CAPTURE – MG

Background

For myasthenia gravis (MG), several outcome measures are used to assess MG, such as the MG-QoL 15r and MG-ADL. Digital technologies as an alternative solution to support the monitoring and management of patients are becoming increasingly popular. Two promising applicable techniques are computer vision and vocal analysis, which both involve using machine learning algorithms applied to audio and video recordings. This data can be used in concert to monitor disease progression and track treatment response, assess the effectiveness of physical therapy or rehabilitation, and identify early signs of relapse, remotely —without the need to see a patient in person. Additionally, these technologies could provide patients with real-time feedback on their speech and motor functions, allowing them to better monitor their symptoms themselves, which improves self-management.

Study design

The aim of this study is to explore whether digital features of several typical MG symptoms—namely dysarthria, dysphonia, proximal arm weakness, and ptosis— can differentiate between participants with and without MG. In addition, we want to explore whether there is a correlation of those digital features with disease severity, quality-of-life and level of fatigue in MG patients.

For this study, each participant will need to visit the hospital once for a set of questionnaires and video and audio recordings. The visit will take approximately one hour.

Main inclusion criteria for MG participants

1. Age ≥ 18 years.
2. A clinical diagnosis of myasthenia gravis (ocular or generalized) as defined by the Dutch national guideline (category “definite” or “probable” MG).
3. Participants have at least one of the symptoms of interest, namely dysarthria, dysphonia, proximal arm weakness and/or ptosis.

Main inclusion criteria for non-MG participants

1. Participants are not diagnosed with and have no clinical suspicion of MG.
2. Participants do not have a medical history of any of the symptoms of interest (namely dysarthria, dysphonia, proximal arm fatigue and/or ptosis).

Enrollment

We aim to include 225 participants: 150 with MG and 75 without MG. If you are a patient with MG and would like to participate, and you have family members or friends without MG who are also interested in joining, we would be happy to hear from you. Of course, you are also very welcome to participate even if no one in your network is able or willing to join.

If you are interested in participating, please email: capture-mg@lumc.nl.

More information can be found here: CAPTURE – MG

Research team
Y.J.M. Campman, dr. M.R. Tannemaat, prof. dr. J.J.G.M. Verschuuren, B.M. Oskam