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- Title
A Method for Predicting the Outcomes of Subthalamic Deep Brain Stimulation Surgery for Parkinson's Disease.
- Authors
Lu Yang; Yao Chen; Haoyuan Wang; Fengfei Lu; Shizhong Zhang
- Abstract
Introduction: Although significant progress has been made in computational simulation of deep brain stimulation (DBS), very few of these have been introduced into daily clinical use. Former computational models usually include imaging data in the attempt to give the visualized suggestion for post-operational programming. To date, the clinical characteristics and electrophysiology are not considered, thus traditional models do not accurately predict the simulated therapeutic outcomes of DBS. However, the micro-electrode recordings could provide both electrophysiological status and location information of the DBS electrode, which is worth its inclusion in the simulation model. Methods: Clinical data of 31 patients treated with DBS for Parkinson's disease, with 107 post-DBS programming visits, were used to create a therapeutic prediction model. Based on the patient-specific clinical data, which included trajectory length within the subthalamic nucleus from micro-electrode recordings and total electric energy delivered by DBS, therapeutic outcomes were predicted using a step-wise multiple linear regression model. Results: Post-operative outcomes were significantly correlated (P < 0.05) to the following parameters: measures of disease duration, age, pre-operative on-state duration, pre-operative non-dyskinesia duration, mini-metal state examination, visit time until surgery, trajectory length within subthalamic nucleus and total electric energy delivered. Using these results, the prediction model was able to predict 43.3% of the variance in motor score (UPDRS III) improvement (R2 = 0.433, P < 0.01). Pre-operative response to levodopa and the reduction of levodopa equivalent dosage was not correlated to the motor score improvement. Conclusions: Our findings provide an insight into the relationship between the therapeutic outcomes and a number of factors such as micro-electrode recordings, patient-specific clinical data and post-operative DBS settings. This multi-variate model represents a novel tool for predicting the therapeutic outcomes and may aid research into the mechanisms of DBS therapy.
- Publication
Stereotactic & Functional Neurosurgery, 2017, Vol 95, p379
- ISSN
1011-6125
- Publication type
Article