Gradient boosting machine model predicts psychiatric complications after deep brain stimulation in Parkinson’s disease

Abstract

BACKGROUND

Deep brain stimulation (DBS) is an effective method for treating the motor symptoms of advanced Parkinson’s disease (PD). However, complications such as depression, anxiety, cognitive impairment, and delirium that occur after DBS surgery can severely affect a patient’s quality of life and the therapeutic effect.

AIM

To construct a gradient boosting machine (GBM) risk model to predict the risk of mental complications such as depression, anxiety, and cognitive impairment in patients with PD after DBS surgery.

METHODS

We retrospectively collected data on patients with PD treated at a top-tier hospital in China between June 2023 and December 2024. During this period, 234 cases were screened and analyzed, of which 70% were included in the modeling set and the remaining 30% in the test set. The modeling set was used to construct the risk prediction model, whereas the test set was used to validate the predictive performance of the model. Additionally, we used the GBM model to predict outcomes for 65 patients with PD who visited the hospital between January 2025 and April 2025, and analyzed the application effect of the model.

RESULTS

In a cohort of 234 patients undergoing DBS, the incidence of psychiatric complications such as depression, anxiety, and cognitive impairment was 37.61%. Age, surgery duration, fasting time, family relationship health assessment scale scores, and unified PD rating scale III scores were identified as independent influencing factors. Based on these variables, the constructed GBM model demonstrated excellent predictive performance, with an accuracy of 80.0%, sensitivity of 95.7%, and specificity of 78.6%. Decision curve analysis revealed that the model’s clinical benefit and applicability are optimal when the threshold is between 0.09 and 0.70.

CONCLUSION

The prediction model constructed based on the GBM algorithm has good predictive performance and can provide a reference for clinical medical staff to identify groups at high risk for mental complications such as depression, anxiety, cognitive impairment, and delirium after DBS.

Keywords: Parkinson’s disease; Deep brain stimulation; Postoperative complications; Gradient boosting machine; Prediction model

Core Tip: This study developed a gradient boosting machine (GBM) model to predict psychiatric complications (depression, anxiety, cognitive impairment, and delirium) in patients with Parkinson’s disease after deep brain stimulation surgery. By analyzing data from 234 patients, the model identified five critical risk factors: Age, surgery duration, fasting time, Family Relationship Health Assessment Scale score, and motor symptom severity (Unified Parkinson’s Disease Rating Scale Part III score). These factors collectively explained 37.6% complication incidence. The GBM model achieved high predictive accuracy (80.0%), sensitivity (95.7%), and area under the curve (0.896) in external validation (65 patients). Decision curve analysis confirmed the optimal clinical utility for risk thresholds between 0.09-0.70, enabling preoperative risk stratification and personalized interventions to mitigate postoperative neuropsychiatric risks.