title: Determining Optimal Route of Hysterectomy for Benign Indications: Clinical Decision Tree Algorithm.
abstract: To evaluate practice change after initiation of a robotic surgery program using a clinical algorithm to determine the optimal surgical approach to benign hysterectomy.
  
  
  
  A retrospective postrobot cohort of benign hysterectomies (2009-2013) was identified and the expected surgical route was determined from an algorithm using vaginal access and uterine size as decision tree branches. We excluded the laparoscopic hysterectomy route. A prerobot cohort (2004-2005) was used to evaluate a practice change after the addition of robotic technology (2007). Costs were estimated.
  
  
  
  Cohorts were similar in regard to uterine size, vaginal parity, and prior laparotomy history. In the prerobot cohort (n=473), 320 hysterectomies (67.7%) were performed vaginally and 153 (32.3%) through laparotomy with 15.1% (46/305) performed abdominally when the algorithm specified vaginal hysterectomy. In the postrobot cohort (n=1,198), 672 hysterectomies (56.1%) were vaginal; 390 (32.6%) robot-assisted; and 136 (11.4%) abdominal. Of 743 procedures, 38 (5.1%) involved laparotomy and 154 (20.7%) involved robotic technique when a vaginal approach was expected. Robotic hysterectomies had longer operations (141 compared with 59 minutes, P<.001) and higher rates of surgical site infection (4.7% compared with 0.2%, P<.001) and urinary tract infection (8.1% compared with 4.1%, P=.05) but no difference in major complications (P=.27) or readmissions (P=.27) compared with vaginal hysterectomy. Algorithm conformance would have saved an estimated $800,000 in hospital costs over 5 years.
  
  
  
  When a decision tree algorithm indicated vaginal hysterectomy as the route of choice, vaginal hysterectomy was associated with shorter operative times, lower infection rate, and lower cost. Vaginal hysterectomy should be the route of choice when feasible.
authors.affiliation: Divisions of Gynecologic Surgery, Biomedical Statistics and Informatics, Health Care Policy and Research, Mayo Clinic, Rochester, Minnesota.
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Schmitt
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Carranza Leon
authors.forename: John A
authors.initials: JA
authors.lastname: Occhino
authors.forename: Amy L
authors.initials: AL
authors.lastname: Weaver
authors.forename: Sean C
authors.initials: SC
authors.lastname: Dowdy
authors.forename: Jamie N
authors.initials: JN
authors.lastname: Bakkum-Gamez
authors.forename: Kalyan S
authors.initials: KS
authors.lastname: Pasupathy
authors.forename: John B
authors.initials: JB
authors.lastname: Gebhart
mesh_terms: D000328:Adult; D000465:Algorithms; D000066491:Clinical Decision-Making / Q000379:methods*; D003663:Decision Trees; D005260:Female; D005831:Genital Diseases, Female / Q000601:surgery; D017721:Hospital Costs; D006801:Humans; D007044:Hysterectomy / Q000009:adverse effects / Q000191:economics / Q000379:methods* / Q000706:statistics & numerical data; D007045:Hysterectomy, Vaginal / Q000009:adverse effects / Q000191:economics / Q000706:statistics & numerical data; D008875:Middle Aged; D061646:Operative Time; D009929:Organ Size; D010359:Patient Readmission / Q000706:statistics & numerical data; D012189:Retrospective Studies; D065287:Robotic Surgical Procedures / Q000009:adverse effects / Q000191:economics / Q000706:statistics & numerical data; D013530:Surgical Wound Infection / Q000209:etiology; D014552:Urinary Tract Infections / Q000209:etiology; D014599:Uterus / Q000473:pathology*