Glycemic Disorder Risk Remote Monitoring Program in the COVID-19 Very Elderly Patients: Preliminary Results - Zulfiqar, Abrar-Ahmad; Massimbo, Delwende Noaga Damien; Hajjam, Mohamed; Gény, Bernard; Talha, Samy; Hajjam, Jawad; Ervé, Sylvie; Hassani, Amir Hajjam El; Andrès, Emmanuel (Frontiers in Physiology, 2021)
Introduction – The coronavirus disease 2019 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,”. Methods – The MyPredi™ 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. The platform was used on COVID-19 patients being monitored in an internal medicine COVID-19 unit at the University Hospital of Strasbourg. Results – 10 older diabetic COVID-19 patients in total were monitored remotely, 6 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 “diabetes-hyperglycemia” risk, with an average of 20.3 alerts per patient and a standard deviation of 26.6. No alerts were emitted for the “diabetes-hypoglycemia” risk. In terms of sensitivity, the results were 100% for diabetes risks, and extremely satisfactory in terms of positive and negative predictive values. 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). Conclusion - To date, relatively few projects 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.