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. 2009 Jan;91(1):51-5.
doi: 10.1016/j.fertnstert.2007.11.038. Epub 2008 May 7.

A logistic model for the prediction of endometriosis

Barbara J Stegmann  1 , Michele Jonsson Funk, Ninet Sinaii, Katherine E Hartmann, James Segars, Lynnette K Nieman, Pamela Stratton
Affiliations

A logistic model for the prediction of endometriosis

Barbara J Stegmann et al. Fertil Steril. 2009 Jan.

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.

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.

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Figures

Figure 1
Figure 1
ROC curve. Model correctly predicts endometriosis in 66.7% of the time. Area under ROC curve=0.7026
See this image and copyright information in PMC

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