Parkinsonism & related disorders · 2026
Introducing a workflow algorithm for adaptive DBS programming in Parkinson's disease.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Anis Saar, Clark Patricia A, Shaffer Shannon, Hartwig Emma, Moser Laurie, Scott Jenera, Hennigs Erica, Walter Ellen, Lopez Gonzalez Tatiana, Escobar David, Walter Benjamin L
Аннотация
BACKGROUND: Adaptive deep brain stimulation (aDBS) utilizing BrainSense™ technology by Medtronic Percept™ stimulator, is a recent advancement that dynamically adjusts stimulation in real-time based on the power of frequency-specific neural signals. Although clinical trials have shown the feasibility of aDBS, real-world implementation remains underreported. We describe the development and implementation of a structured workflow for aDBS activation and programming at a tertiary movement disorders center.
METHODS: This quality improvement project included the first 50 patients with Parkinson's disease who initiated aDBS between March and September 2025 at the Cleveland Clinic. Data were abstracted from clinical records and programming logs as part of routine process evaluation. Findings were discussed with programming clinicians to identify variability and troubleshooting steps, leading to iterative refinement and consensus on a standardized workflow for aDBS activation and programming.
RESULTS: The primary reasons for converting to aDBS were dyskinesias, motor fluctuations, and limited therapeutic windows on continuous DBS. Programming emphasized individualized threshold strategies (single vs. dual), ensuring each was fully optimized before transition. Successful implementation depended on three key factors: clearly defined therapeutic goals, validation of the frequency of interest, and confirmation of appropriate system adaptation through Timeline plots review and real-time signal streaming. A structured flowchart summarizing the programming and troubleshooting sequence is provided.
CONCLUSIONS: Implementation of aDBS in routine clinical practice is feasible with a structured, team-based workflow. Standardizing programming steps and incorporating iterative feedback support safety, consistency, and broader adoption across centers.
METHODS: This quality improvement project included the first 50 patients with Parkinson's disease who initiated aDBS between March and September 2025 at the Cleveland Clinic. Data were abstracted from clinical records and programming logs as part of routine process evaluation. Findings were discussed with programming clinicians to identify variability and troubleshooting steps, leading to iterative refinement and consensus on a standardized workflow for aDBS activation and programming.
RESULTS: The primary reasons for converting to aDBS were dyskinesias, motor fluctuations, and limited therapeutic windows on continuous DBS. Programming emphasized individualized threshold strategies (single vs. dual), ensuring each was fully optimized before transition. Successful implementation depended on three key factors: clearly defined therapeutic goals, validation of the frequency of interest, and confirmation of appropriate system adaptation through Timeline plots review and real-time signal streaming. A structured flowchart summarizing the programming and troubleshooting sequence is provided.
CONCLUSIONS: Implementation of aDBS in routine clinical practice is feasible with a structured, team-based workflow. Standardizing programming steps and incorporating iterative feedback support safety, consistency, and broader adoption across centers.