Clinical Trial Details
— Status: Recruiting
Administrative data
NCT number |
NCT05097001 |
Other study ID # |
DIPS |
Secondary ID |
|
Status |
Recruiting |
Phase |
N/A
|
First received |
|
Last updated |
|
Start date |
November 1, 2021 |
Est. completion date |
December 2024 |
Study information
Verified date |
April 2024 |
Source |
Wuerzburg University Hospital |
Contact |
Martin Reich, Dr. |
Phone |
0931-201-0 |
Email |
Reich_M1[@]ukw.de |
Is FDA regulated |
No |
Health authority |
|
Study type |
Interventional
|
Clinical Trial Summary
The primary objective of this exploratory study is to prospectively evaluate the feasibility
of image-guided programming of pallidal deep brain stimulation (DBS) for dystonia. The
dystonias are a heterogeneous group of movement disorders that share the core clinical
feature of abnormal involuntary muscle contractions in common. Pallidal DBS is an established
therapy for severe cases with an average improvement in dystonia severity of 50-60%. However,
outcomes are variable and difficult to predict, and clinical trials report up to 25% of
Nonresponders. Variability in electrode placement and inappropriate stimulation settings may
account for much of this variability in outcome. In addition, improvement in dystonia is
delayed, often days to weeks after a change in DBS therapy, complicating programming.
Our group recently developed a computer model to predict optimal individualized stimulation
settings in patients based on the outcome of a large cohort of of chronically treated
patients. In-silico testing showed a 16.3% better mean group improvement with
computer-assisted programming compared with physician-assisted programming and a dramatic
reduction in non-responders (from 25% to 5%).
In this prospective study, the computer model will be compared in a randomized, controlled,
and double blinded setting against best clinical DBS programming. The primary outcome will be
a responder analysis in which dystonia severity will be compared between conventional
clinical and model-based programming will be compared.
Description:
Dystonia is a neurological syndrome characterized by involuntary, sustained, or repetitive
muscle contractions of opposing muscle groups that cause twisting movements and abnormal
postures. Dystonias meet the prevalence criterion of a rare disorder, with prevalence
estimates ranging from 0.2-5/100,000 for infantile or juvenile forms to 3-732/100,000 for
dystonias manifesting in adults. In addition to motor impairments and mobility limitations,
patients - especially young patients - suffer from non-motor symptoms: depression (> 15%),
anxiety (> 25%), decreased sleep quality (> 70%), and pain (> 75%). In addition, there is a
high risk of economic impairment, including job loss (> 55%), and decreased productivity are
serious social consequences (> 50%). However, disability is usually secondary to motor
impairment, and adequate treatment of motor symptoms can lead to profound improvements in
quality of life (Skogseid et al. 2007). Currently, there is no cure for dystonia, and
pharmacological symptom treatment is limited. Deep brain stimulation (DBS) of the internal
globus pallidus (GPi) is a recommended therapy for severe dystonia with Class I evidence for
safety and efficacy (Vidailhet et al. 2005; Volkmann et al. 2012; Volkmann et al. 2014).
However, clinical trials report up to 25% non-responders (<25% motor improvement) (Volkmann
et al. 2012; Volkmann et al. 2014). Variability in electrode placement and inappropriate
stimulation settings may explain much of this outcome variability (Reich et al. 2019). In
addition, dystonia improves with a delay, often weeks after initiation or days after
switching neurostimulation therapy, complicating clinical programming for DBS (Kupsch et al.
2011). Our group recently presented a novel approach based on empirical knowledge from a
large cohort of chronically treated patients. We defined probabilistic maps of antidystonic
effects using electrode position and volumes of tissue activation (VTA) from >100 patients.
This method predicts a 16.3% better mean group improvement with computer-selected electrode
choices compared with physician programming and a reduced proportion of non-responders from
25% to <5% (Reich et al. 2019). This potential advantage of computer-assisted programming
capabilities will be tested in the study described here.