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

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

doi:10.3389/fphys.2021.749731

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 MyPrediTM e-platform to automatically detect the exacerbation of glycemic disorder risk in COVID-19 older diabetic patients.Methods: The MyPrediTM 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.

Highlights

Relatively few projects and trials in diabetic patients have been run within the “telemedicine 2.0” setting, using AI (Artificial Intelligence), ICT (Information and Communication Technologies) and the Web 2.0
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
10 diabetic elderly patients affected by COVID-19 disease were monitored remotely, while there was no refusal

Summary

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 MyPrediTM e-platform to automatically detect the exacerbation of glycemic disorder risk in COVID-19 older diabetic patients. 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) In this context, telemedicine may be an effective approach to solve problems of education, compliance, and monitoring and provider access (Andrès et al, 2018a). The use of “Web 2.0” technologies in health/medicine care or in telemedicine is referred to as “Health 2.0” or “Medicine 2.0,” and “telemedicine 2.0.”

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Discussion

Conclusion