Simulation-based training

Simulation-based training

Simulation is the imitation of the operation of a real-world process or system over time.

The recent emphasis on simulation-based training in neurosurgery has led to the development of many simulation models and training courses.

Simulation-based training is increasingly being used for assessment and training of psychomotor skills involved in medicine. The application of artificial intelligence and machine learning technologies has provided new methodologies to utilize large amounts of data for educational purposes. A significant criticism of the use of artificial intelligence in education has been a lack of transparency in the algorithms’ decision-making processes.

A study aimed to 1) introduce a new framework using explainable artificial intelligence for simulation-based training in surgery, and 2) validate the framework by creating the Virtual Operative Assistant, an automated educational feedback platform. Twenty-eight skilled participants (14 staff neurosurgeons, 4 fellows, 10 PGY 4-6 residents) and 22 novice participants (10 PGY 1-3 residents, 12 medical students) took part in this study. Participants performed a virtual reality subpial brain tumor resection task on the NeuroVR simulator using a simulated ultrasonic aspirator and bipolar.

Metrics of performance were developed, and leave-one-out cross validation was employed to train and validate a support vector machine in Matlab. The classifier was combined with a unique educational system to build the Virtual Operative Assistant which provides users with automated feedback on their metric performance with regards to expert proficiency performance benchmarks. The Virtual Operative Assistant successfully classified skilled and novice participants using 4 metrics with an accuracy, specificity and sensitivity of 92, 82 and 100%, respectively. A 2-step feedback system was developed to provide participants with an immediate visual representation of their standing related to expert proficiency performance benchmarks. The educational system outlined establishes a basis for the potential role of integrating artificial intelligence and virtual reality simulation into surgical educational teaching. The potential of linking expertise classification, objective feedback based on proficiency benchmarks, and instructor input creates a novel educational tool by integrating these three components into a formative educational paradigm 1).


Patel et al. aimed to identify the currently available simulators and training courses for neurosurgery, assess their validity and determine their effectiveness.

Both Medline and EMBASE were searched for English language articles that validate simulation models for neurosurgery. Each study was screened according to Messick’s validity framework, and rated in each domain. McGaghie’s model of translational outcomes was then used to determine a level of effectiveness (LoE) for each simulator or training course.

Upon screening of 6006 articles, 114 were identified either validating or determining a LoE for 108 simulation-based training models or courses. Achieving the highest rating for each validity domain were: six models and training courses for content validity; 12 for response processes; 4 for internal structure; 14 for relations to other variables and none for consequences. For translational outcomes, 6 simulators or training achieved a LoE of greater than 2 and thus demonstrated skills transfer beyond the simulation setting.

With the advent of increasing neurosurgery simulators and training tools, there is a need for more validity studies. Further attempts to investigate translational outcomes to the operating theatre when using these simulators are particularly warranted. Finally, more training tools incorporating full immersion simulation and non-technical skills training are recommended 2) 3).


The current simulation technology used for neurosurgical training leaves much to be desired. Significant efforts are thoroughly exhausted in hopes of developing simulations that translate to give learners the “real-life” feel. Though a respectable goal, this may not be necessary as the application for simulation in neurosurgical training may be most useful in early learners. The ultimate uniformly agreeable endpoint of improved outcome and patient safety drives these investments 4).


Medicine and surgery are turning towards simulation to improve on limited patient interaction during residency training. Many simulators today utilize virtual reality with augmented haptic feedback with little to no physical elements.

To optimize the learning exercise, it is essential that both visual and haptic simulators are presented to best present a real-world experience. Many systems attempt to achieve this goal through a total virtual interface.

Bova et al., approach has been to create a mixed-reality system consisting of a physical and a virtual component. A physical model of the head or spine is created with a 3-dimensional printer using deidentified patient data. The model is linked to a virtual radiographic system or an image guidance platform. A variety of surgical challenges can be presented in which the trainee must use the same anatomic and radiographic references required during actual surgical procedures.

