Simulation training approaches in intracranial aneurysm surgery-a systematic review.

BACKGROUND With the increasing complexity and decreasing exposure to intracranial aneurysm surgery, training and maintenance of the surgical skills have become challenging. This review elaborated on simulation training for intracranial aneurysm clipping. METHODS A systematic review was performed according to the PRISMA guidelines to identify studies on aneurysm clipping training using models and simulators. The primary outcome was the identification of the predominant modes of the simulation process, models, and training methods associated with a microsurgical learning curve. The secondary outcomes included assessments of the validation of such simulators and the learning capability from the use of such simulators. RESULTS Of the 2068 articles screened, 26 studies met the inclusion criteria. The chosen reports used a wide range of simulation approaches including ex vivo methods (n = 6); virtual reality (VR) platforms (n = 11); and static (n = 6) and dynamic (n = 3) 3D-printed aneurysm models (n = 6). The ex vivo training methods have limited availability, VR simulators lack haptics and tactility, while 3D static models lack important microanatomical components and the simulation of blood flow. 3D dynamic models including pulsatile flow are reusable and cost-effective but miss microanatomical components. CONCLUSIONS The existing training methods are heterogenous and do not realistically simulate the complete microsurgical workflow. The current simulations lack certain anatomical features and crucial surgical steps. Future research should focus on developing and validating a reusable, cost-effective training platform. No systematic validation method exists for the different training models, so there is a need to build homogenous assessment tools and validate the role of simulation in education and patient safety

3D-printed head model in patient's education for micro-neurosurgical aneurysm clipping procedures.

BACKGROUND Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and 3D reconstruction from Digital Subtraction Angiography (DSA) are currently used in clinical consultations for patients diagnosed with intracranial aneurysms; however, they have limitations in helping patients understand the disease and possible treatments. This study investigates the use of a 3D-printed model of the patients' neurosurgical anatomy and vascular pathology as an educational tool in outpatient clinics. METHODS A 3D-printed model of a middle cerebral artery aneurysm was created for use during patient consultations to discuss microsurgical treatment of unruptured cerebral aneurysms. In total, 38 patients and 5 neurosurgeons were included in the study. After the consultation, the patients and neurosurgeons received a questionnaire to assess the effectiveness of the 3D-printed model as an educational tool. RESULTS The 3D model improved the patients' understanding of the diagnosis, the aneurysm's relationship to the parent artery; the treatment process as well as the risks if left untreated. The patients found the 3D model to be an interesting tool (97%). The neurosurgeons were satisfied with the 3D-printed model as a patient encounter tool, they found the model effective during consultation (87%) and better than the conventional education tools used during consultations (97%). CONCLUSION Using a 3D model improves communication, enhances the patient's understanding of the pathology and its treatment and potentially facilitates the informed consent process in patients undergoing intracranial aneurysm surgery

Training Performance Assessment for Intracranial Aneurysm Clipping Surgery Using a Patient-Specific Mixed-Reality Simulator: A Learning Curve Study.

BACKGROUND AND OBJECTIVES The value of simulation-based training in medicine and surgery has been widely demonstrated. This study investigates the introduction and use of a new mixed-reality neurosurgical simulator in aneurysm clipping surgery, focusing on the learning curve and performance improvement. METHODS Five true-scale craniotomy head models replicating patient-specific neuroanatomy, along with a mixed-reality simulator, a neurosurgical microscope, and a set of microsurgical instruments and clips, were used in the operation theater to simulate aneurysm microsurgery. Six neurosurgical residents participated in five video-recorded simulation sessions over 4 months. Complementary learning modalities were implemented between sessions. Thereafter, three blinded analysts reported on residents' use of the microscope, quality of manipulation, aneurysm occlusion, clipping techniques, and aneurysm rupture. Data were also captured regarding training time and clipping attempts. RESULTS Over the course of training, clipping time and number of clipping attempts decreased significantly (P = .018, P = .032) and the microscopic skills improved (P = .027). Quality of manipulation and aneurysm occlusion scoring improved initially although the trend was interrupted because the spacing between sessions increased. Significant differences in clipping time and attempts were observed between the most and least challenging patient models (P = .005, P = .0125). The least challenging models presented higher rates of occlusion based on indocyanine green angiography evaluation from the simulator. CONCLUSION The intracranial aneurysm clipping learning curve can be improved by implementing a new mixed-reality simulator in dedicated training programs. The simulator and the models enable comprehensive training under the guidance of a mentor

An SVM-Based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis

The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels

Monitoring the Depth of Anaesthesia

One of the current challenges in medicine is monitoring the patients’ depth of general anaesthesia (DGA). Accurate assessment of the depth of anaesthesia contributes to tailoring drug administration to the individual patient, thus preventing awareness or excessive anaesthetic depth and improving patients’ outcomes. In the past decade, there has been a significant increase in the number of studies on the development, comparison and validation of commercial devices that estimate the DGA by analyzing electrical activity of the brain (i.e., evoked potentials or brain waves). In this paper we review the most frequently used sensors and mathematical methods for monitoring the DGA, their validation in clinical practice and discuss the central question of whether these approaches can, compared to other conventional methods, reduce the risk of patient awareness during surgical procedures

Follicular fluid content and oocyte quality: from single biochemical markers to metabolomics

The assessment of oocyte quality in human in vitro fertilization (IVF) is getting increasing attention from embryologists. Oocyte selection and the identification of the best oocytes, in fact, would help to limit embryo overproduction and to improve the results of oocyte cryostorage programs. Follicular fluid (FF) is easily available during oocyte pick-up and theorically represents an optimal source on non-invasive biochemical predictors of oocyte quality. Unfortunately, however, the studies aiming to find a good molecular predictor of oocyte quality in FF were not able to identify substances that could be used as reliable markers of oocyte competence to fertilization, embryo development and pregnancy. In the last years, a well definite trend toward passing from the research of single molecular markers to more complex techniques that study all metabolites of FF has been observed. The metabolomic approach is a powerful tool to study biochemical predictors of oocyte quality in FF, but its application in this area is still at the beginning. This review provides an overview of the current knowledge about the biochemical predictors of oocyte quality in FF, describing both the results coming from studies on single biochemical markers and those deriving from the most recent studies of metabolomic

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700