Learning Curve of Robotic End-to-Side Microanastomoses.

BACKGROUND AND OBJECTIVES: Robotics are becoming increasingly widespread within various neurosurgical subspecialties, but data pertaining to their feasibility in vascular neurosurgery are limited. We present our novel attempt to evaluate the learning curve of a robotic platform for microvascular anastomoses. METHODS: One hundred and sixty one sutures were performed and assessed. Fourteen anastomoses (10 robotic [MUSA-2 Microsurgical system; Microsure] and 4 hand-sewn) were performed by the senior author on 1.5-mm caliber tubes and recorded with the Kinevo 900 (Zeiss) operative microscope. We separately compared interrupted sutures (from needle insertion until third knot) and running sutures (from needle insertion until loop pull-down). Average suture timing across all groups was compared using an unpaired Student's t test. Exponential smoothing (α = 0.2) was then applied to the robotic data sets for validation and a second set of t tests were performed. RESULTS: We compared 107 robotic sutures with 54 hand-sewn sutures. There was a significant difference between the average time/stitch for the robotic running sutures (n = 55) and the hand-sewn running sutures (n = 31) (31.2 seconds vs 48.3 seconds, respectively; P-value = .00052). Exponential smoothing (α = 0.2) reinforced these results (37.6 seconds vs 48.3 seconds; P-value = .014625). Average robotic running times surpassed hand-sewn by the second anastomosis (38.8 seconds vs 48.3 seconds) and continued to steadily decrease with subsequent stitches. The average of the robotic interrupted sutures (n = 52) was significantly longer than the hand-sewn (n = 23) (171.3 seconds vs 70 seconds; P = .000024). Exponential smoothing (α = 0.2) yielded similar results (196.7 seconds vs 70 seconds; P = .00001). However, average robotic interrupted times significantly decreased from the first to the final anastomosis (286 seconds vs 105.2 seconds; P = .003674). CONCLUSION: Our results indicate the learning curve for robotic microanastomoses is short and encouraging. The use of robotics warrants further study for potential use in cerebrovascular bypass procedures.

Use of Carotid Web Angioarchitecture for Stroke Risk Assessment.

OBJECTIVE: To examine the usefulness of carotid web (CW), carotid bifurcation and their combined angioarchitectural measurements in assessing stroke risk. METHODS: Anatomic data on the internal carotid artery (ICA), common carotid artery (CCA), and the CW were gathered as part of a retrospective study from symptomatic (stroke) and asymptomatic (nonstroke) patients with CW. We built a model of stroke risk using principal-component analysis, Firth regression trained with 5-fold cross-validation, and heuristic binary cutoffs based on the Minimal Description Length principle. RESULTS: The study included 22 patients, with a mean age of 55.9 ± 12.8 years; 72.9% were female. Eleven patients experienced an ischemic stroke. The first 2 principal components distinguished between patients with stroke and patients without stroke. The model showed that ICA-pouch tip angle (P = 0.036), CCA-pouch tip angle (P = 0.036), ICA web-pouch angle (P = 0.036), and CCA web-pouch angle (P = 0.036) are the most important features associated with stroke risk. Conversely, CCA and ICA anatomy (diameter and angle) were not found to be risk factors. CONCLUSIONS: This pilot study shows that using data from computed tomography angiography, carotid bifurcation, and CW angioarchitecture may be used to assess stroke risk, allowing physicians to tailor care for each patient according to risk stratification.

Early Experience of Surgical Planning for STA-MCA Bypass Using Virtual Reality.

BACKGROUND: The superficial temporal artery (STA)-to-middle cerebral artery (MCA) bypass requires precise preoperative planning, and 3-dimensional virtual reality (VR) models have recently been used to optimize planning of STA-MCA bypass. In the present report, we have described our experience with VR-based preoperative planning of STA-MCA bypass. METHODS: Patients from August 2020 to February 2022 were analyzed. For the VR group, using 3-dimensional models from the patients' preoperative computed tomography angiograms, VR was used to locate the donor vessels, potential recipient, and anastomosis sites and plan the craniotomy, which were referenced throughout surgery. Computed tomography angiograms or digital subtraction angiograms were used to plan the craniotomy for the control group. The procedure time, bypass patency, craniotomy size, and postoperative complication rates were assessed. RESULTS: The VR group included 17 patients (13 women; age, 49 ± 14 years) with Moyamoya disease (76.5%) and/or ischemic stroke (29.4%). The control group included 13 patients (8 women; age, 49 ± 12 years) with Moyamoya disease (92.3%) and/or ischemic stroke (7.3%). For all 30 patients, the preoperatively planned donor and recipient branches were effectively translated intraoperatively. No significant difference were found in the procedure time or craniotomy size between the 2 groups. Bypass patency was 94.1% for the VR group (16 of 17) and 84.6% for the control group (11 of 13). No permanent neurological deficits occurred in either group. CONCLUSIONS: Our early experience has shown that VR can serve as a useful, interactive preoperative planning tool by enhancing visualization of the spatial relationship between the STA and MCA without compromising the surgical results.