NCT04093908|Completed
Prediction of STN DBS Motor Response in PD
DBS-PREDICT
Machine Learning Prediction of Motor Response After STN DBS in Parkinson Patients, a Retrospective Multicenter Validation Study
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DBS-PREDICT
Machine Learning Prediction of Motor Response After STN DBS in Parkinson Patients, a Retrospective Multicenter Validation Study
Sponsor study identifier
observational
322
1 country
1
Study Start
August 1, 2019
Actual (reported)Primary Completion
December 17, 2019
Actual (reported)Study Completion
December 17, 2019
Actual (reported)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
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
December 17, 2019
Actual (reported)Click on a node to explore related trials.
Some registry-reported names shown below are not yet linked in this graph.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
August 1, 2019
Actual (reported)September 17, 2019
Actual (reported)September 18, 2019
Actual (reported)December 17, 2019
Actual (reported)December 17, 2019
Actual (reported)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
predictive accuracy
See description primary outcome 1.
one-year postoperative
true positive prediction rate
See description primary outcome 1.
one-year postoperative
false positive prediction rate
See description primary outcome 1.
one-year postoperative
We collect retrospective data from several international centers containing preoperative variables (demographical and clinical) and postoperative outcome (UPDRS II, III, IV) one year postoperatively, and merge these data to one validation cohort.
Generating individual probabilities for motor response based on preoperative variables
Registry-reported names are shown in full. Names without a link have not yet been matched to a Syfrah profile.
One cohort consisting of PD patients who underwent STN DBS in several international centers.
MaastrichtUMC
Maastricht, Limburg, 6229 AZ, Netherlands
322 participants (actual enrollment reported by the registry).
First Submitted
September 17, 2019
First Posted
September 18, 2019
Study Start
August 1, 2019
Actual (reported)Stimulation Parameters and Non-motor Symptoms in PD Treated With DBS
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Primary Completion
December 17, 2019
Actual (reported)Study Completion
December 17, 2019
Actual (reported)Last Updated
September 1, 2020
Record last verified: 2020-02
Anonymized data will be shared after completing analysing
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Source: ClinicalTrials.gov.
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