Learning curve analysis of applying Seprafilm hyaluronic acid/carboxymethylcellulose membrane during laparoscopic hysterectomy

Abstract

This study was designed to evaluate the learning curve of applying Seprafilm (modified hyaluronic acid and carboxymethylcellulose; Genzyme, Cambridge, MA, USA) during laparoscopic hysterectomy or subtotal hysterectomy with or without adnexectomy. In this retrospective cohort study, 35 patients who underwent laparoscopic hysterectomy or subtotal hysterectomy with or without adnexectomy were enrolled. The Seprafilm was cut into 4 pieces, rolled up with a trimmed plastic sleeve and delivered through an incision wound made for the 5-mm ancillary trocar. The membrane was unrolled and placed on the rough surface after hysterectomy or subtotal hysterectomy with or without adnexectomy. The time from the insertion of the first piece of membrane into the abdominal cavity to the complete removal of the trimmed plastic sleeve was recorded. The median time for Seprafilm placement was 3 min. The learning curve was analyzed using the power-law method and suggested that 10 cases were required to achieve proficiency in the procedure. The presence of adnexectomy was significantly associated with the time required for Seprafilm placement (P < 0.001). Although Seprafilm placement is more complicated compared to the liquid and gel forms of anti-adhesion barriers, surgical proficiency seemed to be attained after 10 cases for an experienced surgeon.

Simple and reliable method for the application of Seprafilm® during laparoscopic surgery

“…Additionally, it is much easier to pull out the gauze than the backing paper of Seprafilm through a trocar. Through our method, the average time to complete a placement for one sheet of Seprafilm is approximately 100 seconds, which is acceptable as stated in other reports 4,5,7 . The important technical knack of this method is to remove remaining fluid in the abdominal cavity and at intra‐trocar sites before applying Seprafilm.…”

“…However, the fragile and sticky characteristics of Seprafilm cause many surgeons to hesitate from using it for deep surgical sites, such as the pelvic wall and retroperitoneal surface, with laparoscopic techniques. There have been several studies that introduced different methods for the placement of Seprafilm during laparoscopic surgery, such as grasping and rolling it directly with forceps, [3][4][5] wrapping it with its backing paper, 6,7 and using an introducer. [8][9][10][11] Although those methods are well-designed and performed placement of Seprafilm with high success rates, some weak points remain.…”

“…Through our method, the average time to complete a placement for one sheet of Seprafilm is approximately 100 seconds, which is acceptable as stated in other reports. 4,5,7 The important technical knack of this method is to remove remaining fluid in the abdominal cavity and at intra-trocar sites before applying Seprafilm. This process can prevent Seprafilm from tearing and adhering to unintentional sites.…”

Clinical feasibility of absorbable gelatin film adhesion barrier (GM142 “TENALEAF®”) in gynecological laparoscopic surgery: Safety assessment for first‐in‐human use and surgical video

“…GM142 showed fewer abnormal changes in bleeding-related laboratory test results compared with Seprafilm, and certain physiological benefits of the gelatin film as an adhesion barrier were suggested. Because trocars were not used in that GM142 study and recent reports 16,17 demonstrated the laparoscopic application of Seprafilm, a study directly comparing laparoscopic application of GM142 and Seprafilm would be interesting.…”

Overview of Machine Learning Process Modelling

“…Interestingly, the results contradict the findings of Heathcote et al [ 11 ] who were modeling and observing human cognitive performance and found out that the exponential law is the best to describe an individual learner and that the power law may be observed only at the generalization level. However, the power law was again better at describing a combined motor-cognitive task [ 40 ]. There is additional research needed to explain why and when human and machine learners might be different in their performances.…”