Dataset Information

Surgical Training for the Management of Intraoperative Aneurysm Rupture Using a Three-Dimensional Artificial Model.

ABSTRACT: Background:Intraoperative aneurysm rupture (IAR) is one of the most dreaded complications of microsurgical cerebral aneurysm clipping. The surgical result is directly affected by whether an operator can control IAR. However, the development of endovascular treatment has decreased the probability of exposure to this situation for neurosurgical residents. Therefore, neurovascular surgeons must develop the skills and mental attitude to deal with IAR through off-the-job training, without conducting actual operations. Materials and Methods:We have developed a tabletop training system using three-dimensional artificial modeling that can be used for daily clinical practice. Results and Conclusions:Our model is useful for training because of its low cost, reusability, and ease of preparation and practice.

SUBMITTER: Kumagai K

PROVIDER: S-EPMC6417360 | biostudies-literature | 2019 Jan-Mar

REPOSITORIES: biostudies-literature

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Surgical Training for the Management of Intraoperative Aneurysm Rupture Using a Three-Dimensional Artificial Model.

Kumagai Kosuke K Mori Kentaro K Takeuchi Satoru S Wada Kojiro K

Asian journal of neurosurgery 20190101 1

<h4>Background</h4>Intraoperative aneurysm rupture (IAR) is one of the most dreaded complications of microsurgical cerebral aneurysm clipping. The surgical result is directly affected by whether an operator can control IAR. However, the development of endovascular treatment has decreased the probability of exposure to this situation for neurosurgical residents. Therefore, neurovascular surgeons must develop the skills and mental attitude to deal with IAR through off-the-job training, without con ...[more]

PMID: 30937030

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Project description:BACKGROUND:Ultrasmall superparamagnetic particles of iron oxide (USPIO) detect cellular inflammation on magnetic resonance imaging (MRI). In patients with abdominal aortic aneurysm, we assessed whether USPIO-enhanced MRI can predict aneurysm growth rates and clinical outcomes. METHODS:In a prospective multicenter open-label cohort study, 342 patients with abdominal aortic aneurysm (diameter ?40 mm) were classified by the presence of USPIO enhancement and were monitored with serial ultrasound and clinical follow-up for ?2 years. The primary end point was the composite of aneurysm rupture or repair. RESULTS:Participants (85% male, 73.1±7.2 years) had a baseline aneurysm diameter of 49.6±7.7 mm, and USPIO enhancement was identified in 146 (42.7%) participants, absent in 191 (55.8%), and indeterminant in 5 (1.5%). During follow-up (1005±280 days), 17 (5.0%) abdominal aortic aneurysm ruptures, 126 (36.8%) abdominal aortic aneurysm repairs, and 48 (14.0%) deaths occurred. Compared with those without uptake, patients with USPIO enhancement have increased rates of aneurysm expansion (3.1±2.5 versus 2.5±2.4 mm/year, P=0.0424), although this was not independent of current smoking habit (P=0.1993). Patients with USPIO enhancement had higher rates of aneurysm rupture or repair (47.3% versus 35.6%; 95% confidence intervals, 1.1-22.2; P=0.0308). This finding was similar for each component of rupture (6.8% versus 3.7%, P=0.1857) or repair (41.8% versus 32.5%, P=0.0782). USPIO enhancement was associated with reduced event-free survival for aneurysm rupture or repair (P=0.0275), all-cause mortality (P=0.0635), and aneurysm-related mortality (P=0.0590). Baseline abdominal aortic aneurysm diameter (P<0.0001) and current smoking habit (P=0.0446) also predicted the primary outcome, and the addition of USPIO enhancement to the multivariate model did not improve event prediction (c-statistic, 0.7935-0.7936). CONCLUSIONS:USPIO-enhanced MRI is a novel approach to the identification of aortic wall cellular inflammation in patients with abdominal aortic aneurysms and predicts the rate of aneurysm growth and clinical outcome. However, it does not provide independent prediction of aneurysm expansion or clinical outcomes in a model incorporating known clinical risk factors. CLINICAL TRIAL REGISTRATION:URL: http://www.isrctn.com. Unique identifier: ISRCTN76413758.

Project description:IntroductionAbdominal computed tomography (CT) can accurately demonstrate organs and vascular structures around the stomach, and its potential role for image guidance is becoming increasingly established. However, solely using two-dimensional CT images to identify critical anatomical structures is undeniably challenging and not surgeon-friendly. To validate the feasibility of a patient-specific 3-D surgical navigation system for preoperative planning and intraoperative guidance during robotic gastric cancer surgery.Materials and methodsA prospective single-arm open-label observational study was conducted. Thirty participants underwent robotic distal gastrectomy for gastric cancer using a virtual surgical navigation system that provides patient-specific 3-D anatomical information with a pneumoperitoneum model using preoperative CT-angiography. Turnaround time and the accuracy of detecting vascular anatomy with its variations were measured, and perioperative outcomes were compared with a control group after propensity-score matching during the same study period.ResultsAmong 36 registered patients, 6 were excluded from the study. Patient-specific 3-D anatomy reconstruction was successfully implemented without any problems in all 30 patients using preoperative CT. All vessels encountered during gastric cancer surgery were successfully reconstructed, and all vascular origins and variations were identical to operative findings. The operative data and short-term outcomes between the experimental and control group were comparable. The experimental group showed shorter anesthesia time (218.6 min vs. 230.3 min; P=0.299), operative time (177.1 min vs. 193.9 min; P=0.137), and console time (129.3 min vs. 147.4 min; P=0.101) than the control group, although the differences were not statistically significant.ConclusionsPatient-specific 3-D surgical navigation system for robotic gastrectomy for gastric cancer is clinically feasible and applicable with an acceptable turnaround time. This system enables patient-specific preoperative planning and intraoperative navigation by visualizing all the anatomy required for gastrectomy in 3-D models without any error.Clinical trial registrationClinicaltrials.gov, identifier NCT05039333.

Dataset Information

Surgical Training for the Management of Intraoperative Aneurysm Rupture Using a Three-Dimensional Artificial Model.

Publications

Surgical Training for the Management of Intraoperative Aneurysm Rupture Using a Three-Dimensional Artificial Model.

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