title: Constructing predictive models for vaginal surgery in patients with noninvasive gynecological conditions.
abstract: OBJECTIVE
  To develop predictive models for vaginal operative route selection based on clinical variables that can be easily assessed preoperatively in patients with noninvasive gynecological conditions.
  
  
  METHODS
  Retrospective study.
  
  
  METHODS
  University Hospital.
  
  
  METHODS
  Women with routine gynecological surgeries via different approaches.
  
  
  METHODS
  The medical records of 315 women without prolapse and undergoing hysterectomy, adnexal cystectomy or myomectomy were reviewed. Multiple logistic regression analysis was used to identify factors associated with the vaginal approach for each procedure. Predictive models were generated and optimal cut-off points were identified using the receiver operating characteristic curve.
  
  
  METHODS
  Predictive models for different vaginal surgical procedures.
  
  
  RESULTS
  For hysterectomy, the patient's body mass index, dysmenorrheal complaints and uterine size were identified as negative predictors for vaginal hysterectomy, whereas previous vaginal delivery was positive. For adnexal cystectomy, adnexal pathology was a negative predictor, whereas previous vaginal delivery and ovarian cyst size were positive. For myomectomy, the body mass index and number of fibroids were negative predictors while previous vaginal delivery was positive. All three models were able to predict the vaginal procedures undergone by women and the areas under the curve were 0.88, 0.95 and 0.92, respectively. Each optimal model cut-off value (logit(p) = 0.53, 0.36, 0.73) resulted in good sensitivity (92.3%, 100% and 87.5%, respectively) and specificity (77.8%, 88.6% and 90.9%, respectively).
  
  
  CONCLUSIONS
  These predictive models, which used clinical variables that can be easily assessed preoperatively, may help surgeons to select candidates for different vaginal procedures.
authors.affiliation: Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Yuan
authors.forename: Hong
authors.initials: H
authors.lastname: Zhou
authors.forename: Hao
authors.initials: H
authors.lastname: Zhang
authors.forename: Haosha
authors.initials: H
authors.lastname: Tang
authors.forename: Mo
authors.initials: M
authors.lastname: Chen
authors.forename: Xishi
authors.initials: X
authors.lastname: Liu
authors.forename: Congjian
authors.initials: C
authors.lastname: Xu
authors.forename: Liangqing
authors.initials: L
authors.lastname: Yao
mesh_terms: D000328:Adult; D005260:Female; D013509:Gynecologic Surgical Procedures / Q000379:methods*; D006801:Humans; D007045:Hysterectomy, Vaginal / Q000379:methods; D008875:Middle Aged; D008953:Models, Anatomic; D012189:Retrospective Studies; D014599:Uterus / Q000601:surgery*; D014621:Vagina / Q000601:surgery*
keywords: Predictive model; adnexal cystectomy; hysterectomy; myomectomy; vaginal surgery