Prediction of STN DBS Motor Response in PD

Parkinson Disease Clinical Trial

— DBS-PREDICT  

Official title:

Machine Learning Prediction of Motor Response After STN DBS in Parkinson Patients, a Retrospective Multicenter Validation Study

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04093908
• Other study ID #: 2019-0739-A9
• Status: Completed
• Start date: August 1, 2019
• Est. completion date: December 17, 2019

Study information

• Verified date: February 2020
• Source: Maastricht University Medical Center
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 322
• Est. completion date: December 17, 2019
• Est. primary completion date: December 17, 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Parkinson Disease

Intervention

Other:
• Prediction of motor outcome after STN DBS based on preoperative variables
Generating individual probabilities for motor response based on preoperative variables

Locations

Country	Name	City	State
Netherlands	MaastrichtUMC	Maastricht	Limburg

Sponsors (1)

Lead Sponsor	Collaborator
Maastricht University Medical Center

Country where clinical trial is conducted

Netherlands, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	area under the curve of the receiver operator curve	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.	one-year postoperative
Primary	predictive accuracy	See description primary outcome 1.	one-year postoperative
Primary	true positive prediction rate	See description primary outcome 1.	one-year postoperative
Primary	false positive prediction rate	See description primary outcome 1.	one-year postoperative