Evaluation of a programming algorithm for deep brain stimulation in dystonia used in a double-blind, sham-controlled multicenter study

Neurol Res Pract. 2019 Sep 24:1:25. doi: 10.1186/s42466-019-0032-2. eCollection 2019.

Abstract

Background: Programming deep brain stimulation in dystonia is difficult because of the delayed benefits and absence of evidence-based guidelines. Therefore, we evaluated the efficacy of a programming algorithm applied in a double-blind, sham-controlled multicenter study of pallidal deep brain stimulation in dystonia.

Methods: A standardized monopolar review to identify the contact with the best acute antidystonic effect was applied in 40 patients, who were then programmed 0.5 V below the adverse effect threshold and maintained on these settings for at least 3 months, if tolerated. If no acute effects were observed, contact selection was based on adverse effects or anatomical criteria. Three-year follow-up data was available for 31 patients, and five-year data for 32 patients. The efficacy of the algorithm was based on changes in motor scores, adverse events, and the need for reprogramming.

Results: The mean (±standard deviation) dystonia motor score decreased by 73 ± 24% at 3 years and 63 ± 38% at 5 years for contacts that exhibited acute improvement of dystonia (n = 17) during the monopolar review. Contacts without acute benefit improved by 58 ± 30% at 3 years (n = 63) and 53 ± 31% at 5 years (n = 59). Interestingly, acute worsening or induction of dystonia/dyskinesia (n = 9) correlated significantly with improvement after 3 years, but not 5 years.

Conclusions: Monopolar review helped to detect the best therapeutic contact in approximately 30% of patients exhibiting acute modulation of dystonic symptoms. Acute improvement, as well as worsening of dystonia, predicted a good long-term outcome, while induction of phosphenes did not correlate with outcome.

Trial registration: ClinicalTrials.gov NCT00142259.

Keywords: Deep brain stimulation; Dystonia; Long-term outcome; Pallidum; Programming algorithm.

Associated data

  • ClinicalTrials.gov/NCT00142259