Prospective Implementation and Evaluation of a Decision-Tree Algorithm for Route of Hysterectomy

Abstract

OBJECTIVE: To evaluate the rate of vaginal hysterectomy and outcomes after initiation of a prospective decision-tree algorithm to determine the optimal surgical route of hysterectomy. METHODS: A prospective algorithm to determine optimal route of hysterectomy was developed, which uses the following factors: history of laparotomy, uterine size, and vaginal access. The algorithm was implemented at our institution from November 24, 2015, to December 31, 2017, for patients requiring hysterectomy for benign indications. Expected route of hysterectomy was assigned by the algorithm and was compared with the actual route performed to identify compliance compared with deviation. Surgical outcomes were analyzed. RESULTS: Of 365 patients who met inclusion criteria, 202 (55.3%) were expected to have a total vaginal hysterectomy, 57 (15.6%) were expected to have an examination under anesthesia followed by total vaginal hysterectomy, 52 (14.2%) were expected to have an examination under anesthesia followed by robotic-assisted total laparoscopic hysterectomy, and 54 (14.8%) were expected to have an abdominal or robotic-laparoscopic route of hysterectomy. Forty-six procedures (12.6%) deviated from the algorithm to a more invasive route (44 robotic, two abdominal). Seven patients had total vaginal hysterectomy when robotic-assisted total laparoscopic hysterectomy or abdominal hysterectomy was expected by the algorithm. Overall, 71% of patients were expected to have a vaginal route of hysterectomy per the algorithm, of whom 81.5% had a total vaginal hysterectomy performed; more than 99% of the total vaginal hysterectomies attempted were successfully completed. CONCLUSION: Vaginal surgery is feasible, carries a low complication rate with excellent outcomes, and should have a place in gynecologic surgery. National use of this prospective algorithm may increase the rate of total vaginal hysterectomy and decrease health care costs.