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Open AccessArticle

Results of the “GER-e-TEC” Experiment Involving the Use of an Automated Platform to Detect the Exacerbation of Geriatric Syndromes

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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, 67000 Strasbourg, France
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Predimed Technology Society, 67300 Schiltigheim, France
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Service de Physiologie et d’Explorations Fonctionnelles, Hôpitaux Universitaires de Strasbourg et Equipe EA 3072 “Mitochondrie, Stress Oxydant et Protection Musculaire”, Faculté de Médecine-Université de Strasbourg, 67000 Strasbourg, France
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Fondation de l′Avenir pour la Recherche Médicale Appliquée Research Department, 75015 Paris, France
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Centre d′Expertise des TIC pour l′autonomie (CenTich) et Mutualité Française Anjou-Mayenne (MFAM)-Angers, 49000 Angers, France
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Laboratoire IRTES-SeT, Université de Technologie de Belfort-Montbéliard (UTBM), Belfort-Montbéliard, 90000 Belfort, France
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Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(12), 3836; https://doi.org/10.3390/jcm9123836
Received: 29 September 2020 / Revised: 17 November 2020 / Accepted: 24 November 2020 / Published: 26 November 2020
(This article belongs to the Section Epidemiology & Public Health)
Introduction: Telemedicine is believed to be helpful in managing patients suffering from chronic diseases, in particular elderly patients with numerous accompanying conditions. This was the basis for the “GERIATRICS and e-Technology (GER-e-TEC) study”, which was an experiment involving the use of the smart MyPredi™ e-platform to automatically detect the exacerbation of geriatric syndromes. Methods: The MyPredi™ 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. These alerts are issued in the event that the health of a patient deteriorates due to an exacerbation of their chronic diseases. An experiment was conducted between 24 September 2019 and 24 November 2019 to test this alert system. During this time, the platform was used on patients being monitored in an internal medicine unit at the University Hospital of Strasbourg. The alerts were compiled and analyzed in terms of sensitivity, specificity, and positive and negative predictive values with respect to clinical data. The results of the experiment are provided below. Results: A total of 36 patients were monitored remotely, 21 of whom were male. The mean age of the patients was 81.4 years. The patients used the telemedicine solution for an average of 22.1 days. The telemedicine solution took a total of 147,703 measurements while monitoring the geriatric risks of the entire patient group. An average of 226 measurements were taken per patient per day. The telemedicine solution generated a total of 1611 alerts while assessing the geriatric risks of the entire patient group. For each geriatric risk, an average of 45 alerts were emitted per patient, with 16 of these alerts classified as “low”, 12 classified as “medium”, and 20 classified as “critical”. In terms of sensitivity, the results were 100% for all geriatric risks and extremely satisfactory in terms of positive and negative predictive values. In terms of survival analysis, the number of alerts had an impact on the duration of hospitalization due to decompensated heart failure, a deterioration in the general condition, and other reasons. Conclusion: The MyPredi™ telemedicine system allows the generation of automatic, non-intrusive alerts when the health of a patient deteriorates due to risks associated with geriatric syndromes. View Full-Text
Keywords: remote monitoring; geriatric risks; MyPredi™ platform; GER-e-TEC study; prevention; elderly patients remote monitoring; geriatric risks; MyPredi™ platform; GER-e-TEC study; prevention; elderly patients
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MDPI and ACS Style

Zulfiqar, A.-A.; Vaudelle, O.; Hajjam, M.; Geny, B.; Talha, S.; Letourneau, D.; Hajjam, J.; Erve, S.; Hajjam El Hassani, A.; Andrès, E. Results of the “GER-e-TEC” Experiment Involving the Use of an Automated Platform to Detect the Exacerbation of Geriatric Syndromes. J. Clin. Med. 2020, 9, 3836.

AMA Style

Zulfiqar A-A, Vaudelle O, Hajjam M, Geny B, Talha S, Letourneau D, Hajjam J, Erve S, Hajjam El Hassani A, Andrès E. Results of the “GER-e-TEC” Experiment Involving the Use of an Automated Platform to Detect the Exacerbation of Geriatric Syndromes. Journal of Clinical Medicine. 2020; 9(12):3836.

Chicago/Turabian Style

Zulfiqar, Abrar-Ahmad; Vaudelle, Orianne; Hajjam, Mohamed; Geny, Bernard; Talha, Samy; Letourneau, Dominique; Hajjam, Jawad; Erve, Sylvie; Hajjam El Hassani, Amir; Andrès, Emmanuel. 2020. "Results of the “GER-e-TEC” Experiment Involving the Use of an Automated Platform to Detect the Exacerbation of Geriatric Syndromes" J. Clin. Med. 9, no. 12: 3836.

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