title: Multi-institutional validation of vasectomy reversal predictor.
abstract: OBJECTIVE
  Some urologists who perform vasectomy reversals are not experienced with performing VE. A model to preoperatively identify patients who may require referral to an experienced VE surgeon was created (). We tested the model at multiple institutions.
  
  
  METHODS
  The model had previously been designed in 483 patients who underwent vasectomy reversal at 1 institution (100% sensitive and 59% specific for predicting the need for VE). It was based on time since vasectomy and patient age. We tested it prospectively in 33 patients and retrospectively in a total of 312 at 6 other institutions. The predictive accuracy of the model was compared to using a simple duration from vasectomy cutoff alone, as is used in clinical practice.
  
  
  RESULTS
  The model had 84% sensitivity and 58% specificity for detecting the need for VE in a total of 345 patients at 7 institutions. If using only a duration from vasectomy cutoff of 10 years to predict the need for VE, sensitivity was only 69%. At a cutoff of 4 years sensitivity was 99% but specificity was only 23%. Thus, the model performed better than any specific duration cutoff alone.
  
  
  CONCLUSIONS
  The predictive model provides 84% sensitivity for detecting patients who may require VE during vasectomy reversal across 7 institutions (58% specificity). The model more accurately predicts the need for VE than using a specific duration from vasectomy cutoff alone.
authors.affiliation: Cleveland Clinic Foundation, Cleveland, Ohio, USA. sijo_p@hotmail.com
authors.forename: Sijo J
authors.initials: SJ
authors.lastname: Parekattil
authors.forename: Wayne
authors.initials: W
authors.lastname: Kuang
authors.forename: Peter N
authors.initials: PN
authors.lastname: Kolettis
authors.forename: Fabio F
authors.initials: FF
authors.lastname: Pasqualotto
authors.forename: Patrick
authors.initials: P
authors.lastname: Teloken
authors.forename: Claudio
authors.initials: C
authors.lastname: Teloken
authors.forename: Ajay K
authors.initials: AK
authors.lastname: Nangia
authors.forename: James A
authors.initials: JA
authors.lastname: Daitch
authors.forename: Craig
authors.initials: C
authors.lastname: Niederberger
authors.forename: Anthony J
authors.initials: AJ
authors.lastname: Thomas
mesh_terms: D000328:Adult; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011446:Prospective Studies; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014669:Vasovasostomy / Q000706:statistics & numerical data*