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SCIENTIFIC LITERATURE · MED

Parkinson Hastalığı için Derin Beyin Stimülasyonunun Klinik Olarak Uygulanan Algılama Tabanlı İlk Programlama: Retrospektif Bir Çalışma.

Clinically Implemented Sensing-Based Initial Programming of Deep Brain Stimulation for Parkinson's Disease: A Retrospective Study.

Makine çevirisi · Editoryal inceleme bekliyor

Neuromodulation · 2024-12-02

DOI: 10.1016/j.neurom.2024.11.002PMID: 39625426

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ObjectivesInitial deep brain stimulation (DBS) programming using a monopolar review is time-consuming, subjective, and burdensome. Incorporating neurophysiology has the potential to expedite, objectify, and automatize initial DBS programming. We aimed to assess the feasibility and performance of clinically implemented sensing-based initial DBS programming for Parkinson's disease (PD).Materials and methodsWe conducted a single-center retrospective study in 15 patients with PD (25 hemispheres) implanted with a sensing-enabled neurostimulator in whom initial subthalamic nucleus/globus pallidus pars interna DBS programming was guided by beta power in real-time local field potential recordings, instead of a monopolar review.ResultsThe initial sensing-based programming visit lasted on average 42.2 minutes (SD 18) per hemisphere. During the DBS optimization phase, a conventional monopolar clinical review was not required in any patients. The initial stimulation contact level remained the same at the final follow-up visit in all hemispheres except three. The final amplitude was on average 0.8 mA (SD 0.9) higher than initially set after the original sensing-based programming visit. One year after surgery, off-medication International Parkinson and Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III total score, tremor subscore, MDS-UPDRS IV, and levodopa-equivalent dose improved by 47.0% (p ConclusionsThis study shows that sensing-based initial DBS programming for PD is feasible and rapid, and selected clinically effective contacts in most patients, including those with tremor. Technologic innovations and practical developments could improve sensing-based programming.

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