Machine Learning Prediction of Motor Response After STN DBS in Parkinson Patients, a Retrospective Multicenter Validation Study
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 322
- 试验地点
- 1
- 主要终点
- predictive accuracy
研究概览
简要总结
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
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •underwent STN DBS for Parkinson's disease
- •completed one year follow up after surgery
排除标准
- •missing data in postoperative UPDRS II, III, IV
结局指标
主要结局
predictive accuracy
时间窗: one-year postoperative
See description primary outcome 1.
true positive prediction rate
时间窗: one-year postoperative
See description primary outcome 1.
area under the curve of the receiver operator curve
时间窗: 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.
false positive prediction rate
时间窗: one-year postoperative
See description primary outcome 1.
次要结局
未报告次要终点