Comparison of clinical outcomes and accuracy of electrode placement between robot-assisted and conventional deep brain stimulation of the subthalamic nucleus: a single-center study

Efficacy and safety of robot-assisted deep brain stimulation for Parkinson's disease: a meta-analysis

Objective

This meta-analysis aims to assess the effectiveness and safety of robot-assisted deep brain stimulation (DBS) surgery for Parkinson's disease(PD).

Methods

Four databases (Medline, Embase, Web of Science and CENTRAL) were searched from establishment of database to 23 March 2024, for articles studying robot-assisted DBS in patients diagnosed with PD. Meta-analyses of vector error, complication rate, levodopa-equivalent daily dose (LEDD), Unified Parkinson's Disease Rating Scale (UPDRS), UPDRS II, UPDRS III, and UPDRS IV were performed.

Results

A total of 15 studies were included in this meta-analysis, comprising 732 patients with PD who received robot-assisted DBS. The pooled results revealed that the vector error was measured at 1.09 mm (95% CI: 0.87 to 1.30) in patients with Parkinson's disease who received robot-assisted DBS. The complication rate was 0.12 (95% CI, 0.03 to 0.24). The reduction in LEDD was 422.31 mg (95% CI: 68.69 to 775.94). The improvement in UPDRS, UPDRS III, and UPDRS IV was 27.36 (95% CI: 8.57 to 46.15), 14.09 (95% CI: 4.67 to 23.52), and 3.54 (95% CI: -2.35 to 9.43), respectively.

Conclusion

Robot-assisted DBS is a reliable and safe approach for treating PD. Robot-assisted DBS provides enhanced accuracy in contrast to conventional frame-based stereotactic techniques. Nevertheless, further investigation is necessary to validate the advantages of robot-assisted DBS in terms of enhancing motor function and decreasing the need for antiparkinsonian medications, in comparison to traditional frame-based stereotactic techniques.Clinical trial registration: PROSPERO(CRD42024529976).

Robot-Assisted Minimally Invasive Asleep Single-Stage Deep Brain Stimulation Surgery: Operative Technique and Systematic Review

BACKGROUND AND OBJECTIVES

Robotic assistance has garnered increased use in neurosurgery. Recently, this has expanded to include deep brain stimulation (DBS). Several studies have reported increased accuracy and improved efficiency with robotic assistance, but these are limited to individual robotic platforms with smaller sample sizes or are broader studies on robotics not specific to DBS. Our objectives are to report our technique for robot-assisted, minimally invasive, asleep, single-stage DBS surgery and to perform a meta-analysis comparing techniques from previous studies.

METHODS

We performed a single-center retrospective review of DBS procedures using a floor-mounted robot with a frameless transient fiducial array registration. We compiled accuracy data (radial entry error, radial target error, and 3-dimensional target error) and efficiency data (operative time, setup time, and total procedure time). We then performed a meta-analysis of previous studies and compared these metrics.

RESULTS

We analyzed 315 electrodes implanted in 160 patients. The mean radial target error was 0.9 ± 0.5 mm, mean target 3-dimensional error was 1.3 ± 0.7 mm, and mean radial entry error was 1.1 ± 0.8 mm. The mean procedure time (including pulse generator placement) was 182.4 ± 47.8 minutes, and the mean setup time was 132.9 ± 32.0 minutes. The overall complication rate was 8.8% (2.5% hemorrhagic/ischemic, 2.5% infectious, and 0.6% revision). Our meta-analysis showed increased accuracy with floor-mounted over skull-mounted robotic platforms and with fiducial-based registrations over optical registrations.

CONCLUSION

Our technique for robot-assisted, minimally invasive, asleep, single-stage DBS surgery is safe, accurate, and efficient. Our data, combined with a meta-analysis of previous studies, demonstrate that robotic assistance can provide similar or increased accuracy and improved efficiency compared with traditional frame-based techniques. Our analysis also suggests that floor-mounted robots and fiducial-based registration methods may be more accurate.

Frameless Robot-Assisted Asleep Centromedian Thalamic Nucleus Deep Brain Stimulation Surgery in Patients with Drug-Resistant Epilepsy: Technical Description and Short-Term Clinical Results

INTRODUCTION

This purpose of this work is to give a detailed description of a surgical technique for frameless robot-assisted asleep deep brain stimulation (DBS) of the centromedian thalamic nucleus (CMT) in drug-resistant epilepsy (DRE).

METHODS

Ten consecutively enrolled patients who underwent CMT-DBS were included in the study. The FreeSurfer "Thalamic Kernel Segmentation" module and experience target coordinates were used for locating the CMT, and quantitative susceptibility mapping (QSM) images were used to check the target. The patient's head was secured with a head clip, and electrode implantation was performed with the assistance of the neurosurgical robot Sinovation^®. After opening the dura, the burr hole was continuously flushed with physiological saline to stop air from entering the skull. All procedures were performed under general anesthesia without intraoperative microelectrode recording (MER).

