ABSTRACT
The health emergency linked to the SARS-CoV-2 pandemic has highlighted problems in the health management of chronic patients due to their risk of infection, suggesting the need of new methods to monitor patients. People living with HIV/AIDS (PLWHA) represent a paradigm of chronic patients where an e-health-based remote monitoring could have a significant impact in maintaining an adequate standard of care. The key objective of the study is to provide both an efficient operating model to “follow” the patient, capture the evolution of their disease, and establish proximity and relief through a remote collaborative model. These dimensions are collected through a dedicated mobile application that triggers questionnaires on the basis of decision-making algorithms, tagging patients and sending alerts to staff in order to tailor interventions. All outcomes and alerts are monitored and processed through an innovative e-Clinical platform. The processing of the collected data aims into learning and evaluating predictive models for the possible upcoming alerts on the basis of past data, using machine learning algorithms. The models will be clinically validated as the study collects more data, and, if successful, the resulting multidimensional vector of past attributes will act as a digital composite biomarker capable of predicting HIV-related alerts. Design: All PLWH > 18 sears old and stable disease followed at the outpatient services of a university hospital (n = 1500) will be enrolled in the interventional study. The study is ongoing, and patients are currently being recruited. Preliminary results are yielding monthly data to facilitate learning of predictive models for the alerts of interest. Such models are learnt for one or two months of history of the questionnaire data. In this manuscript, the protocol—including the rationale, detailed technical aspects underlying the study, and some preliminary results—are described. Conclusions: The management of HIV-infected patients in the pandemic era represents a challenge for future patient management beyond the pandemic period. The application of artificial intelligence and machine learning systems as described in this study could enable remote patient management that takes into account the real needs of the patient and the monitoring of the most relevant aspects of PLWH management today. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
ABSTRACT
The aim of this study was to explore the psychological impact of the initial stage of the 2019 coronavirus (COVID-19) pandemic on people living with HIV (PLWH), a population at increased risk of psychological distress. PLWH participated in an online survey exploring demographic and clinical data, physical symptoms, contact history, knowledge and concerns, precautionary measures and additional information about COVID-19 during the first phase of the pandemic in Italy. The Impact of Event Scale-Revised (IES-R) (identifying the COVID-19 pandemic as a specific traumatic life event) and the Depression, Anxiety and Stress Scale (DASS-21) also formed part of the survey. Out of 98 participants, 45% revealed from mild to severe psychological impact from COVID-19 according to IES-R. A lower percentage, instead, complained of significant levels of depression (14%), anxiety (11%) or stress (6%) according to DASS-21. Higher education, being unemployed, number of perceived COVID-19 physical symptoms, concerns about risk of contracting COVID-19 and the pandemic situation in Italy, and needing additional information to prevent COVID-19 infection were positively associated to a higher risk of negative psychological impact. Moreover, among the participants, female gender, age, fewer years from HIV diagnosis and not being aware of their own viremia were associated to a higher risk of negative psychological outcomes. Almost half of our PLWH sample experienced significant levels of distress related to the COVID-19 pandemic. Women, elderly patients and those with recent HIV diagnosis appear to be the more psychologically fragile subgroups. Our findings could help identify patients most in need of psychological interventions to improve the wellbeing of PLWH.