Robot-Assisted Deep Brain Stimulation: High Accuracy and Streamlined Workflow

A systematic review and meta-analysis of robot-assisted deep brain stimulation: comparative insights with conventional techniques

INTRODUCTION

Recent robotic-assisted surgical systems have shown promising efficiency and accuracy in brain surgeries. However, their application in deep brain stimulation (DBS) surgery remains limited.

METHODS

Studies from the Embase, Scopus, and Pubmed databases were included using a modified search string, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We extracted technical aspects of robotic-assisted DBS (RA-DBS) surgery, patient characteristics, accuracy, safety, and overall clinical results. We applied a random effects model for analysis. Heterogeneity was evaluated using Cochran's Q test and the I2 statistic. Additionally, subgroup analysis was conducted for the type of anesthesia, and frame-based versus frameless procedures and using microelectrode recordings (MER). Quality appraisal was conducted using the Newcastle-Ottawa Scale.

RESULTS

The pooled mean radial error (RE) and Euclidean distance (3D-EE) in robotic surgeries were 1.04 mm (95% CI 0.93 to 1.15 mm) and 1.19 mm (95% CI: 0.94 - 1.45 mm). RA-DBS outcomes regarding RE were comparable to conventional stereotactic DBS (C-DBS) surgery (SMD: -0.56, 95% CI: -1.35 - 0.22, P-value: 0.16). Also, the difference in the rates of reported complications was insignificant (OR: 0.26, 95% CI: -0.64 - 1.15, P-value: 0.58). None of the frame-based or frameless (P-value: 0.36), the use of MER (P-value: 0.11), or the type of anesthesia (P-value: 0.27) showed significant differences. However, there was an insignificant lower RE trend in the studies that employed frame-based techniques, used general anesthesia, and did not utilize MER.

CONCLUSION

Our study shows an acceptable level of error associated with RA-DBS. We found that the accuracy and complication rates were comparable to C-DBS. Due to substantial heterogeneity in the pooled mean RE and 3D-EE, more studies with higher sample sizes are required to evaluate RA-DBS.

Should asleep deep brain stimulation in Parkinson's disease be preferred over the awake approach? - Cons

No abstract available.

Safety and efficiency of deep brain stimulation in the elderly patients with Parkinson's disease

AIMS

Deep brain stimulation (DBS) is not routinely performed in elderly patients (≥75 years old) to date because of concerns about complications and decreased benefit. This study aimed to evaluate the safety and efficacy of DBS in elderly patients with Parkinson's disease.

METHODS

A retrospective analysis was performed using data from 40 elderly patients from four centers who were treated with neurosurgical robot-assisted DBS between September 2016 and December 2021. These patients were followed up for a minimum period of 2 years, with a subgroup of nine patients followed up for 5-7 years. Patient demographic characteristics, surgical information, pre- and postoperative motor scores, non-motor scores, activities of daily living, and complications were retrospectively analyzed.

RESULTS

The mean surgical procedure duration was 1.65 ± 0.24 h, with a mean electrode implantation duration of 1.10 ± 0.23 h and a mean pulse generator implantation duration of 0.55 ± 0.07 h. The mean pneumocephalus volume, electrode fusion error, and Tao's DBS surgery scale were 16.23 ± 12.81 cm3, 0.81 ± 0.23 mm, and 77.63 ± 8.08, respectively. One patient developed a skin infection, and the device was removed. The Unified Parkinson's disease rating scale, Unified Parkinson's disease rating scale of Part III, tremor, rigidity, bradykinesia, axial, and Barthel index for activities of daily living (ADL-Barthel) scores significantly improved at the 2-year follow-up (p < 0.05). The levodopa equivalent daily dose (LEDD) was significantly reduced at the 2-year follow-up (p < 0.05). However, the Montreal cognitive assessment, Hamilton depression scale, and Hamilton anxiety scale scores did not significantly change during the 2-year follow-up (p > 0.05). Additionally, in the subgroup with a 5-year follow-up, the motor symptoms, ADL-Barthel score, and cognitive function worsened over time compared to baseline. However, there was still an improvement in motor symptoms and ADL with DBS on-stimulation compared with the off-stimulation state. The LEDD increased 5 years after surgery compared to that at baseline. Eleven patients had passed away during follow-up, the mean survival time was 38.3 ± 17.3 months after surgery, and the mean age at the time of death was 81.2 (range 75-87) years.

CONCLUSION

Robot-assisted DBS surgery for the elderly patients with Parkinson's disease is accurate and safe. Motor symptoms and ADL significantly improve and patients can benefit from long-term neuromodulation, which may decrease the risk of death.

Evolution of Deep Brain Stimulation Techniques for Complication Mitigation

Complication mitigation in deep brain stimulation has been a topic matter of much discussion in the literature. In this article, we examine how neurosurgeons as individuals and as a field generated and adapted techniques to prevent infection, lead fracture/lead migration, and suboptimal outcomes in both the acute period and longitudinally. The authors performed a MEDLINE search inclusive of articles from 1987 to June 2023 including human studies written in English. Using the Rayyan platform, two reviewers (J.P. and R.M.) performed a title screen. Of the 776 articles, 252 were selected by title screen and 172 from abstract review for full-text evaluation. Ultimately, 124 publications were evaluated. We describe the initial complications and inefficiencies at the advent of deep brain stimulation and detail changes instituted by surgeons that reduced them. Furthermore, we discuss the trend in both undesired short-term and long-term outcomes with emphasis on how surgeons recognized and modified their practice to provide safer and better procedures. This scoping review adds to the literature as a guide to both new neurosurgeons and seasoned neurosurgeons alike to understand better what innovations have been trialed over time as we embark on novel targets and neuromodulatory technologies.

