Introduction Despite careful patient selection for subthalamic nucleus deep brain stimulation (STN DBS), some Parkinson’s disease patients show limited improvement of motor disability. Innovative predictive analysing methods hold potential to develop a tool for clinicians that reliably predicts individual postoperative motor response, by only regarding clinical preoperative variables. The main aim of preoperative prediction would be to improve preoperative patient counselling, expectation management, and postoperative patient satisfaction. Methods We developed a machine learning logistic regression prediction model which generates probabilities for experiencing weak motor response one year after surgery. The model analyses preoperative variables and is trained on 89 patients using a five-fold cross-validation. Imaging and neurophysiology data are left out intentionally to ensure usability in the preoperative clinical practice. Weak responders (n = 30) were defined as patients who fail to show clinically relevant improvement on Unified Parkinson Disease Rating Scale II, III or IV. Results The model predicts weak responders with an average area under the curve of the receiver operating characteristic of 0.79 (standard deviation: 0.08), a true positive rate of 0.80 and a false positive rate of 0.24, and a diagnostic accuracy of 78%. The reported influences of individual preoperative variables are useful for clinical interpretation of the model, but cannot been interpreted separately regardless of the other variables in the model. Conclusion The model’s diagnostic accuracy confirms the utility of machine learning based motor response prediction based on clinical preoperative variables. After reproduction and validation in a larger and prospective cohort, this prediction model holds potential to support clinicians during preoperative patient counseling.

中文翻译：

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机器学习预测帕金森病深部脑刺激后的运动反应——回顾性队列中的原理证明

引言 尽管对丘脑底核深部脑刺激 (STN DBS) 进行了仔细的患者选择，但一些帕金森病患者的运动障碍改善有限。创新的预测分析方法有可能为临床医生开发一种工具，该工具仅通过临床术前变量来可靠地预测个体术后运动反应。术前预测的主要目的是改善术前患者咨询、期望管理和术后患者满意度。方法 我们开发了一种机器学习逻辑回归预测模型，该模型生成术后一年运动反应较弱的概率。该模型分析了术前变量，并使用五重交叉验证对 89 名患者进行了训练。成像和神经生理学数据被有意遗漏以确保在术前临床实践中的可用性。弱反应者（n = 30）被定义为未能在统一帕金森病评定量表 II、III 或 IV 上显示出临床相关改善的患者。结果该模型预测弱反应者的平均接受者操作特征曲线下面积为0.79（标准差：0.08），真阳性率为0.80，假阳性率为0.24，诊断准确率为78%。报告的个别术前​​变量的影响对于模型的临床解释很有用，但无论模型中的其他变量如何，都不能单独解释。结论该模型的诊断准确性证实了基于临床术前变量的基于机器学习的运动反应预测的实用性。在更大的前瞻性队列中复制和验证后，该预测模型有可能在术前患者咨询期间为临床医生提供支持。