Using the aforementioned techniques, they have created a ventriculostomy simulators, percutaneous stereotactic lesion procedure for trigeminal neuralgia, and spinal instrumentation.

The system has provided the residents an opportunity to understand and appreciate the complex 3-dimensional anatomy of the 3 neurosurgical procedures simulated. The systems have also provided an opportunity to break procedures down into critical segments, allowing the user to concentrate on specific areas of deficiency 5).


Multiple simulators have been developed for neurosurgical training, including those for minimally invasive procedures, vascular, skull base, pediatric, tumor resection, functional neurosurgery, and spine surgery.

Advances in imaging and computer technology have led to the development of different simulation models to complement traditional surgical training. Sophisticated virtual reality (VR) simulators with haptic feedback and impressive imaging technology have provided novel options for training in neurosurgery. Breakthrough training simulation using 3D printing technology holds promise for future simulation practice, proving high-fidelity patient-specific models to complement residency surgical learning 6).


Shakur et al., developed a real-time augmented reality simulator for percutaneous trigeminal rhizotomy using the ImmersiveTouch platform. Ninety-two neurosurgery residents tested the simulator at American Association of Neurological Surgeons Top Gun 2014. Postgraduate year (PGY), number of fluoroscopy shots, the distance from the ideal entry point, and the distance from the ideal target were recorded by the system during each simulation session. Final performance score was calculated considering the number of fluoroscopy shots and distances from entry and target points (a lower score is better). The impact of PGY level on residents’ performance was analyzed.

Seventy-one residents provided their PGY-level and simulator performance data; 38% were senior residents and 62% were junior residents. The mean distance from the entry point (9.4 mm vs 12.6 mm, P = .01), the distance from the target (12.0 mm vs 15.2 mm, P = .16), and final score (31.1 vs 37.7, P = .02) were lower in senior than in junior residents. The mean number of fluoroscopy shots (9.8 vs 10.0, P = .88) was similar in these 2 groups. Linear regression analysis showed that increasing PGY level is significantly associated with a decreased distance from the ideal entry point (P = .001), a shorter distance from target (P = .05), a better final score (P = .007), but not number of fluoroscopy shots (P = .52).

Because technical performance of percutaneous rhizotomy increases with training, they proposed that the skills in performing the procedure in there virtual reality model would also increase with PGY level, if this simulator models the actual procedure. The results confirm this hypothesis and demonstrate construct validity 7).


Simulation technology identifies neurosurgical residency applicants with differing levels of technical ability. These results provide information for studies being developed for longitudinal studies on the acquisition, development, and maintenance of psychomotor skills. Technical abilities customized training programs that maximize individual resident bimanual psychomotor training dependant on continuously updated and validated metrics from virtual reality simulation studies should be explored 8).


Surgical education is moving rapidly to the use of simulation for technical training of residents and maintenance or upgrading of surgical skills in clinical practice. To optimize the learning exercise, it is essential that both visual and haptic cues are presented to best present a real-world experience. Many systems attempt to achieve this goal through a total virtual interface.

To demonstrate that the most critical aspect in optimizing a simulation experience is to provide the visual and haptic cues, allowing the training to fully mimic the real-world environment.

Bova et al approach has been to create a mixed-reality system consisting of a physical and a virtual component. A physical model of the head or spine is created with a 3-dimensional printer using deidentified patient data. The model is linked to a virtual radiographic system or an image guidance platform. A variety of surgical challenges can be presented in which the trainee must use the same anatomic and radiographic references required during actual surgical procedures.

Using the aforementioned techniques, they have created simulators for ventriculostomy, percutaneous stereotactic lesion procedure for trigeminal neuralgia, and spinal instrumentation. The design and implementation of these platforms are presented.

The system has provided the residents an opportunity to understand and appreciate the complex 3-dimensional anatomy of the 3 neurosurgical procedures simulated. The systems have also provided an opportunity to break procedures down into critical segments, allowing the user to concentrate on specific areas of deficiency 9).

