![]() ![]() Thus, telemedicine is likely to provide assistance and even optimize the management of these chronic conditions, in particular by avoiding certain emergencies and repeated hospitalizations, as demonstrated in the field of heart failure and diabetes through the use of remote monitoring or remote follow-up ( Chaudhry et al., 2010 Anker et al., 2011 Andrès et al., 2018c Seferovic et al., 2019 Zulfiqar et al., 2019). In this context, telemedicine may be an effective approach to solve problems of education, compliance, and monitoring and provider access ( Andrès et al., 2018a). Intensive glucose control has been shown to delay or prevent the development of micro- and macro-vascular complications related to diabetes ( Andrès et al., 2018a). 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. 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).Ĭonclusion: This work is a pilot study with preliminary results. For the sensitivity of alerts emitted, the results were extremely satisfactory, and also in terms of positive and negative predictive values. In our study, we did not note any hypoglycemia, so the system emitted any alerts. 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. The patients used the telemedicine solution for an average of 14.5 days. The mean age of the patients was 84.1 years. Results: 10 older diabetic COVID-19 patients in total were monitored remotely, six of whom were male. The alerts were compiled and analyzed in terms of sensitivity, specificity, positive and negative predictive values with respect to clinical data. 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. An experiment was conducted between December 14th, 2020 and February 25th, 2021 to test this alert system. ![]() Methods: The MyPredi TM 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. ![]() This was the basis for the “GER-e-TEC COVID study,” an experiment involving the use of the smart MyPredi TM e-platform to automatically detect the exacerbation of glycemic disorder risk in COVID-19 older diabetic patients. Introduction: 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. 5Laboratoire IRTES-SeT, Université de Technologie de Belfort-Montbéliard (UTBM), Belfort, France.4Centre d’Expertise des TIC pour l’Autonomie (CenTich) et Mutualité Française Anjou-Mayenne (MFAM)-Angers, Angers, France.3Faculté de Médecine-Université de Strasbourg, Service de Physiologie et d’Explorations Fonctionnelles, Hôpitaux Universitaires de Strasbourg et Equipe EA 3072 “Mitochondrie, Stress Oxydant et Protection Musculaire,” Strasbourg, France.2Predimed Technology Society, Schiltigheim, France.1Service de Médecine Interne, Diabète et Maladies Métaboliques de la Clinique Médicale B, Hôpitaux Universitaires de Strasbourg et Equipe EA 3072 “Mitochondrie, Stress Oxydant et Protection Musculaire,” Faculté de Médecine-Université de Strasbourg, Strasbourg, France.Abrar-Ahmad Zulfiqar 1*, Delwende Noaga Damien Massimbo 2, Mohamed Hajjam 2, Bernard Gény 3, Samy Talha 3, Jawad Hajjam 4, Sylvie Ervé 4, Amir Hajjam El Hassani 5 and Emmanuel Andrès 1,3 ![]()
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