Abstract
Background: Deep brain stimulation (DBS) has become a standard treatment for advanced stages of Parkinson's disease, essential tremor, and dystonia. In addition to the correct surgical device implantation, effective programming is regarded to be the most important factor for clinical outcome. Despite established strategies for adjusting neurostimulation, DBS programming remains time- and resource-consuming. Although kinematic and neuronal biosignals have recently been examined as potential feedback for closed-loop DBS (CL-DBS), there is an ongoing need for programming strategies to adapt the stimulation parameters and electrode configurations accurately and effectively.
Methods: Here, we tested the usefulness of a patient-rated visual analog scale (VAS) for real-time adjustment of DBS parameters. The stimulation parameters (contact and amplitude) in Parkinson's patients with STN-DBS (n = 17) were optimized based on the patient's subjective VAS rating. A Minkowski distance (Md) was calculated to compare the individual combination of contact selection and amplitude to the stimulation parameters that resulted from classical programming based on clinical signs and symptoms.
Results: We found no statistically significant difference between VAS-based and classical programming in regard to the specific contact or amplitude used or in regard to the clinical disease severity (UPDRS).
Conclusions: Our data suggest that VAS-based and classical programming strategies both lead to similar short-term results. Although further research will be required to assess the validity of VAS-based DBS programming, our results support the investigation of the patient's subjective rating as an additional and valid feedback signal for individualized DBS adjustment.
Dokumententyp: | Zeitschriftenartikel |
---|---|
Publikationsform: | Publisher's Version |
Keywords: | Deep brain stimulation (DBS); closed loop; Parkinson's disease (PD); visual analog scale (VAS); Minkowski distance |
Fakultät: | Medizin |
Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
URN: | urn:nbn:de:bvb:19-epub-87459-9 |
ISSN: | 1664-2295 |
Sprache: | Englisch |
Dokumenten ID: | 87459 |
Datum der Veröffentlichung auf Open Access LMU: | 25. Jan. 2022, 09:24 |
Letzte Änderungen: | 06. Apr. 2022, 16:40 |