Comprehensive clinical scale-based machine learning model for predicting subthalamic nucleus deep brain stimulation outcomes in Parkinson's disease.

Abstract

Parkinson's Disease (PD) is a growing burden with varied clinical manifestations and responses to Subthalamic Nucleus Deep Brain Stimulation (STN-DBS). At present, there is no effective and simple machine learning model based on comprehensive clinical scales to predict the improvement in motor symptoms of PD treated with DBS. A total of 647 PD patients from the First Affiliated Hospital of University of Science and Technology of China were enrolled retrospectively. LightGBM machine learning algorithm was used for modeling, and 123 PD patients from Qingdao Municipal Hospital were used as external data to verify the effectiveness of the model. The study was registered in the Chinese Clinical Trial Registry with the registration number of ChiCTR2300073955. The LightGBM model outperformed others, demonstrating an internal test set AUC of 0.874 (95%CI [0.822-0.927]) and an average AUC of 0.921 ± 0.03 during cross-validation. The external validation yielded an AUC of 0.769 (95% CI[0.685-0.853]). Key predictive variables identified include MMSE scores, HAMA scores, years of education, medication improvement rate, and preoperative UPDRS scores. The results indicate that the LightGBM model based on the top seven influencing factors is a promising tool for predicting the improvement in motor symptoms of PD after 1 year of STN-DBS.