TOPICS: Health management of chronic patients due to their risk of infection; Health patient monitoring.

SOURCE: MDPI, February 2022;

HIV Patients’ Tracer for Clinical Assistance and Research during the COVID-19 Epidemic (INTERFACE): A Paradigm for Chronic Conditions

Antonella Cingolani 1,2 ; Konstantina Kostopoulou 3 ; Alice Luraschi 1,4 ; Aristodemos Pnevmatikakis 3,* ; Silvia Lamonica 1 ; Sofoklis Kyriazakos 3,5 ; Chiara Iacomini 1,4 ; Francesco Vladimiro Segala 2 ; Giulia Micheli 2 ; Cristina Seguiti 2 ; Stathis Kanavos 3 ; Alfredo Cesario 1,3 ; Enrica Tamburrini 1,2 ; Stefano Patarnello 2 ; Vincenzo Valentini 1,2,4 and Roberto Cauda 1,4

1   Fondazione Policlinico A. Gemelli IRCCS, 00168 Rome, Italy
2   Infectious Diseases Department, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
3   Innovation Sprint, 1200 Brussels, Belgium
4   Gemelli Generator, Fondazione Policlinico A. Gemelli IRCCS, 00168 Rome, Italy
5   BTECH, Department of Business Development and Technology, Aarhus University, 7400 Herning, Denmark
* Author to whom correspondence should be addressed.


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 learned 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.

Keywords: HIV; COVID-19; e-Clinical assistance; outcome prediction

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