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

  • Abrar-Ahmad Zulfiqar 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
  • Orianne Vaudelle Predimed Technology, Schiltigheim, France
  • Mohamed Hajjam Predimed Technology, Schiltigheim, France
  • Dominique Letourneau Fondation de l'Avenir pour la Recherche Médicale Appliquée, Paris, France
  • Jawad Hajjam Centre d'Expertise des TIC pour l'autonomie (CenTich) et Mutualité Française Anjou-Mayenne (MFAM) – Angers, Angers, France
  • Sylvie Ervé Centre d'Expertise des TIC pour l'autonomie (CenTich) et Mutualité Française Anjou-Mayenne (MFAM) – Angers, Angers, France
  • Anna Karen Garate Escamilla Laboratoire IRTES-SeT, Université de Technologie de Belfort-Montbéliard (UTBM), Belfort-Montbéliard, Belfort, France
  • Amir Hajjam Laboratoire IRTES-SeT, Université de Technologie de Belfort-Montbéliard (UTBM), Belfort-Montbéliard, Belfort, France
  • Emmanuel Andrès 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

Keywords

Telemonitoring, geriatric risks, MyPredi, e-platform, GER-e-TEC study

Abstract

Introduction: We tested the MyPrediTM 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 MyPrediTM 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.

 

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References

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  • Published: 2020-12-10

    Issue: Vol 7 No 12 (view)

    Section: Articles

    How to cite:
    1.
    Zulfiqar A-A, Vaudelle O, Hajjam M, Letourneau D, Hajjam J, Ervé S, Garate Escamilla AK, Hajjam A, Andrès E. First Test of an Automated Detection Platform to Identify Risk of Decompensation in Elderly Patients . EJCRIM 2020;7 doi:10.12890/2020_002102.

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