AUTHOR=Zulfiqar Abrar-Ahmad , Massimbo Delwende Noaga Damien , Hajjam Mohamed , Gény Bernard , Talha Samy , Hajjam Jawad , Ervé Sylvie , Hassani Amir Hajjam El , Andrès Emmanuel 

TITLE=Glycemic Disorder Risk Remote Monitoring Program in the COVID-19 Very Elderly Patients: Preliminary Results

JOURNAL=Frontiers in Physiology

VOLUME=12

YEAR=2021

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2021.749731

DOI=10.3389/fphys.2021.749731

ISSN=1664-042X

ABSTRACT=<p><bold>Introduction:</bold> The coronavirus disease 2019 (COVID-19) pandemic has necessitated the use of new technologies and new processes to care for hospitalized patients, including diabetes patients. This was the basis for the “GER-e-TEC COVID study,” an experiment involving the use of the smart <italic>MyPredi</italic><sup>TM</sup> e-platform to automatically detect the exacerbation of glycemic disorder risk in COVID-19 older diabetic patients.</p><p><bold>Methods:</bold> The <italic>MyPredi</italic><sup>TM</sup> platform is connected to a medical analysis system that receives physiological data from medical sensors in real time and analyzes this data to generate (when necessary) alerts. An experiment was conducted between December 14th, 2020 and February 25th, 2021 to test this alert system. During this time, the platform was used on COVID-19 patients being monitored in an internal medicine COVID-19 unit at the University Hospital of Strasbourg. The alerts were compiled and analyzed in terms of sensitivity, specificity, positive and negative predictive values with respect to clinical data.</p><p><bold>Results:</bold> 10 older diabetic COVID-19 patients in total were monitored remotely, six of whom were male. The mean age of the patients was 84.1 years. The patients used the telemedicine solution for an average of 14.5 days. 142 alerts were emitted for the glycemic disorder risk indicating hyperglycemia, with an average of 20.3 alerts per patient and a standard deviation of 26.6. In our study, we did not note any hypoglycemia, so the system emitted any alerts. For the sensitivity of alerts emitted, the results were extremely satisfactory, and also in terms of positive and negative predictive values. In terms of survival analysis, the number of alerts and gender played no role in the length of the hospital stay, regardless of the reason for the hospitalization (COVID-19 management).</p><p><bold>Conclusion:</bold> This work is a pilot study with preliminary results. To date, relatively few projects and trials in diabetic patients have been run within the “telemedicine 2.0” setting, particularly using AI, ICT and the Web 2.0 in the era of COVID-19 disease.</p>