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DIPS(基于肌张力障碍影像的刺激编程:一项前瞻性、随机、双盲交叉试验)

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

作者信息

Lange Florian, Roothans Jonas, Wichmann Tim, Gelbrich Götz, Röser Christoph, Volkmann Jens, Reich Martin

机构信息

Department of Neurology, University Hospital and Julius Maximilian University, Josef-Schneider-Straße 11, 97080, Würzburg, Germany.

Institute for Clinical Epidemiology and Biometry (ICE-B) at the University of Würzburg, Josef-Schneider-Straße 2, 97080, Würzburg, Germany.

出版信息

Neurol Res Pract. 2021 Dec 20;3(1):65. doi: 10.1186/s42466-021-00165-6.

Abstract

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).

摘要

引言

脑深部刺激内侧苍白球是治疗肌张力障碍的一种有效方法。然而,临床疗效存在很大差异,即使在经过严格筛选的原发性肌张力障碍患者中,也有高达25%的患者无反应。在一大群患者中,我们最近证明,苍白球脑深部电刺激治疗肌张力障碍的临床疗效差异在很大程度上可能是由苍白球区域内的确切位置和刺激范围导致的。在此,我们基于这些见解测试一种新的程控方法:我们首先通过汇总在一项多中心研究中收集的80多名患者的个体电极位置和激活组织体积,定义了抗肌张力障碍效应的概率图。随后,我们修改了算法,以便能够根据预期临床结果在计算机上测试初治患者的所有可能刺激设置,从而有可能为个体患者预测最佳刺激参数。

方法

在一项由德国教育与研究部资助的研究框架内,这种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测试。临床疗效的主要参数和主要终点基于在连续刺激4周后,由盲法医生根据临床肌张力障碍评定量表对两种干预措施(最佳临床设置和模型预测设置)的肌张力障碍严重程度进行评分。主要终点定义为“模型预测设置下的成功治疗”(是或否)。如果模型预测设置下的运动症状与临床设置相同或更好(绝对差异百分比的容差为5%),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耗以及医生的程控时间。

展望

我们设想,计算机引导的脑深部刺激计算机程控可能为肌张力障碍患者提供最佳刺激设置,而无需数月的程控负担。该研究方案旨在评估哪种程控方法在控制肌张力障碍患者的运动症状严重程度和改善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上注册(NCT05097001)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2529/8686267/f2d2a2892afe/42466_2021_165_Fig1_HTML.jpg

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