In:
International Journal of Surgery, Ovid Technologies (Wolters Kluwer Health)
Kurzfassung:
Intestinal anastomosis is a clinical procedure widely used to reconstruct the digestive tract, but authentic laparoscopic intracorporeal intestinal anastomosis（LIIA） models are lacking. However, three-dimensional (3D) printing can enable authentic and reusable models. In this paper, a novel cost-effective 3D-printing training model of LIIA is designed and the authenticity and validity of the model are tested. Methods: An FDM 3D printing and assembled lab model were built to test LIIA. Fifteen surgeons were required to perform LIIA, and their operation score and time were recorded and analyzed. Five experts were invited to assess the face and content validity of the models. A study was also performed to further evaluate and validate the learning curve of surgeons. Results: The difference in Modified anastomosis objective structured assessment of technical skills (MAOSATS) scores between the expert, intermediate and novice groups were significant (64.1±1.8: 48.5±1.7: 29.5±3.1, P 〈 0.001). In addition, the operation time of the procedure was statistically different for all three groups (21.5±1.9: 30.6±2.8:70.7±4.0, P 〈 0.001 ). The five experts rated the face and content validity of the model very highly, with the median being four out of five. Surgeons who underwent repeated training program showed improved surgical performance. After eight training, the novices’ performance was similar to that of the average level of untrained intermediates, while the operation scores of the intermediates were close to that of the average level of experts. Conclusions: In this study, it is found that the LIIA model exhibits excellent face, content and construct validity, repeated simulation training of the LIIA training program improved the surgeon’s operative performance, so the model is considered one of the effective methods for LIIA training and assessment of surgical quality in the future and for reducing healthcare costs.
Materialart:
Online-Ressource
ISSN:
1743-9191
DOI:
10.1097/JS9.0000000000000582
Sprache:
Englisch
Verlag:
Ovid Technologies (Wolters Kluwer Health)
Publikationsdatum:
2023
ZDB Id:
2201966-2
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