A logistic model for the prediction of endometriosis.
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.
Stegmann
Barbara J
BJ
Reproductive Biology and Medicine Branch, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland 20892-1109, USA.
Funk
Michele Jonsson
MJ
Sinaii
Ninet
N
Hartmann
Katherine E
KE
Segars
James
J
Nieman
Lynnette K
LK
Stratton
Pamela
P
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