HIV Drug-Resistant Patient Information Management, Analysis, and Interpretation

"She tells me the HIV is eating my brains": barriers and facilitators to antiretroviral therapy adherence among Eswatini adolescents living with HIV

Despite the successful rollout of antiretroviral therapy (ART) and positive ART outcomes in the Kingdom of Eswatini, adolescents still present poor ART outcomes including low viral load suppression and suboptimal ART adherence. The aim of the study was to explore the perceptions of adolescents living with HIV (ALHIV) on the barriers and facilitators to ART adherence in Eswatini. We conducted a qualitative study using in-depth interviews among 29 ALHIV and on ART in Eswatini in December 2023. Adolescents aged 10-19 years who were aware of their HIV status were recruited purposively from five Teen Clubs in the Hhohho region. Six barriers to ART were reported by participants namely perceived stigma and discrimination, competing demands between ART schedules and their personal and social lives, medication issues, health facility factors, lack of transport and food, and diminishing support from caregivers. The main facilitators of ART adherence were having a social support system, status disclosure, privacy, HIV and ART knowledge, and motivation to stay alive. Supportive environments are crucial to enhance ART adherence among ALHIV. These can be promoted through multi-component interventions that target status disclosure, increase knowledge of HIV and ART, ensure privacy and address stigma and discrimination.

Utilizing Big Data analytics and electronic health record data in HIV prevention, treatment, and care research: a literature review

Propelled by the transformative power of modern information and communication technologies, digitalization of data, and the increasing affordability of high-performance computing, Big Data science has brought forth revolutionary advancement in many areas of business, industry, health, and medicine. The HIV research and care service community is no exception to the benefits from the availability and utilization of Big Data analytics. Electronic health record (EHR) data (e.g., administrative and billing data, electronic medical records, or other digital records of information pertinent to individual or population health) are an essential source of health and disease outcome data because of the large amount of real-world, comprehensive, and often longitudinal data, which provide a good opportunity for leveraging advanced Big Data analytics in addressing challenges in HIV prevention, treatment, and care. This review focuses on studies that apply Big Data analytics to EHR data with aims to synthesize the HIV-related issues that EHR data studies can tackle, identify challenges in the utilization of EHR data in HIV research and practice, and discuss future needs and directions that can realize the promising potential role of Big Data in ending the HIV epidemic.

Concordance of genotypic resistance interpretation algorithms in HIV-1 infected patients: An exploratory analysis in Greece

PURPOSE

Genotypic resistance-related mutations in HIV-1 disease are often difficult to interpret. Different algorithms have been developed to provide meaningful application into clinical context. We aimed to compare, for the first time in Greece, the results of genotypic resistance derived from three interpretation algorithms.

METHODS

The sequences of 120 HIV 1-infected patients were tested for genotypic resistance to 19 antiretroviral (ARV) drugs (n = 2280 sequences). The interpretation results of Rega, ANRS and ViroSeq algorithms were compared.

RESULTS

Complete concordance was found for 2/19 ARV drugs, namely lamivudine and emptricitabine. Concordance was high for nucleoside reverse transcriptase inhibitors (NRTIs) and low for protease inhibitors (PIs). In inter-algorithm pairs, agreement was high between Rega and ViroSeq (kappa = 0.701), especially by ARV class, namely NRTIs (k = 0.869) and NNRTIs (k = 0.562). The only exception was noted for rilpivirine, where agreement was higher between ANRS and Rega (k = 0.410) compared to other inter-algorithm pairs (k = 0.018-0.055). By contrast, for PIs all comparisons yielded concordance equivalent to chance (k = 0.000).

CONCLUSIONS

Our exploratory analysis provided evidence of significant inter-algorithm discordances, especially for PIs and NNRTIs highlighting the importance of matching the results of different algorithms to achieve optimized risk stratification. Ongoing research could assist clinical physicians in interpreting complex genotypic resistance patterns.