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

Jennifer J. Schmitt, Mary V. Baker, John A. Occhino, Michaela E. McGree, Amy L. Weaver, Jamie N. Bakkum-Gamez, Sean C. Dowdy, Kalyan S. Pasupathy, John B. Gebhart

Research output: Contribution to journalReview articlepeer-review

Abstract

Between 2007 and 2010, the rate of total vaginal hysterectomy (TVH) in the United States among commercially insured patients undergoing hysterectomy decreased from 21.7% to 19.8% and by 2013 decreased to 11.5%. Between 2007 and 2010, use of robotic-assisted hysterectomy increased from 0.5% to 9.5%. Because TVH is the most cost-effective route and has a low complication rate, it is recommended when feasible. Algorithms have been developed based on a history of laparotomy, vaginal access, and uterine size, but are not widely used. In a previous study, a clinical decision-tree algorithm for benign hysterectomies was created by many of the authors of the present study who applied it retrospectively to 2 patient cohorts: those treated before (2004 2005) and after (2009 2013) the advent of robotic surgery (Obstet Gynecol 2017;129:130 138). In that study, 15.1% of hysterectomies deviated from the algorithm to a more invasive route before the initiation of robotic surgery and 25.8% afterward. When the algorithm suggested using TVH and it was performed, patients had better outcomes than those who had robotic hysterectomy. The primary aim of this study was to assess the optimal surgical route for hysterectomy using a prospective algorithm and decision tree based on history of laparotomy, uterine size, and vaginal access. The algorithm was implemented for all patients requiring a hysterectomy for benign indications at the Mayo Clinic, Rochester, Minn, between November 24, 2015, and December 31, 2017. Expected route of hysterectomy for each patient was determined using the prospective algorithm and was compared with the actual route performed to identify compliance compared with deviation. The previously published results from the authors' retrospective algorithm applied to 2009 2013 institutional data were compared with the prospective use of the algorithm. Of the 365 women meeting the criteria for inclusion, 202 (55.3%) were expected to have a TVH, 57 (15.6%) were expected to have an examination under anesthesia to evaluate its effect on the rate of deviation from the algorithm and then TVH, 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. A total of 46 procedures (12.6%) deviated from the algorithm to a more invasive route (44 were robotic when vaginal was expected, and 2 were abdominal when vaginal was expected). Seven patients (1.9%) underwent TVH when robotic-assisted total laparoscopic hysterectomy or abdominal hysterectomy was expected by the algorithm. Seventy-one percent of patients (259/365) were expected to have a vaginal route of hysterectomy per the algorithm; 81.5% of these women (211/259) underwent a TVH, and more than 99% (209/211) of the total vaginal hysterectomies attempted were successfully completed. These findings show that the TVH is feasible, has a low complication rate and excellent outcomes, and should be used in more than 50% of hysterectomies. Skilled vaginal surgeons can provide TVH as a safe and effective option, even in women for whom the algorithm would predict a more invasive approach. If this prospective algorithm is used nationally, the rate of TVH may increase while health care costs decrease.

Original languageEnglish (US)
Pages (from-to)465-466
Number of pages2
JournalObstetrical and Gynecological Survey
Volume75
Issue number8
DOIs
StatePublished - Aug 1 2020

ASJC Scopus subject areas

  • Obstetrics and Gynecology

Fingerprint Dive into the research topics of 'Prospective Implementation and Evaluation of a Decision-Tree Algorithm for Route of Hysterectomy'. Together they form a unique fingerprint.

Cite this