References

1)

Mirchi N, Bissonnette V, Yilmaz R, Ledwos N, Winkler-Schwartz A, Del Maestro RF. The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS One. 2020 Feb 27;15(2):e0229596. doi: 10.1371/journal.pone.0229596. eCollection 2020. PubMed PMID: 32106247.
2)

Patel E, Aydin A, Cearns M, Dasgupta P, Ahmed K. A Systematic Review of Simulation-based Training in Neurosurgery, Part 1: Cranial Neurosurgery. World Neurosurg. 2019 Sep 18. pii: S1878-8750(19)32430-1. doi: 10.1016/j.wneu.2019.08.262. [Epub ahead of print] PubMed PMID: 31541755.
3)

Patel E, Aydin A, Cearns M, Dasgupta P, Ahmed K. A Systematic Review of Simulation-based Training in Neurosurgery, Part 2: Spinal and Paediatric Surgery, Neurointerventional Radiology and Non-Technical Skills. World Neurosurg. 2019 Sep 18. pii: S1878-8750(19)32442-8. doi: 10.1016/j.wneu.2019.08.263. [Epub ahead of print] PubMed PMID: 31541754.
4)

Konakondla S, Fong R, Schirmer CM. Simulation training in neurosurgery: advances in education and practice. Adv Med Educ Pract. 2017 Jul 14;8:465-473. doi: 10.2147/AMEP.S113565. eCollection 2017. Review. PubMed PMID: 28765716; PubMed Central PMCID: PMC5524176.
5) , 9)

Bova FJ, Rajon DA, Friedman WA, Murad GJ, Hoh DJ, Jacob RP, Lampotang S, Lizdas DE, Lombard G, Lister JR. Mixed-reality simulation for neurosurgical procedures. Neurosurgery. 2013 Oct;73 Suppl 1:138-45. doi: 10.1227/NEU.0000000000000113. PubMed PMID: 24051877.
6)

Rehder R, Abd-El-Barr M, Hooten K, Weinstock P, Madsen JR, Cohen AR. The role of simulation in neurosurgery. Childs Nerv Syst. 2016 Jan;32(1):43-54. doi: 10.1007/s00381-015-2923-z. Review. PubMed PMID: 26438547.
7)

Shakur SF, Luciano CJ, Kania P, Roitberg BZ, Banerjee PP, Slavin KV, Sorenson J, Charbel FT, Alaraj A. Usefulness of a Virtual Reality Percutaneous Trigeminal Rhizotomy Simulator in Neurosurgical Training. Neurosurgery. 2015 Sep;11 Suppl 3:420-5; discussion 425. doi: 10.1227/NEU.0000000000000853. PubMed PMID: 26103444.
8)

Winkler-Schwartz A, Bajunaid K, Mullah MA, Marwa I, Alotaibi FE, Fares J, Baggiani M, Azarnoush H, Zharni GA, Christie S, Sabbagh AJ, Werthner P, Del Maestro RF. Bimanual Psychomotor Performance in Neurosurgical Resident Applicants Assessed Using NeuroTouch, a Virtual Reality Simulator. J Surg Educ. 2016 Jul 6. pii: S1931-7204(16)30026-5. doi: 10.1016/j.jsurg.2016.04.013. [Epub ahead of print] PubMed PMID: 27395397.

Microsurgical Basics and Bypass Techniques

Microsurgical Basics and Bypass Techniques

by Evgenii Belykh (Author), Nikolay L. Martirosyan (Author), M. Yashar Kalani (Author), Peter Nakaji (Author)

A step-by-step manual on fundamental microsurgical bypass techniques young neurosurgeons need to master!

All neurosurgeons must undergo rigorous training in the laboratory and practice bypass techniques repetitively before performing microneurosurgery on a patient. Microsurgical Basics and Bypass Techniques by Evgenii Belykh, Nikolay Martirosyan, M. Yashar S. Kalani, and Peter Nakaji is a comprehensive yet succinct manual on fundamental laboratory techniques rarely included in clinical textbooks. The resource simplifies repetitive microsurgical practice in the laboratory by providing a menu of diverse, progressively challenging exercises.

