Development and validation of a simulation training platform for the ligation of deep dorsal vein complex in radical prostatectomy

ObjectiveThis study aimed to design a low-cost, simulation training platform for the ligation of deep dorsal vein (DVC) complex in radical prostatectomy and validate its training effectiveness.MethodsA simplified prostate urethra model was produced by 0-degree silica gel and pulse pressure banding. This model was placed on a slope of about 30 degrees using cardboard to thus creating a narrow environment of the pelvis. The DVC ligation was performed by a 2D laparoscopy simulator. A total of 27 participants completed the study include 13 novices, 10 surgical residents and 4 urology experts. The novices were trained five trails with 24 hours interval, the residents and experts completed the DVC ligation once. The construct validity of this simulation training platform was performed by completing time, the GOALS (Global Operative Assessment of Laparoscopic Skills) and TSA (i.e. Task Specific Assessments) score. The face validity and content validity were performed by a specific closed-ended questionnaire.ResultsThere was no significant difference among three groups in demographic or psychometric variables (p > 0.05). Compared to the novices, the residents spend a shorter time to complete the DVC ligation (p < 0.05) and had higher GOALS scores (p < 0.05), but had no significant difference in TSA scores (p > 0.05). Additionally, the experts groups had a better performance compared to residents group in the completing time (p < 0.05), GOALS score (p < 0.05) and TSA score (p < 0.05). The learning curve of novices significantly promoted along with the increased times of training. Almost 90 percent of subjects considered that this simulator had a good performance in the realism and practicability.ConclusionWe developed a novel low-cost a simulation training platform for the ligation of deep dorsal vein complex in radical prostatectomy, and this simulator had a good performance in the construct validity, face validity and content validity.