Parkinson Disease
Conditions
Keywords
deep brain stimulation, motor outcome, prediction model, machine learning, Subthalamic nucleus
Brief summary
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
Detailed description
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.
Interventions
OTHERPrediction of motor outcome after STN DBS based on preoperative variables
Generating individual probabilities for motor response based on preoperative variables
Sponsors
Maastricht University Medical Center
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* underwent STN DBS for Parkinson's disease * completed one year follow up after surgery
Exclusion criteria
\- missing data in postoperative UPDRS II, III, IV
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| area under the curve of the receiver operator curve | one-year postoperative | Motor outcome is categorised in a binary outcome variable. The model will predict to which outcome group the patient will belong one-year postoperatively. The primary outcome measure is the performance of the predicted outcome categories with the actual outcome categories. Performance of prediction models is expressed as area under the curve of the receiver operator curve, predictive accuracy, true positive prediction rate, and false positive prediction rate. |
| predictive accuracy | one-year postoperative | See description primary outcome 1. |
| true positive prediction rate | one-year postoperative | See description primary outcome 1. |
| false positive prediction rate | one-year postoperative | See description primary outcome 1. |
Countries
Netherlands
Outcome results
None listed