Capturing Key Symptoms Using Smartphone Recordings in Patients With Myasthenia Gravis (CAPTURE-MG)
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 225
- 试验地点
- 2
- 主要终点
- Differentiating between MG-patients and non-MG participants using digital features of dysarhtria, dysphonia, proximal arm fatigue and ptosis.
研究概览
简要总结
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.
详细描述
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 (four for MG participants, three 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 sharing of this information in this identifiable format.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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) as defined by the Dutch national guideline (category "definite" or "probable" MG).
- •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).
排除标准
- •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 Criteria for non-MG participants only:
- •1. Limitation of upper limb mobility or speech impairment of any cause.
结局指标
主要结局
Differentiating between MG-patients and non-MG participants using digital features of dysarhtria, dysphonia, proximal arm fatigue and ptosis.
时间窗: Assessed at a single time point during outpatient visit
Using machine-learning algorithms.
次要结局
- Correlating digital features of dysarthria, dysphonia, proximal arm fatigue and ptosis in MG patients with disease severity as measured by the MGC score.(Assessed at a single time point during outpatient visit)
- Correlating digital features of dysarthria, dysphonia, proximal arm fatigue and ptosis in MG patients with the impact of MG on daily activities as measured by the MG-ADL.(Assessed at a single time point during outpatient visit)
- The performance of automated signal processing of speech recordings collected through smartphone microphone for detection of dysarthria and dysphonia compared to clinical assessment.(Assessed at a single time point during outpatient visit)
- 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 compared to clinical assessment.(Assessed at a single time point during outpatient visit)
- The performance of automated measurement of ptosis-provoking exercises through computer vision techniques applied to smartphone camera recordings for detection of ptosis compared to clinical assessment.(Assessed at a single time point during outpatient visit)
- Correlating digital features of dysarthria, dysphonia, proximal arm fatigue and ptosis in MG patients with their level of fatigue as measured by the CIS-fatigue subscale.(Assessed at a single time point during outpatient visit)
研究者
Martijn R. Tannemaat, MD PhD
Principal investigator
Leiden University Medical Center