DIPS(基于肌张力障碍图像的刺激程序设计:一项前瞻性、随机、双盲交叉试验)。

Florian Lange, Jonas Roothans, Tim Wichmann, Götz Gelbrich, Christoph Röser, Jens Volkmann, Martin Reich
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引用次数: 1

摘要

简介:深部脑刺激内苍白球是治疗肌张力障碍的有效方法。然而,临床结果有很大的可变性,即使在高度选择的原发性肌张力障碍患者中,也有高达25%的无反应。在一个大的患者队列中,我们最近证明了苍白侧DBS治疗肌张力障碍的不同临床结果可能在很大程度上取决于苍白侧区域的确切位置和刺激量。在这里,我们基于这些见解测试了一种新的编程方法:我们首先通过聚合多个中心收集的> 80名患者的单个电极位置和激活的组织体积来定义抗张力作用的概率图。随后,我们修改了算法,以便能够根据预期的临床结果在计算机上测试新生患者的所有可能的刺激设置,从而有可能预测个体患者的最佳可能刺激参数。方法:在bmbf资助的研究框架内,这一基于计算机预测肌张力障碍患者最佳刺激参数的概念将在一项随机对照交叉研究中进行验证。临床疗效和主要终点的主要参数是基于连续刺激4周后两种干预措施(最佳临床设置和模型预测设置)的临床肌张力障碍评定量表所反映的盲法医师对肌张力障碍严重程度的评定。主要终点定义为“模型预测设置的成功治疗”(是或否)。如果具有模型预测设置的运动症状与临床设置相等或更好(绝对百分比差异的5%公差),则该值为“是”。次要终点将包括生活质量测量、神经刺激系统的计算能量消耗和医生编程时间。展望:我们设想,计算机引导的深部脑刺激程序可以为肌张力障碍患者提供最佳的刺激设置,而无需数月的编程会话负担。该研究方案旨在评估哪种编程方法在控制运动症状严重程度和改善肌张力障碍患者的生活质量方面更有效(最佳临床设置和模型预测设置)。试验注册于2021年10月27日在ClinicalTrials.gov注册(NCT05097001)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

DIPS (Dystonia Image-based Programming of Stimulation: a prospective, randomized, double-blind crossover trial).

DIPS (Dystonia Image-based Programming of Stimulation: a prospective, randomized, double-blind crossover trial).

DIPS (Dystonia Image-based Programming of Stimulation: a prospective, randomized, double-blind crossover trial).

Introduction: Deep brain stimulation of the internal globus pallidus is an effective treatment for dystonia. However, there is a large variability in clinical outcome with up to 25% non-responders even in highly selected primary dystonia patients. In a large cohort of patients we recently demonstrated that the variable clinical outcomes of pallidal DBS for dystonia may result to a large degree by the exact location and stimulation volume within the pallidal region. Here we test a novel approach of programing based on these insights: we first defined probabilistic maps of anti-dystonic effects by aggregating individual electrode locations and volumes of tissue activated of > 80 patients collected in a multicentre effort. We subsequently modified the algorithms to be able to test all possible stimulation settings of de novo patients in silico based on the expected clinical outcome and thus potentially predict the best possible stimulation parameters for the individual patients.

Methods: Within the framework of a BMBF-funded study, this concept of a computer-based prediction of optimal stimulation parameters for patients with dystonia will be tested in a randomized, controlled crossover study. The main parameter for clinical efficacy and primary endpoint is based on the blinded physician rating of dystonia severity reflected by Clinical Dystonia Rating Scales for both interventions (best clinical settings and model predicted settings) after 4 weeks of continuous stimulation. The primary endpoint is defined as "successful treatment with model predicted settings" (yes or no). The value is "yes" if the motor symptoms with model predicted settings are equal or better (tolerance 5% of absolute difference in percentages) to clinical settings. Secondary endpoints will include measures of quality of life, calculated energy consumption of the neurostimulation system and physician time for programming.

Perspective: We envision, that computer-guided deep brain stimulation programming in silico might provide optimal stimulation settings for patients with dystonia without the burden of months of programming sessions. The study protocol is designed to evaluate which programming method is more effective in controlling motor symptom severity and improving quality of life in dystonia (best clinical settings and model predicted settings). Trial registration Registered with ClinicalTrials.gov on Oct 27, 2021 (NCT05097001).

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