Convenient Quantification of Myopathic Change in Muscle Via Electrical Impedance Myography
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 150
- 试验地点
- 1
- 主要终点
- Pathology-specific penalized regression development. Predictive algorithms connecting EIM data sets to MRI outcomes representative of muscle pathology, including muscle cross-sectional area, fat content, edema, and fiber disorganization.
研究概览
简要总结
This study is being done to further develop a device, the mScan, to measure muscle health as compared to measurements of muscle health using MRI (magnetic resonance imaging). This device is held against the skin and uses Electrical Impedance Myography (EIM). EIM uses a very small, noninvasive (e.g. no needles), brief (about 6 seconds), and painless electrical current to measure the muscle. The investigators will look at how the mScan predicts the muscle measurements seen on MRI in people with and without muscle disease. The investigators hope that this can be used in the future as a quick, convenient and less time-consuming way than MRI to assess muscle health. This could be used to measure how well treatments for different muscle disorders are working over a period of time.
详细描述
Magnetic resonance imaging (MRI) is an important clinical tool for tracking skeletal muscle disease and response to therapy in a variety of conditions ranging from muscular dystrophy to myositis. MRI can serve as a surrogate measure of skeletal muscle pathology; it can quantify atrophy, edema, fatty infiltration, and myofiber disorganization, obviating the need for biopsy. There is little question that tracking MRI changes will speed therapeutic clinical trials in many muscle diseases; its use has been strongly encouraged. Although MRI can provide excellent assessment of muscle condition, MRI has many drawbacks including high cost, general inconvenience, need for the subject to lie flat without moving, limited evaluation of upper extremity muscles, need for detailed image analysis to distill complex imaging data down to a simple value for disease tracking, difficulty obtaining repeated measurements in a clinical trial, and challenges in standardization of protocols across institutions. These limitations prevent MRI from being an easily applied biomarker for assessment of muscle health and disease status. A technology that offers compositional information similar to MRI but that overcomes MRI's many drawbacks could serve as an extraordinary powerful biomarker in regular patient care and clinical therapeutic trials.
Electrical impedance myography (EIM) is such a technology. In fact, EIM is currently being used as biomarker in a number of neuromuscular disorders. In EIM, using a small handheld device, a weak, directionally focused, multi-frequency electrical current is applied to a muscle, resulting surface voltages are measured, and impedance values are derived. Alterations in these values provide insight into the condition of muscle, including atrophy, edema, fatty infiltration, and myofiber disorganization. In addition to ALS, EIM has already shown considerable value as a biomarker in a number of disorders including muscular dystrophy, myositis, and simple deconditioning. In sum, the investigators hypothesize that EIM has the potential to serve as a proxy for MRI, providing much of the same information but with far greater speed and convenience, lower cost, smaller size, greater flexibility and tolerability and without the need for cumbersome image analysis.
While much data has been acquired showing EIM is sensitive to muscle health, there is only sparse data relating EIM directly to MRI. Given the complexity of both EIM and MRI, applying machine learning approaches to these data sets can serve as a means for establishing a relationship between these two technologies. This would allow EIM to serve as an extremely convenient tool for tracking muscle health and potentially as a biomarker in future clinical therapeutic trials and day-to-day patient care.
Research Question: Can EIM supplement and potentially substitute for MRI in the assessment of primary diseases of skeletal muscle (myopathies)?
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 89 Years(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Ages 18-89
- •Evidence of a primary myopathic condition as determined by detailed chart review, including results of genetic testing, serological data, or previous muscle biopsy
排除标准
- •Inability to lie flat or history of claustrophobia
- •>1+ lower extremity edema
- •Presence of multiple other pathologies affecting lower extremity muscles to be studied
- •Pregnancy
- •Contraindications for MRI scanning - e.g. MRI incompatible pacemaker, deep brain stimulator, or lower extremity hardware
- •Contraindications to undergo DXA Scan
- •Any studies/scans with a radioisotope within the past 15 days
- •Any imaging with radiographic contrast in the past 7 days
- •Weight greater than 450 lbs
- •Calcium supplements or antacids containing calcium in the past 24 hours
- •Severe obesity with BMI > 35 kg/m2, given difficulties fitting in MRI scanner and impact of severe obesity on EIM data
- •Chronic skin conditions with ulcerations which would interfere with EIM electrode contact or be uncomfortable for the participant
研究组 & 干预措施
Healthy Control
干预措施: Electrical Impedance Myography (Device)
Myopathy
Participants with Myopathies
干预措施: Electrical Impedance Myography (Device)
结局指标
主要结局
Pathology-specific penalized regression development. Predictive algorithms connecting EIM data sets to MRI outcomes representative of muscle pathology, including muscle cross-sectional area, fat content, edema, and fiber disorganization.
时间窗: Two years
A. EIM data preparation: Outputs including resistance, reactance, and phase values at 41 frequencies between 10 kHz to 10 MHz in both longitudinal and transverse directions, will be used. Raw EIM data will be filtered using an automated algorithm that deletes statistically defined errant individual frequency points. B. MRI analysis of data after preparation C. Penalized regression approach-basic approach using least absolute shrinkage and selection operator (Lasso)21 for assessing the entire multifrequency set up to 10 MHz will be used to develop predictive models of EIM with further nested cross validation. D: This trained model will then be applied specifically to the remaining 20% of data which will serve as the test set and final values in RMSE will then be calculated for this second data set. The investigators will define success as achieving an R value 0.6 or greater, which is considered a moderate-to-strong association for most clinical outcomes
次要结局
未报告次要终点
研究者
Pushpa Narayanaswami
Vice-Chair of Clinical Operations; Professor of Neurology
Beth Israel Deaconess Medical Center