Step-by-step instructions accompanied by easy-to-understand illustrations, expert commentary, and videos effectively bridge the gap between laboratory practice and operating room performance. The book starts with an opening chapter on four founding principles of microsurgical practice inherited from great thinkers and concludes with a chapter featuring cerebrovascular bypass cases. Chapters 2-8 offer a complete one-week curriculum, with a different lab exercise each day, focused on learning basic microsurgery skills.

Key Features

Twenty-six videos cover a wide array of topics – from diverse methods for holding instruments and suturing techniques – to end-to-end, end-to-side, and side-to-side anastomosis procedures

High quality color illustrations clearly demonstrate basic techniques

Practical laboratory exercises include how to organize a microsurgical laboratory, essential training and skills, basic arterial and deep-field anastomoses, kidney autotransplantation, supermicrosurgery, and aneurysm clipping

Invaluable tips such as preventing bypass errors and applying laboratory skills to neurosurgical practice

This is an essential microsurgical learning and teaching guide for neurosurgical residents on how to perform basic bypass and anastomoses procedures step by step.

Part of the Fundamental Skills in Neurosurgery Series, Series Editors: Peter Nakaji, Vadim A. Byvaltsev, and Robert F. Spetzler.

This book includes complimentary access to a digital copy on https://medone.thieme.com.

Nosocomial infection

Nosocomial infection

see also Nosocomial meningitis.


Hospital-acquired infection (HAI) — also known as nosocomial infection — is an infection whose development is favored by a hospital environment, such as one acquired by a patient during a hospital visit or one developing among hospital staff.


In a single-center, retrospective analysis of critically ill pediatric trauma patients, nosocomial infections were more frequently observed in patients admitted following polytrauma with traumatic brain injury than in patients with isolated traumatic brain injury or trauma without traumatic brain injury 1).


In the last few decades, there has been a tremendous advancement in foetal and maternal care, and it has led to premature babies born as early as 25 weeks of gestation being nursed and cared for in neonatal and pediatric intensive care units. However, these children can pick up a number of uncommon and rare hospital-acquired infections including central nervous system infections.

Wagh and Sinha have given their own insight as to the prevention of healthcare-associated infections in paediatric intensive care settings and reviewed the current literature on the topic.

Healthcare-associated infections are largely preventable provided adequate prevention and protective measures are put in place and prevention guidelines are stritctly followed 2).


In the United States, the Centers for Disease Control and Prevention estimated roughly 1.7 million hospital-associated infections, from all types of microorganisms, including bacteria, combined, cause or contribute to 99,000 deaths each year.

In Europe, where hospital surveys have been conducted, the category of gram-negative infections are estimated to account for two-thirds of the 25,000 deaths each year. Nosocomial infections can cause severe pneumonia and infections of the urinary tract, bloodstream and other parts of the body. Many types are difficult to attack with antibiotics, and antibiotic resistance is spreading to gram-negative bacteria that can infect people outside the hospital.

Hospital-acquired infections are an important category of hospital-acquired conditions. HAI is sometimes expanded as healthcare associated infection to emphasize that infections can be correlated with health care in various settings (not just hospitals), which is also true of hospital-acquired conditions generally.


Data on nosocomial bloodstream infections (NBSI) in neurosurgery is limited. A study aimed to analyze the epidemiology, microbiology, outcome, and risk factors for death in neurosurgical patients with NBSI in a multidrug resistant setting.

Neurosurgical patients with a confirmed NBSI within the period 2003-2012 were retrospectively analyzed. NBSI was diagnosed when a pathogen was isolated from a blood sample obtained after the first 48 h of hospitalization. Patients’ demographic, clinical, and microbiological data were recorded and analyzed using univariate and multivariate analysis.

