Prediction of STN DBS Motor Response in PD

Despite careful patient selection for subthalamic nucleus deep brain stimulation (STN DBS), some Parkinson's disease (PD) patients show limited improvement of motor disability. Non-conclusive results and the lack of a practical implantable prediction algorithm from previous prediction studies maintain the need for a simple tool for neurologists that provides a reliable prediction on postoperative motor improvement for individual patients. In this study, a prior developed prediction model for motor response after STN DBS in PD patients is validated. The model generates individual probabilities for becoming a weak responder one year after surgery. The model will be validated in a validation cohort collected from several international centers. The predictive model is made public accessible before data collection on: https://github.com/jgvhabets/DBSPREDICT

Predicting motor outcome after STN DBS in Parkinson Disease can be challenging for the clinician. Current prediction studies report non-conclusive results on the most important predictors and are limited by used computational methods. Traditional statistical analyses which focus on correlations are biased by predictor- and confounder-selection by the investigators. Modern computational methods like machine learning prediction models are less limited by sample size and can consider a wider range of predictors which leads to less selection-bias. Retrospective patient data is collected from multiple international centers. This retrospective, multicenter cohort is used to validate the model which is developed based on a single-center retrospective cohort. The goal is to develop a prediction tool that provides the clinician with a probability for weak response during the preoperative phase. This could support the clinician in including or informing the patient during preoperative counseling. The predictive model is made public accessible before data collection on: https://github.com/jgvhabets/DBSPREDICT.