title: A logistic model for the prediction of endometriosis.
abstract: OBJECTIVE
  To develop a model that uses individual and lesion characteristics to help surgeons choose lesions that have a high probability of containing histologically confirmed endometriosis.
  
  
  METHODS
  Secondary analysis of prospectively collected information.
  
  
  METHODS
  Government research hospital in the United States.
  
  
  METHODS
  Healthy women 18-45 years of age, with chronic pelvic pain and possible endometriosis, who were enrolled in a clinical trial.
  
  
  METHODS
  All participants underwent laparoscopy, and information was collected on all visible lesions. Lesion data were randomly allocated to a training and test data set.
  
  
  METHODS
  Predictive logistic regression, with the outcome of interest being histologic diagnosis of endometriosis.
  
  
  RESULTS
  After validation, the model was applied to the complete data set, with a sensitivity of 88.4% and specificity of 24.6%. The positive predictive value was 69.2%, and the negative predictive value was 53.3%, equating to correct classification of a lesion of 66.5%. Mixed color; larger width; and location in the ovarian fossa, colon, or appendix were most strongly associated with the presence of endometriosis.
  
  
  CONCLUSIONS
  This model identified characteristics that indicate high and low probabilities of biopsy-proven endometriosis. It is useful as a guide in choosing appropriate lesions for biopsy, but the improvement using the model is not great enough to replace histologic confirmation of endometriosis.
authors.affiliation: Reproductive Biology and Medicine Branch, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland 20892-1109, USA.
authors.forename: Barbara J
authors.initials: BJ
authors.lastname: Stegmann
authors.forename: Michele Jonsson
authors.initials: MJ
authors.lastname: Funk
authors.forename: Ninet
authors.initials: N
authors.lastname: Sinaii
authors.forename: Katherine E
authors.initials: KE
authors.lastname: Hartmann
authors.forename: James
authors.initials: J
authors.lastname: Segars
authors.forename: Lynnette K
authors.initials: LK
authors.lastname: Nieman
authors.forename: Pamela
authors.initials: P
authors.lastname: Stratton
mesh_terms: D000293:Adolescent; D000328:Adult; D001706:Biopsy; D003951:Diagnostic Errors / Q000706:statistics & numerical data; D004715:Endometriosis / Q000175:diagnosis* / Q000473:pathology; D005260:Female; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D011237:Predictive Value of Tests; D011336:Probability; D012680:Sensitivity and Specificity; D011795:Surveys and Questionnaires; D055815:Young Adult