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Clinical Trial Summary

The implementation of an mechanical in-exsufflator device (MI-E) requires specific expertise because it is a complex device that requires fine-tuning of the settings according to different clinical situations to optimize its effectiveness. Generally, it is performed by experienced physiotherapists in neuromuscular disease reference centers or directly at home via medical-technical home care providers. Treatment data is recorded by the machine at each MI-E session, which may be daily or less frequent, depending on the patient's dependency. All of this information can be accessed by manually downloading the data from the SD card that comes with each MI-E machine. Therefore, the retrieval of this information systematically requires the visit of staff to the patient's home. To date, compliance with these devices is not regularly measured since there is no means of telecommunication allowing remote monitoring of these therapies, whereas technological development in the field of remote monitoring has allowed remote monitoring of patients with sleep apnea syndrome treated with continuous positive airway pressure (CPAP) and, more recently, of some patients with chronic respiratory insufficiency treated with invasive ventilation (NIV). These developments are transforming on the one hand the follow-up of patients under NIV at home by the medical and paramedical teams and on the other hand the financial coverage by the health insurance organizations (ETAPES programs). Within the framework of NIV therapy, we think that remote monitoring of the quality of the sessions, i.e. measurement of peak expiratory flow, insufflated volumes, frequency and duration of the sessions, could facilitate and improve the follow-up of these patients for the medical-technical providers, the expert physiotherapists and the doctors of the reference centers. It is still too early to assume the extent to which data from remote monitoring of MI-E devices would improve patient follow-up. Nevertheless, given the increasing number of devices installed over the past several years, it is likely that the issue of telemonitoring will become a central issue. Thus, in this observational trial, we propose to evaluate the feasibility of a simple system of remote monitoring of MI-E devices in non-therapy-naive patients, with the objective of assessing the barriers and limitations of remote monitoring in this population. Primary aim is to evaluate the feasibility of remote monitoring of data from the MI-E device used in the patient's home in neuromuscular diseases. Patients will be identified by the investigators using the AGIR à dom software package (medical-technical follow-up file). If the patient accepts, the information and no-objection form will be sent to them electronically or by mail following this call, and at least 3 days before their scheduled appointment. During the patient's usual follow-up visit, if the patient does not object to participating in the study, AGIR staff in dom will install the device. This visit will take place in the patient's home. During this visit, a SanDisk (SD) Eye-Fi SDHC 4GB + WiFi Class4 memory card will be inserted into the port provided, in place of the memory card already present in the MI-E device. Then a Raspberry Pi 4 Model B will be placed in the room where the MI-E device is normally used by the patient, and connected to a power source (accessible electrical outlet in the room). The wifi SD card, which uses the device's power supply, will communicate with the Raspberry Pi via the wifi network and upload the recorded data each time the MI-E device is used. After 90 days, a routine recovery visit will be scheduled. AGIR à dom staff will replace the wifi SD card installed during the D0 visit with the standard SD card originally provided with the MI-E device. The data locally on the SD Wifi card will then be downloaded for analysis and comparison with the data being uploaded


Clinical Trial Description

Perspectives Democratizing remote monitoring of cough devices would: Potentially decrease the costs associated with data retrieval Encourage the establishment of centers of expertise where expert practitioners could follow multiple patients (e.g., at the provider's site). To study the relationship between certain data from the MI-E (use, adjustment of settings, peak expiratory flow or insufflated volume). The development of an automated analysis system for predictive data (increase in bronchial congestion, inefficiency of the device) to optimize the follow-up of these patients. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT05723289
Study type Observational
Source KerNel Biomedical
Contact Marius G Lebret, PhD
Phone +33618561003
Email mariuslebret@gmail.com
Status Recruiting
Phase
Start date June 2023
Completion date December 2023

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