AARP, San Francisco, CA, USA.
Division of Operating Rooms, University of California San Francisco, San Francisco, CA, USA.
J Clin Monit Comput. 2019 Jun;33(3):481-491. doi: 10.1007/s10877-018-0181-9. Epub 2018 Jul 13.
This report presents a method for tracking Motor Evoked Potential (MEP) amplitudes over the course of a case using a moving least squares linear regression (LSMAs). During a case, newly obtained MEP amplitudes are compared to those predicted by a just previous linear regression (least squares moving average or LSMA). When detected by this comparison, a set criterion amplitude loss will then trigger linear regression of ensuing MEP amplitudes on an expanding step function which tracks the persistence of the amplitude loss for the remainder of the case. Three cases are presented. One in which the patient woke up with a newly acquired weakness in the left tibialis anterior and another in which MEP amplitudes were suddenly lost from the right foot, but after intervention, they were restored again. In a third case the patient again woke up with a new post-operative deficit, but MEP trial sampling had been more limited and variable than in the first two cases. When the linear trending method was applied to the affected myotome in the first case, the expanding step function regression was triggered after the moment of MEP loss and remained at a high level until the end of case. In the second case, the expanding step function regression was also triggered in the relevant myotome at the time of the reported MEP change, but diminished by end of case. In the third case the tracking method again successfully triggered a predictive R-Square despite the limited number of pre-event trials. The R-Square value of the expanding step function regression appears to have discriminative capability with regard to new post-op deficit. Given the importance of the intra-operative MEP for monitoring motor functioning and the high degree of variability that can affect it, the development of new quantitative, statistical methods to detect real from apparent MEP change will be necessary.
本报告介绍了一种使用移动最小二乘线性回归 (LSMAs) 在病例过程中跟踪运动诱发电位 (MEP) 幅度的方法。在病例中，将新获得的 MEP 幅度与之前的线性回归（最小二乘移动平均或 LSMA）预测值进行比较。当通过这种比较检测到设定的标准幅度损失时，随后的 MEP 幅度将在线性回归上进行线性回归，该线性回归使用扩展的阶跃函数，该函数跟踪幅度损失在病例其余部分的持续时间。呈现了三个案例。一个案例中，患者醒来时左侧胫骨前肌出现新的无力，另一个案例中，右脚的 MEP 幅度突然丢失，但经过干预后又恢复了。在第三个案例中，患者再次醒来时出现新的术后缺陷，但 MEP 试验采样比前两个案例更有限且更具变化性。当线性趋势方法应用于第一个案例中受影响的肌节时，扩展的阶跃函数回归在 MEP 损失的那一刻被触发，并在病例结束前保持在较高水平。在第二个案例中，在报告的 MEP 变化时，相关肌节也触发了扩展的阶跃函数回归，但在病例结束时减小。在第三个案例中，尽管预事件试验数量有限，跟踪方法再次成功触发了有预测性的 R-平方。扩展阶跃函数回归的 R-平方值似乎具有新的术后缺陷的区分能力。鉴于术中 MEP 对监测运动功能的重要性以及可能影响其的高度可变性，开发新的定量、统计方法来检测真正的 MEP 变化而不是表面的 MEP 变化将是必要的。
