Nomogram for Prediction of Postoperative Delirium after Deep Brain Stimulation of Subthalamic Nucleus in Parkinson's Disease under General Anesthesia.

Abstract

Introduction: Postoperative delirium can increase cognitive impairment and mortality in patients with Parkinson's disease. The purpose of this study was to develop and internally validate a clinical prediction model of delirium after deep brain stimulation of the subthalamic nucleus in Parkinson's disease under general anesthesia.

Methods: We conducted a retrospective observational cohort study on the data of 240 patients with Parkinson's disease who underwent deep brain stimulation of the subthalamic nucleus under general anesthesia. Demographic characteristics, clinical evaluation, imaging data, laboratory data, and surgical anesthesia information were collected. Multivariate logistic regression was used to develop the prediction model for postoperative delirium.

Results: A total of 159 patients were included in the cohort, of which 38 (23.90%) had postoperative delirium. Smoking (OR 4.51, 95% CI 1.56-13.02, p < 0.01) was the most important risk factor; other independent predictors were orthostatic hypotension (OR 3.42, 95% CI 0.90-13.06, p=0.07), inhibitors of type-B monoamine oxidase (OR 3.07, 95% CI 1.17-8.04, p=0.02), preoperative MRI with silent brain ischemia or infarction (OR 2.36, 95% CI 0.90-6.14, p=0.08), Hamilton anxiety scale score (OR 2.12, 95% CI 1.28-3.50, p < 0.01), and apolipoprotein E level in plasma (OR 1.48, 95% CI 0.95-2.29, p=0.08). The area under the receiver operating characteristic curve (AUC) was 0.76 (95% CI 0.66-0.86). A nomogram was established and showed good calibration and clinical predictive capacity. After bootstrap for internal verification, the AUC was 0.74 (95% CI 0.66-0.83).

Conclusion: This study provides evidence for the independent inducing factors of delirium after deep brain stimulation of the subthalamic nucleus in Parkinson's disease under general anesthesia. By predicting the development of delirium, our model may identify high-risk groups that can benefit from early or preventive intervention.