An update on simulation training in rhinology: a systematic review of evidence

BACKGROUND

Rhinological procedures demand a high degree of technical expertise and anatomical knowledge. Because of limited surgical opportunities, ethical considerations and the complexity of these procedures, simulation-based training has become increasingly important. This review aimed to evaluate the effectiveness of simulation models used in rhinology training.

METHODS

Searches were conducted on PubMed, Embase, Cochrane and Google Scholar for studies conducted between July 2012 and July 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis ('PRISMA') protocol defined a final list of articles. Each validated study was assigned a level of evidence and a level of recommendation based on the Oxford Centre of Evidence-Based Medicine classification.

RESULTS

Following exclusions, 42 articles were identified which encompassed six types of simulation models and 26 studies evaluated validity. The rhinological skills assessed included endoscopic sinus surgery (n = 28), skull base/cerebrospinal fluid leak repair (n = 14), management of epistaxis and/or sphenopalatine artery ligation (n = 8), and septoplasty and septorhinoplasty (n = 6). All studies reported the beneficial impact of their simulation models on trainee development.

CONCLUSION

Simulation training in rhinology is a valuable adjunct to traditional surgical education. Although evidence is of moderate quality, the findings highlight the importance of simulation-based training in rhinology training.

Training models and simulators for endoscopic transsphenoidal surgery: a systematic review

Endoscopic transsphenoidal surgery is a novel surgical technique requiring specific training. Different models and simulators have been recently suggested for it, but no systematic review is available. To provide a systematic and critical literature review and up-to-date description of the training models or simulators dedicated to endoscopic transsphenoidal surgery. A search was performed on PubMed and Scopus databases for articles published until February 2023; Google was also searched to document commercially available. For each model, the following features were recorded: training performed, tumor/arachnoid reproduction, assessment and validation, and cost. Of the 1199 retrieved articles, 101 were included in the final analysis. The described models can be subdivided into 5 major categories: (1) enhanced cadaveric heads; (2) animal models; (3) training artificial solutions, with increasing complexity (from "box-trainers" to multi-material, ct-based models); (4) training simulators, based on virtual or augmented reality; (5) Pre-operative planning models and simulators. Each available training model has specific advantages and limitations. Costs are high for cadaver-based solutions and vary significantly for the other solutions. Cheaper solutions seem useful only for the first stages of training. Most models do not provide a simulation of the sellar tumor, and a realistic simulation of the suprasellar arachnoid. Most artificial models do not provide a realistic and cost-efficient simulation of the most delicate and relatively common phase of surgery, i.e., tumor removal with arachnoid preservation; current research should optimize this to train future neurosurgical generations efficiently and safely.