RESULTS

The mean age of the patients at surgery and onset of seizures was 22 years (range 11-41 years) and 11 years (range 1-21 years), respectively. The median duration of seizures before CMT-DBS surgery was 10 years (2-26 years). CMT was successfully segmented, and its position was verified by experience target coordinates and QSM images in all ten patients. The mean surgical time for bilateral CMT-DBS in this cohort was 165 ± 18 min. The mean pneumocephalus volume was 2 cm³. The median absolute errors in the x-, y-, and z-axes were 0.7 mm, 0.5 mm, and 0.9 mm, respectively. The median Euclidean distance (ED) and radial error (RE) was 1.3 ± 0.5 mm and 1.0 ± 0.3 mm, respectively. No significant difference was found between right- and left-sided electrodes regarding the RE nor the ED. After a mean 12-month follow-up, the average reduction in seizures was 61%, and six patients experienced a ≥ 50% reduction in seizures, including one patient who had no seizures after the operation. All patients tolerated the anesthesia operation, and no permanent or serious complications were reported.

CONCLUSIONS

Frameless robot-assisted asleep surgery is a precise and safe approach for placing CMT electrodes in patients with DRE, shortening the surgery time. The segmentation of the thalamic nuclei enables the precise location of the CMT, and the flow of physiological saline to seal the burr holes is a good way to reduce the influx of air. CMT-DBS is an effective method to reduce seizures.

Nomogram for Predicting Depression Improvement after Deep Brain Stimulation for Parkinson’s Disease

Background: Parkinson’s disease is a common neurodegenerative disease, with depression being a common non-motor symptom. Bilateral subthalamic nucleus deep brain stimulation is an effective method for the treatment of Parkinson’s disease. Thus, this study aimed to establish a nomogram of the possibility of achieving a better depression improvement rate after subthalamic nucleus deep brain stimulation in patients with Parkinson’s disease. Methods: We retrospectively analyzed 103 patients with Parkinson’s disease who underwent subthalamic nucleus deep brain stimulation and were followed up for the improvement of their Hamilton Depression scale scores 1 year postoperatively. Univariate and multivariate logistic regression analyses were used to select factors affecting the improvement rate of depression. A nomogram was then developed to predict the possibility of achieving better depression improvement. Furthermore, the discrimination and fitting performance was evaluated using a calibration diagram, receiver operating characteristics, and decision curve analysis. Results: The mean and median improvement rates of Hamilton Depression scores were 13.1 and 33.3%, respectively. Among the 103 patients, 70.8% had an improved depression, 23.3% had a worsened depression, and 5.8% remained unchanged. Logistic multivariate regression analysis showed that age, preoperative Parkinson’s Disease Questionnaire, Hamilton Anxiety, and Hamilton Depression scores were independent factors for the possibility of achieving a better depression improvement rate. Based on these results, a nomogram model was developed. The nomogram had a C-index of 0.78 (95% confidence interval: 0.69–0.87) and an area under the receiver operating characteristics of 0.78 (95% confidence interval: 0.69–0.87). The calibration plot and decision curve analysis further demonstrated goodness-of-fit between the nomogram predictions and actual observations. Conclusion: We developed a nomogram to predict the possibility of achieving good depression improvement 1 year after subthalamic nucleus deep brain stimulation in patients with Parkinson’s disease, which showed a certain value in judging the expected depression improvement of these patients.

Nomogram to Predict Cognitive State Improvement after Deep Brain Stimulation for Parkinson's Disease

Purpose: Parkinson’s disease (PD) is a common neurodegenerative disease, for which cognitive impairment is a non-motor symptom (NMS). Bilateral subthalamic nucleus deep brain stimulation (STN-DBS) is an effective treatment for PD. This study established a nomogram to predict cognitive improvement rate after STN-DBS in PD patients. Methods: We retrospectively analyzed 103 PD patients who underwent STN-DBS. Patients were followed up to measure improvement in MoCA scores one year after surgery. Univariate and multivariate logistic regression analyses were used to identify factors affecting improvement in cognitive status. A nomogram was developed to predict this factor. The discrimination and fitting performance were evaluated by receiver operating characteristics (ROC) analysis, calibration diagram, and decision curve analysis (DCA). Results: Among 103 patients, the mean improvement rate of the MoCA score was 37.3% and the median improvement rate was 27.3%, of which 64% improved cognition, 27% worsened cognition, and 8.7% remained unchanged. Logistic multivariate regression analysis showed that years of education, UPDRSIII drug use, MoCA Preop, and MMSE Preop scores were independent factors affecting the cognitive improvement rate. A nomogram model was subsequently developed. The C-index of the nomogram was 0.98 (95%CI, 0.97–1.00), and the area under the ROC was 0.98 (95%CI 0.97–1.00). The calibration plot and DCA demonstrated the goodness-of-fit between nomogram predictions and actual observations. Conclusion: Our nomogram could effectively predict the possibility of achieving good cognitive improvement one year after STN-DBS in patients with PD. This model has value in judging the expected cognitive improvement of patients with PD undergoing STN-DBS.