The main objective is to explore whether digital features of dysarthria, dysphonia, proximal arm fatigue and ptosis can differentiate between participants with and without MG.
ID
Source
Brief title
Condition
- Neuromuscular disorders
Synonym
Research involving
Sponsors and support
Intervention
Outcome measures
Primary outcome
The primary endpoint is to explore whether digital features of dysarthria,
dysphonia, proximal arm fatigue and ptosis can differentiate between
participants with and without MG.
Secondary outcome
a) To assess the correlation between digital features of dysarthria, dysphonia,
proximal arm fatigue and ptosis in MG patients and disease severity as measured
by the MGC score.
b) To assess the correlation between digital features of dysarthria, dysphonia,
proximal arm fatigue and ptosis in MG patients and their quality-of-life as
measured by the MG-ADL questionnaire.
c) To assess the performance of automated signal processing of speech
recordings collected through smartphone microphone for detection of dysarthria
and dysphonia, in comparison to assessment by accredited clinicians.
d) To assess the performance of automated measurement of proximal arm-fatiguing
exercises through computer vision techniques applied to smartphone camera
recordings for detection of proximal arm muscle weakness and fatigability, in
comparison to assessment by accredited clinicians.
e) To assess the performance of automated measurement of ptosis-provoking
exercises through computer vision techniques applied to smartphone camera
recordings for detection of ptosis, in comparison to assessment by accredited
clinicians.
f) To assess the correlation between digital features of dysarthria, dysphonia,
proximal arm fatigue and ptosis in MG patients and their level of fatigue as
measured by the Checklist Individual Strength (CIS-) fatigue subscale.
Background summary
Myasthenia Gravis (MG) is a chronic antibody-mediated auto-immune disease
affecting the neuromuscular junction. MG is characterized by fluctuating
weakness and fatigability of the skeletal muscles. While a subset of patients
(15%) experience only ocular symptoms, the majority (85%) experience
generalized manifestations. Several outcome measures are used to assess MG in
clinical trials and in practice, such as the MG-QoL 15r and MG-ADL. Digital
technologies as an alternative solution to support the monitoring and
management of patients with neurological disorders, are becoming increasingly
popular. Digital medicine, specifically mobile health applications, has the
potential to support the management of MG by providing remote patient
monitoring and data collection. Two promising applicable techniques are
computer vision and vocal analysis, which both involve using machine learning
algorithms applied to either images and videos captured by the smartphone
camera or audio recorded by the smartphone microphone, respectively. Computer
vision can be used to monitor MG patients for ocular symptoms such as diplopia
or ptosis or may be used to assess physical activity, endurance and mobility in
MG patients. Audio or speech analysis solutions hold the potential to identify
and quantify speech disturbances in MG, such as dysarthria and dysphonia. These
data can be used in concert to monitor disease progression, track treatment
response, assess the effectiveness of physical therapy or rehabilitation, and
identify early signs of relapse, remotely, without the need for live clinical
support. Additionally, these technologies could provide patients with real-time
feedback on their speech and motor functions to help monitor symptoms and
self-assess disease status, adjust communication strategies, improve
self-management, and reduce the burden of frequent clinic visits. It is
hypothesized that there are distinct digital biomarkers that differentiate MG
patients from healthy controls and that these biomarkers will correlate with MG
severity scores and quality of life. We expect that a remote solution that
empowers patients to track their disease status and facilitates the
documentation of their experiences, would offer noteworthy advantages.
Study objective
The main objective is to explore whether digital features of dysarthria,
dysphonia, proximal arm fatigue and ptosis can differentiate between
participants with and without MG.
Study design
This study will make use of a cross-sectional design of MG patients and non-MG
participants to quantitatively assess key MG symptoms, and to explore the
applicability of machine learning algorithms to their measurement.
Study burden and risks
Due to the cross-sectional design, participants will only have to visit Leiden
University Medical Center (LUMC) once. For patients already treated in the
LUMC, we will try to align this visit with a standard clinical appointment.
After inclusion, all baseline data, consisting of demographics, clinical
history and a number of questionnaires (three for MG participants, one for
non-MG participants), will be collected. The symptom-specific assessments are
performed in a standard order, with the most fatiguing task (i.e. proximal arm
fatigue static assessment) last. We estimate the visit will take a total of 60
minutes.
This study is considered to be low risk. Withholding pyridostigmine for a
limited period is part of standard care of MG (before investigations or
clinical assessments) and does not affect long term clinical outcome. MG
participants will consent to withhold pyridostigmine for 12 hours prior to the
study visit if they are on this treatment and restart it after the visit. As
this is a non-interventional, observational study where only
questionnaire-based and non-contact digital data are being collected, the only
source of marginal risk relates to data protection and confidentiality,
including arrangements for the transfer and storage of data. Given it would not
be possible to deidentify the digital audio or video data while maintaining the
requisite integrity for data analysis, we will seek explicit consent for the
use of this information in this identifiable format.
Albinusdreef 2
Leiden 2300 RC
NL
Albinusdreef 2
Leiden 2300 RC
NL
Listed location countries
Age
Inclusion criteria
In order to be eligible to participate in this study, a subject must meet all
of the following criteria:
- Age >= 18 years
- Ability to understand the requirements of the study and provide written
informed consent.
Inclusion criteria for MG participants only
- A clinical diagnosis of myasthenia gravis (ocular or generalized) with the
typical fluctuating muscle weakness and at least one of the following:
--- positive serologic test for AChR or MuSK antibodies;
--- an abnormal electrodiagnostic test: repetitive nerve stimulation (RNS) or
single-fiber electromyography (SFEMG).
- MGFA Clinical Classification of disease severity I-IV.
- Subjects have at least one of the symptoms of interest (namely dysarthria,
dysphonia, proximal arm fatigue and/or ptosis).
Inclusion criteria for non-MG participants only
- Subjects are not diagnosed with and have no clinical suspicion of MG.
- Subjects do not have a medical history of any of the symptoms of interest
(namely dysarthria, dysphonia, proximal arm fatigue and/or ptosis).
Exclusion criteria
A potential subject who meets any of the following criteria will be excluded
from participation in this study:
- Not willing to be audio-recorded for the study assessments.
- Not willing to be video-recorded for the study assessments.
- Subjects currently taking part in a clinical trial of an Investigational
Medicinal Product.
- Subjects who have used an immediate release pyridostigmine-based medication
in the 12 hours prior to their participation and participants on prolonged
release pyridostigmine.
- Subjects have cognitive or physical limitations that, in the opinion of the
investigator, limits the subject's ability to complete study procedures.
Exclusion criteria for MG participants only
- Subjects with an upper-limb amputation or who are non-verbal.
- Subjects with a diagnosed neurological disease resulting in muscle weakness,
other than MG.
Exclusion criterion for non-MG participants only
- Limitation of upper limb mobility or speech impairment of any cause.
Design
Recruitment
Medical products/devices used
metc-ldd@lumc.nl
metc-ldd@lumc.nl
Followed up by the following (possibly more current) registration
No registrations found.
Other (possibly less up-to-date) registrations in this register
No registrations found.
In other registers
| Register | ID |
|---|---|
| CCMO | NL86693.058.24 |