Clinical trial NCT04093908: Prediction of STN DBS Motor Response in PD
Official title: Machine Learning Prediction of Motor Response After STN DBS in Parkinson Patients, a Retrospective Multicenter Validation Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Parkinson Disease
Keywords: deep brain stimulation
motor outcome
prediction model
machine learning
Subthalamic nucleus
Lead sponsor: Maastricht University Medical Center
Sponsor class: OTHER
First posted: 2019-09-18
Enrollment: 322
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: One cohort consisting of PD patients who underwent STN DBS in several international centers.

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.

Eligibility criteria: 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

Primary outcomes:
measure: area under the curve of the receiver operator curve
timeFrame: one-year postoperative
measure: predictive accuracy
timeFrame: one-year postoperative
measure: true positive prediction rate
timeFrame: one-year postoperative
measure: false positive prediction rate
timeFrame: one-year postoperative
Secondary outcomes:
Other outcomes:
Locations:
city: Maastricht
country: Netherlands
facility: MaastrichtUMC
state: Limburg
Study officials:
References: