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Abstract Background Accurate interpretation of HIV drug resistance (HIVDR) testing is challenging, yet important for patient care. We compared genotyping interpretation, based on the Stanford University HIV Drug Resistance Database (Stanford HIVdb), and virtual phenotyping, based on the Janssen Diagnostics BVBA’s vircoTYPE™ HIV-1, and investigated their level of agreement in antiretroviral (ARV) naive patients in Asia, where non-B subtypes predominate. Methods Sequences from 1301 ARV-naive patients enrolled in the TREAT Asia Studies to Evaluate Resistance – Monitoring Study (TASER-M) were analysed by both interpreting systems. Interpretations from both Stanford HIVdb and vircoTYPE™ HIV-1 were initially grouped into 2 levels: susceptible and non-susceptible. Discrepancy was defined as a discordant result between the susceptible and non-susceptible interpretations from the two systems for the same ARV. Further analysis was performed when interpretations from both systems were categorised into 3 levels: susceptible, intermediate and resistant; whereby discrepancies could be categorised as major discrepancies and minor discrepancies. Major discrepancy was defined as having a susceptible result from one system and resistant from the other. Minor discrepancy corresponded to having an intermediate interpretation in one system, with a susceptible or resistant result in the other. The level of agreement was analysed using the prevalence adjusted bias adjusted kappa (PABAK). Results Overall, the agreement was high, with each ARV being in “almost perfect agreement”, using Landis and Koch’s categorisation. Highest discordance was observed for efavirenz (75/1301, 5.8%), all arising from susceptible Stanford HIVdb versus non-susceptible vircoTYPE™ HIV-1 predictions. Protease Inhibitors had highest level of concordance with PABAKs all above 0.99, followed by Nucleoside Reverse Transcriptase Inhibitors with PABAKs above 0.97 and non-NRTIs with the lowest PABAK of 0.88. The 68/75 patients with discordant efavirenz results harboured the V179D/E mutations compared to 7/1226 with no efavirenz discrepancy (p-value Conclusions The two systems agreed well with lowest concordance observed for efavirenz. When interpreting HIVDR, especially in non-B subtypes, clinical correlation is crucial, in particular when efavirenz resistance is interpreted based on V179D/E.

Abstract Background The aim of this study was to examine the relationship between trends in CD4 counts (slope) and HIV viral load (VL) after initiation of combination antiretroviral treatment (cART) in Asian patients in The TREAT Asia HIV Observational Database (TAHOD). Methods Treatment-naive HIV-infected patients who started cART with three or more and had three or more CD4 count and HIV VL tests were included. CD4 count slopes were expressed as changes of cells per microliter per year. Predictors of CD4 count slopes from 6 months after initiation were assessed by random-effects linear regression models. Results A total of 1676 patients (74% male) were included. The median time on cART was 4.2 years (IQR 2.5-5.8 years). In the final model, CD4 count slope was associated with age, concurrent HIV VL and CD4 count, disease stage, hepatitis B or C co-infection, and time since cART initiation. CD4 count continues to increase with HIV VL up to 20 000 copies/mL during 6-12 months after cART initiation. However, the HIV VL has to be controlled below 5 000, 4 000 and 500 copies/mL for the CD4 count slope to remain above 20 cells/microliter per year during 12-18, 18-24, and beyond 24 months after cART initiation. Conclusions After cART initiation, CD4 counts continued to increase even when the concurrent HIV VL was detectable. However, HIV VL needed to be controlled at a lower level to maintain a positive CD4 count slope when cART continues. The effect on long-term outcomes through the possible development of HIV drug resistance remains uncertain.

Abstract Background To assess the risk and the prognostic significance of tuberculosis (TB) diagnosis in patients from The TREAT Asia HIV Observational Database, a multi-centre prospective cohort of HIV-infected patients receiving HIV care in the Asia-Pacific region. Methods The risk of TB diagnosis after recruitment was assessed in patients with prospective follow-up. TB diagnosis was fitted as a time-dependent variable in assessing overall survival. Results At baseline, 22% of patients were diagnosed with TB. TB incidence was 1.98 per 100 person-years during follow up, with predictors including younger age, lower recent CD4 count, duration of antiretroviral treatment, and living in high TB burden countries. Among 3279 patients during 6968 person-years, 142 died (2.04 per 100 person-years). Compared to patients with CDC category A or B illness only, mortality was marginally higher in patients with single Non-TB AIDS defining illness (ADI), or TB only (adjusted HR 1.35, p = 0.173) and highest in patients with multiple non-TB AIDS or both TB and other ADI (adjusted HR 2.21, p < 0.001). Conclusion The risk of TB diagnosis was associated with increasing immunodeficiency and partly reduced by antiretroviral treatment. The prognosis of developing TB appeared to be similar to that following a diagnosis of other non-TB ADI.