A total of 236 patients with nosocomial infection (NI) were identified and 378 isolates were recovered from blood cultures. Incidence of NI was 4.3 infections/1000 bed-days. Gram negative bacteria slightly predominated (54.5 %). The commonest bacteria were coagulase-negative Staphylococcus (CoNS, 26 %), Klebsiella pneumoniae (15.3 %), Pseudomonas aeruginosa (14.8 %), and Acinetobacter baumannii(13.2 %). Carbapenem resistance was found in 90 % of A. baumannii, in 66 % of P. aeruginosa, and in 22 % (2003-2007) to 77 % (2008-2012) of K. pneumoniae isolates (p < 0.05). Most CoNS and Staphylococcus aureus isolates (94 and 80 %, respectively) were methicillin-resistant. All Gram-negative isolates were sensitive to colistin and all Gram-positive isolates were sensitive to vancomycin and linezolid. Antimicrobial consumption decreased after 2007 (p < 0.05). Overall mortality was 50.4 %. In multivariate analysis, advanced age and stay in an Intermediate Care Unit (IMCU) were independent risk factors for in-hospital mortality (p < 0.05).

Overall, high incidence of NBSI and considerable resistance of Gram positive and particularly Gram negative bacteria were noted in neurosurgical patients. Mortality was high with advanced age and stay in IMCU being the most important death-related factor 3).


Case series

In one hundred fifty-three patients with aSAH. Delayed cerebral ischemia (DCI) was identified in 32 patients (20.9%). Nosocomial infection (odds ratio [OR] 3.5, 95% confidence interval [CI] 1.09-11.2, p = 0.04), ventriculitis (OR 25.3, 95% CI 1.39-458.7, p = 0.03), aneurysm re-rupture (OR 7.55, 95% CI 1.02-55.7, p = 0.05), and clinical vasospasm (OR 43.4, 95% CI 13.1-143.4, p < 0.01) were independently associated with the development of DCI. Diagnosis of nosocomial infection preceded the diagnosis of DCI in 15 (71.4%) of 21 patients. Patients diagnosed with nosocomial infection experienced significantly worse outcomes as measured by the modified Rankin Scale score at discharge and 1 year (p < 0.01 and p = 0.03, respectively).

Nosocomial infection is independently associated with DCI. This association is hypothesized to be partly causative through the exacerbation of systemic inflammation leading to thrombosis and subsequent ischemia 4).

References

1)

Sribnick EA, Hensley J, Moore-Clingenpeel M, Muszynski JA, Thakkar RK, Hall MW. Nosocomial Infection Following Severe Traumatic Injury in Children. Pediatr Crit Care Med. 2020 Feb 25. doi: 10.1097/PCC.0000000000002238. [Epub ahead of print] PubMed PMID: 32106190.
2)

Wagh A, Sinha A. Prevention of healthcare associated infections in pediatric intensive care unit. Childs Nerv Syst. 2018 Aug 18. doi: 10.1007/s00381-018-3909-4. [Epub ahead of print] PubMed PMID: 30121831.
3)

Tsitsopoulos PP, Iosifidis E, Antachopoulos C, Anestis DM, Karantani E, Karyoti A, Papaevangelou G, Kyriazidis E, Roilides E, Tsonidis C. Nosocomial bloodstream infections in neurosurgery: a 10-year analysis in a center with high antimicrobial drug-resistance prevalence. Acta Neurochir (Wien). 2016 Sep;158(9):1647-54. doi: 10.1007/s00701-016-2890-5. Epub 2016 Jul 25. PubMed PMID: 27452903.
4)

Foreman PM, Chua M, Harrigan MR, Fisher WS 3rd, Vyas NA, Lipsky RH, Walters BC, Tubbs RS, Shoja MM, Griessenauer CJ. Association of nosocomial infections with delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage. J Neurosurg. 2016 Dec;125(6):1383-1389. PubMed PMID: 26871202.

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