Machine learning prediction of motor response after deep brain stimulation in Parkinson’s disease—proof of principle in a retrospective cohort

PeerJ

article

Jeroen G.V. Habets¹  Marcus L.F. Janssen¹  Annelien A. Duits³  Laura C.J. Sijben⁴  Anne E.P. Mulders¹  Bianca De Greef⁴  Yasin Temel¹  Mark L. Kuijf⁴  Pieter L. Kubben¹  Christian Herff¹ 

[1] Department of Neurosurgery, School for Mental Health and Neuroscience, Maastricht University;Department of Clinical Neurophysiology, Maastricht University Medical Center;Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center;Department of Neurology, Maastricht University Medical Center;Department of Clinical Epidemiology and Medical Technology Assessment ,(KEMTA), Maastricht University Medical Center;Department of Neurosurgery, Maastricht University Medical Center;Department of Neurosurgery, Radboud University Medical Center

关键词: Parkinson’s disease;    Deep brain stimulation;    Subthalamic nucleus;    Prediction;    Outcome;   

DOI  :  10.7717/peerj.10317

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

IntroductionDespite 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.MethodsWe 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.ResultsThe 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.ConclusionThe 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.

【 授权许可】

CC BY