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

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

doi:10.3389/fphys.2021.749731

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

Front Physiol. 2021 Oct 27:12:749731. doi: 10.3389/fphys.2021.749731. eCollection 2021.

Authors

Abrar-Ahmad Zulfiqar¹, Delwende Noaga Damien Massimbo², Mohamed Hajjam², Bernard Gény³, Samy Talha³, Jawad Hajjam⁴, Sylvie Ervé⁴, Amir Hajjam El Hassani⁵, Emmanuel Andrès^{1

3}

Affiliations

¹ Service 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.
² Predimed Technology Society, Schiltigheim, France.
³ Faculté 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.
⁴ Centre d'Expertise des TIC pour l'Autonomie (CenTich) et Mutualité Française Anjou-Mayenne (MFAM)-Angers, Angers, France.
⁵ Laboratoire IRTES-SeT, Université de Technologie de Belfort-Montbéliard (UTBM), Belfort, France.

Abstract

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. 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. 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. 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. Results: 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). Conclusion: 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.

Keywords: COVID-19; GER-e-TEC study; MyPrediTM platform; diabetes risk; elderly patients; prevention; remote monitoring.