Tractography-Based Modeling Explains Treatment Outcomes in Patients Undergoing Deep Brain Stimulation for Obsessive-Compulsive Disorder

BACKGROUND

Deep brain stimulation (DBS) is an established and expanding therapy for treatment-refractory obsessive-compulsive disorder. Previous work has suggested that a white matter circuit providing hyperdirect input from the dorsal cingulate and ventrolateral prefrontal regions to the subthalamic nucleus could be an effective neuromodulatory target.

METHODS

We tested this concept by attempting to retrospectively explain through predictive modeling the ranks of clinical improvement as measured by the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) in 10 patients with obsessive-compulsive disorder who underwent DBS to the ventral anterior limb of internal capsule with subsequent programming uninformed by the putative target tract.

RESULTS

Rank predictions were carried out using the tract model by a team that was completely uninvolved in DBS planning and programming. Predicted Y-BOCS improvement ranks significantly correlated with actual Y-BOCS improvement ranks at the 6-month follow-up (r = 0.75, p = .013). Predicted score improvements correlated with actual Y-BOCS score improvements (r = 0.72, p = .018).

CONCLUSIONS

Here, we provide data in a first-of-its-kind report suggesting that normative tractography-based modeling can blindly predict treatment response in DBS for obsessive-compulsive disorder.

Learning Curves during Implementation of Robotic Stereotactic Surgery

INTRODUCTION

Adoption of robotic techniques is increasing for neurosurgical applications. Common cranial applications include stereoelectroencephalography (sEEG) and deep brain stimulation (DBS). For surgeons to implement robotic techniques in these procedures, realistic learning curves must be anticipated for surgeons to overcome the challenges of integrating new techniques into surgical workflow. One such way of quantifying learning curves in surgery is cumulative sum (CUSUM) analysis.

METHODS

Here, the authors present retrospective review of stereotactic cases to perform a CUSUM analysis of operative time for robotic cases at a single institution performed by 2 surgeons. The authors demonstrate learning phase durations of 20 and 16 cases in DBS and sEEG, respectively.

RESULTS

After plateauing of operative time, mastery phases started at cases 132 and 72 in DBS and sEEG. A total of 273 cases (188 DBS and 85 sEEG) were included in the study. The authors observed a learning plateau concordant with change of location of surgery after exiting the learning phase.

CONCLUSION

This study demonstrates the learning curve of 2 stereotactic workflows when integrating robotics as well as being the first study to examine the robotic learning curve in DBS via CUSUM analysis. This work provides data on what surgeons may expect when integrating this technology into their practice for cranial applications.

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.

Stereo-EEG-guided network modulation for psychiatric disorders: Surgical considerations

BACKGROUND

Deep brain stimulation (DBS) and other neuromodulatory techniques are being increasingly utilized to treat refractory neurologic and psychiatric disorders.

OBJECTIVE

/Hypothesis: To better understand the circuit-level pathophysiology of treatment-resistant depression (TRD) and treat the network-level dysfunction inherent to this challenging disorder, we adopted an approach of inpatient intracranial monitoring borrowed from the epilepsy surgery field.

METHODS

We implanted 3 patients with 4 DBS leads (bilateral pair in both the ventral capsule/ventral striatum and subcallosal cingulate) and 10 stereo-electroencephalography (sEEG) electrodes targeting depression-relevant network regions. For surgical planning, we used an interactive, holographic visualization platform to appreciate the 3D anatomy and connectivity. In the initial surgery, we placed the DBS leads and sEEG electrodes using robotic stereotaxy. Subjects were then admitted to an inpatient monitoring unit for depression-specific neurophysiological assessments. Following these investigations, subjects returned to the OR to remove the sEEG electrodes and internalize the DBS leads to implanted pulse generators.

RESULTS

Intraoperative testing revealed positive valence responses in all 3 subjects that helped verify targeting. Given the importance of the network-based hypotheses we were testing, we required accurate adherence to the surgical plan (to engage DBS and sEEG targets) and stability of DBS lead rotational position (to ensure that stimulation field estimates of the directional leads used during inpatient monitoring were relevant chronically), both of which we confirmed (mean radial error 1.2±0.9 mm; mean rotation 3.6±2.6°).

CONCLUSION

This novel hybrid sEEG-DBS approach allows detailed study of the neurophysiological substrates of complex neuropsychiatric disorders.

Application of the robot-assisted implantation in deep brain stimulation

INTRODUCTION

This work aims to assess the accuracy of robotic assistance guided by a videometric tracker in deep brain stimulation (DBS).

METHODS

We retrospectively reviewed a total of 30 DBS electrode implantations, assisted by the Remebot robotic system, with a novel frameless videometric registration workflow. Then we selected 30 PD patients who used stereotactic frame surgery to implant electrodes during the same period. For each electrode, accuracy was assessed using radial and axial error.

RESULTS

The average radial error of the robot-assisted electrode implantation was 1.28 ± 0.36 mm, and the average axial error was 1.20 ± 0.40 mm. No deaths or associated hemorrhages, infections or poor incision healing occurred.

CONCLUSION

Robot-assisted implantation guided by a videometric tracker is accurate and safe.