A logistic model for the prediction of endometriosis

Barbara J. Stegmann, Michele Jonsson Funk, Ninet Sinaii, Katherine E. Hartmann, James Segars, Lynnette K. Nieman, Pamela Stratton

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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. Design: Secondary analysis of prospectively collected information. Setting: Government research hospital in the United States. Patient(s): Healthy women 18-45 years of age, with chronic pelvic pain and possible endometriosis, who were enrolled in a clinical trial. Intervention(s): All participants underwent laparoscopy, and information was collected on all visible lesions. Lesion data were randomly allocated to a training and test data set. Main Outcome Measure(s): Predictive logistic regression, with the outcome of interest being histologic diagnosis of endometriosis. Result(s): 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. Conclusion(s): 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.

Original languageEnglish
Pages (from-to)51-55
Number of pages5
JournalFertility and Sterility
Issue number1
StatePublished - Jan 2009


  • Endometriosis
  • logistic regression modeling
  • prediction

ASJC Scopus subject areas

  • Reproductive Medicine
  • Obstetrics and Gynecology


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