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NCT Number: NCT04093908

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

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Key information

Conditions

Parkinson Disease Basal Ganglia Diseases Brain Diseases Central Nervous System Diseases Movement Disorders Nervous System Diseases Neurodegenerative Diseases Parkinsonian Disorders Synucleinopathies

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

MaastrichtUMC

Maastricht, Limburg, 6229 AZ, Netherlands

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Prediction of motor outcome after STN DBS based on preoperative variables

Other

Generating individual probabilities for motor response based on preoperative variables

Primary outcomes

  1. area under the curve of the receiver operator curve

    Time frame: 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.

  2. predictive accuracy

    Time frame: one-year postoperative

    See description primary outcome 1.

  3. true positive prediction rate

    Time frame: one-year postoperative

    See description primary outcome 1.

  4. false positive prediction rate

    Time frame: one-year postoperative

    See description primary outcome 1.

Sponsors and collaborators

Lead sponsor

Maastricht University Medical Center

Other

Registry information

Official study title

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

Acronym: DBS-PREDICT

Important dates

Study start
2019
Primary completion
2019
Study completion
2019
First posted
Sep 18, 2019
Registry last updated
Sep 1, 2020

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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