First Test of an Automated Detection Platform to Identify Risk of Decompensation in Elderly Patients

Abrar-Ahmad Zulfiqar; Orianne Vaudelle; Mohamed Hajjam; Dominique Letourneau; Jawad Hajjam; Sylvie Erve; Anna Karen Garate Escamilla; Amir Hajjam; Emmanuel Andres

doi:10.12890/2020_002102

First Test of an Automated Detection Platform to Identify Risk of Decompensation in Elderly Patients

Eur J Case Rep Intern Med. 2020 Dec 10;7(12):002102. doi: 10.12890/2020_002102. eCollection 2020.

Authors

Abrar-Ahmad Zulfiqar¹, Orianne Vaudelle², Mohamed Hajjam², Dominique Letourneau³, Jawad Hajjam⁴, Sylvie Erve⁴, Anna Karen Garate Escamilla⁵, Amir Hajjam⁵, Emmanuel Andres¹

Affiliations

¹ Service de Médecine Interne, Diabète et Maladies Métaboliques de la Clinique Médicale B, Hôpitaux Universitaires de Strasbourg and Equipe EA 3072 "Mitochondrie, Stress oxydant et Protection musculaire", Faculté de Médecine, Université de Strasbourg, Strasbourg, France.
² Predimed Technology, Schiltigheim, France.
³ Fondation de l'Avenir pour la Recherche Médicale Appliquée, Paris, 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-Montbéliard, Belfort, France.

Abstract

Introduction: We tested the MyPredi^™ e-platform which is dedicated to the automated, intelligent detection of situations posing a risk of decompensation in geriatric patients.

Objective: The goal was to validate the technological choices, to consolidate the system and to test the robustness of the MyPredi^™ e-platform through daily use.

Results: The telemedicine solution took 3,552 measurements for a hospitalized patient during her stay, with an average of 237 measurements per day, and issued 32 alerts, with an average of 2 alerts per day. The main risk was heart failure which generated the most alerts (n=13). The platform had 100% sensitivity for all geriatric risks, and had very satisfactory positive and negative predictive values.

Conclusion: The present experiment validates the technological choices, the tools and the solutions developed.

Learning points: Patients with chronic conditions can be monitored with telemedicine systems to optimise their management, particularly during the COVID-19 pandemic.The goal was to validate the technological choices, to consolidate the system and to test the robustness of the MyPredi^™ e-platform, through daily use in an elderly patient.The present experiment demonstrates the relevance of the technological choices, the tools and the solutions developed.

Keywords: GER-e-TEC study; MyPredi™ e-platform; Telemonitoring; geriatric risks.