Application of Targeted Reinnervation for People with Transradial Amputation

描述（由申请人提供）：经桡动脉截肢是最常见的上肢大截肢水平，在过去十年中，估计占美国平民上肢损失的40%，占美国退伍军人上肢损失的50%。大多数人经历经桡动脉截肢由于创伤，是年轻的，需要假肢，使他们能够恢复积极的，富有成效的生活方式。目前的假体不能提供足够的功能，并且更新的多功能假体（能够进行许多不同的手抓握）不能通过目前的控制策略来充分地控制。功能差是经桡动脉截肢者中假体排斥率高的主要因素，这导致这些人的残疾增加。我们已经开发出一种外科技术，称为靶向肌肉神经再支配（TMR），其中用于将重要控制信息传递到缺失肢体的切断神经被转移到新的“目标”肌肉。在神经移植后，每当个体试图移动他们失去的手臂时，目标肌肉就会收缩，产生可用于控制假肢中类似运动的肌电图信号。因此，TMR提供了对宝贵但以前不可用的控制信息的访问。由CBM-RIC开发的模式识别算法可以解码这些丰富的EMG信息，从而实现对更多功能的直观控制;用户只需尝试进行所需的手臂或手部运动。这种模式识别系统技术已经获得商业生产许可，并将在明年内用于临床实施。在较高水平的手臂截肢者中，将TMR与模式识别相结合，大大改善了对更多自由度的控制，并且对于用户来说，控制更容易，更直观。这项多中心研究的目的是开发经桡动脉TMR的手术技术，并通过进行临床试验比较模式识别控制（TMR前后）与传统控制策略（TMR前），评价TMR在经桡动脉截肢者中的受益。正中神经和尺神经，携带控制信息的内在的手肌肉参与许多手抓，将转移到前臂肌肉。受试者将在为期8周的家庭试验期间使用最先进的多功能假体（具有模式识别或常规控制）进行日常生活活动，随后进行全面的功能测试和用户调查。初步的尸体和手术研究表明，经桡动脉TMR是简单可行的，从更高级别的TMR截肢者的数据表明，当结合模式识别，TMR提供了改进的控制多个手的把握。我们希望TMR，与模式识别，将允许经桡截肢者控制更多的手抓住更容易和更直观。经桡神经TMR的发展将使这些人能够控制高度灵巧的假手。它将 无疑也会刺激对先进控制系统的新研究，使更先进的机电一体化设备的开发成为可能。

DESCRIPTION (provided by applicant): Transradial amputation is the most common level of major upper limb amputation, accounting for an estimated 40% of upper limb loss in the US civilian population and 50% in the US military veteran population over the last decade. Most individuals experience transradial amputation due to trauma, are young, and need prostheses that allow them to resume an active, productive lifestyle. Current prostheses do not provide adequate function, and newer, multifunction prostheses (that are capable of many different hand grasps) cannot be adequately controlled by current control strategies. Poor function is a major factor in high rates of prosthesis rejection in transradial amputees, which contributes to increased disability for these individuals. We have developed a surgical technique, called targeted muscle reinnervation (TMR) in which severed nerves that used to carry important control information to the missing limb are transferred to new 'target' muscles. After reinnervation, the target muscles contract whenever the individual attempts to move their missing arm, generating electrical EMG signals that can be used to control analogous movements in a prosthesis. TMR thus provides access to valuable but previously unavailable control information. Pattern recognition algorithms, developed by CBM-RIC, can decode this rich EMG information to enable intuitive control of many more functions; the user simply has to attempt to make the desired arm or hand movement. This pattern recognition system technology has been licensed for commercial production and will be available for clinical implementation within the next year. Combining TMR with pattern recognition in higher-level arm amputees has resulted in substantially improved control of more DOFs, and control is easier and more intuitive for the user. The goal of this multicenter study is to develop surgical techniques for transradial TMR and to evaluate the benefits of TMR in transradial amputees by performing clinical trials to compare pattern recognition control, before and after TMR, with a conventional control strategy (before TMR). The median and ulnar nerves, which carry control information for intrinsic hand muscles involved in many hand grasps, will be transferred to forearm muscles. Subjects will use a state-of-the-art multifunction prosthesis-with either pattern recognition or conventional control-in activities of daily living during 8-week home trials, followd by comprehensive functional testing and user surveys. Preliminary cadaver and surgical studies indicate that transradial TMR is simple and feasible; data from higher-level TMR amputees indicate that when combined with pattern recognition, TMR provides improved control of multiple hand grasps. We expect that TMR, with pattern recognition, will allow transradial amputees to control more hand grasps more easily and more intuitively. Development of transradial TMR will enable these individuals to control highly dexterous prosthetic hands. It will also undoubtedly stimulate new research into advanced control systems and enable the development of more advanced mechatronic devices.