% (mean = 77%), sensitivity of 62-80% (mean = 67%) and specificity of 75-89% (mean = 81%). When tested with the 50 test cases, the area under the curve was 0.70-0.88, accuracy 64-82%, sensitivity 62-80% and specificity 68-95%. The genotypic sensitivity scores achieved an area under the curve of 0.51-0.52, overall accuracy of 54-56%, sensitivity of 43-64% and specificity of 41-73%., Conclusion: The models achieved a consistent, high level of accuracy in predicting treatment responses, which was markedly superior to that of genotypic sensitivity scores. The models are being used to power an experimental system now available via the Internet.

Objective: To examine the impact of viral hepatitis co-infection on HIV disease outcomes following commencement of combination antiretroviral therapy in a developing country setting., Methods: HIV RNA suppression, CD4 cell count recovery, and HIV disease progression were examined within a cohort of Thai HIV-infected patients enrolled in eight HIV-NAT randomized controlled trials of antiretroviral therapy (n = 692). Hepatitis B virus (HBV) and hepatitis C virus (HCV) testing was performed on stored serum., Results: Mean age was 32.3 years, 52% were male, 11% had CDC category C HIV disease at baseline, and 22% had received prior antiretroviral therapy. Prevalence of HBV, HCV and HBV/HCV co-infection was 8.7, 7.2 and 0.4%, respectively. Median HIV RNA reductions (log10 copies/ml) were approximately 1.5 for HIV, HIV-HBV, HIV-HCV subgroups from week 4 up to week 48. Mean increases in CD4 cell count were significantly lower among HIV-HBV and HIV-HCV subgroups at week 4 (HIV, 62 x 10(6) cells/l; HIV-HBV, 29 x 10(6) cells/l; HIV-HCV, 33 x 10(6) cells/l), however, by week 48 CD4 cell increases were similar (HIV, 115 x 10(6) cells/l; HIV-HBV, 113 x 10(6) cells/l; HIV-HCV, 97 x 10(6) cells/l). Cox regression analyses showed that HIV-HBV or HIV-HCV co-infection were not associated with a CD4 cell count increase of 100 x 10(6) cells/l over 48 weeks. Estimated progression to AIDS event or death at week 48 was 3.3% (95% confidence interval, 2.0-5.1%) for HIV, 6.7% (2.5-14.6%) for HIV-HBV, and 8.0% (2.2-20.5%) for HIV-HCV subgroups (P > 0.05)., Conclusions: An early delayed CD4 count recovery among HIV/viral hepatitis co-infected patients was not sustained, and was not associated with increased HIV disease progression.