Conectar
We are in a booming era of artificial intelligence, particularly with the increased availability of technologies that can help generate content, such as ChatGPT. Healthcare institutions are discussing or have started utilizing these innovative technologies within their workflow. Major electronic health record vendors have begun to leverage large language models to process and analyze vast amounts of clinical natural language text, performing a wide range of tasks in healthcare settings to help alleviate clinicians' burden. Although such technologies can be helpful in applications such as patient education, drafting responses to patient questions and emails, medical record summarization, and medical research facilitation, there are concerns about the tools' readiness for use within the healthcare domain and acceptance by the current workforce. The goal of this article is to provide nurses with an understanding of the currently available foundation models and artificial intelligence tools, enabling them to evaluate the need for such tools and assess how they can impact current clinical practice. This will help nurses efficiently assess, implement, and evaluate these tools to ensure these technologies are ethically and effectively integrated into healthcare systems, while also rigorously monitoring their performance and impact on patient care. HIV-induced chronic inflammation, immune activation and combination antiretroviral therapy (cART) are linked with adverse metabolic changes known to cause cardiovascular adversities. This study evaluates the prevalence of lipodystrophy, and metabolic syndrome (MetS), and analyses risk factors in HIV-infected Ethiopians taking cART.
A multicentre cross-sectional study was conducted at tertiary-level hospitals. Eligible participants attending the HIV clinics were enrolled. Sociodemographic, anthropometric, clinical, HIV treatment variables, lipid profile, fasting blood glucose level, risk factors and components of MetS, also lipodystrophy, were studied. Data were analysed by SPSS statistical package V.25 with descriptive and analytical statistics. For multivariable analysis of risk factors, a logistic regression model was used. Results were presented in frequency and percentages, mean±SD, or median+IQR. Statistical significance was taken as p
Among 518 studied participants, two-thirds were females, and the mean age of the study population was 45 years (SD=11). The mean duration of cART was 10 years (SD=4). Median CD4 count was 460 cells/mm3. The prevalence of MetS according to the Adult Treatment Panel III (2005) criteria was 37.6%. In multivariable analysis, independent risk factors for MetS were age >45 years (aHR 1.8, 95% CI 1.2 to 2.4), female sex (aHR 1.8, 95% CI 1.1 to 2.8), body mass index (BMI)
Our study revealed 38% of the participants had MetS indicating considerable cardiovascular disease (CVD) risks. Independent risk factors for MetS were BMI≥25 kg/m2, efavirenz and lopinavir/ritonavir-based cART, female sex and age ≥45 years. In addition to prevention, CVD risk stratification and management will reduce morbidity and mortality in people with HIV infection.
FreshRSS 