Developing symptom-based predictive models of endometriosis as a clinical screening tool: results from a multicenter study

Symptom-based models predict any-stage endometriosis poorly and stage III and IV disease accurately. A tool based on such models can help prioritise women for surgical investigation.

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Authors

Kelechi E. Nnoaham, M.D., Lone Hummelshoj, Stephen H. Kennedy, M.R.C.O.G., Crispin Jenkinson, D.Phil., Krina T. Zondervan, D.Phil., on behalf of the World Endometriosis Research Foundation Women's Health Symptom Survey Consortium

Vol 98, Issue 3, Pages 692-701.e5

Abstract

Objective:

To generate and validate symptom-based models to predict endometriosis among symptomatic women prior to undergoing their first laparoscopy

Design:

Prospective, observational, two-phase study, in which women completed a 25-item questionnaire prior to surgery.

Setting:

Nineteen hospitals in thirteen countries

Patients:

1,396 symptomatic women scheduled for laparoscopy without a previous surgical diagnosis of endometriosis.

Interventions:

None

Main outcome measures:

Sensitivity and specificity of endometriosis diagnosis predicted by symptoms and patient characteristics from optimal models developed using multiple logistic regression analyses in one dataset (phase I), and independently validated in a second dataset (phase II) by receiver operating characteristic (ROC) curve analysis.

Results:

360 (46.7%) women in phase I and 364 (58.2%) in phase II were diagnosed with endometriosis at laparoscopy. Menstrual dyschezia (pain on opening bowels) and a history of benign ovarian cysts most strongly predicted both any and stage III/IV endometriosis in both phases (all p

Conclusion:

Our symptom-based models predict any-stage endometriosis relatively poorly and stage III/IV disease with good accuracy. Predictive tools based on such models could help to prioritise women for surgical investigation in clinical practice and thus contribute to reducing time to diagnosis. We invite other researchers to validate the key models in additional populations.

Read the full text at: http://www.fertstert.org/article/S0015-0282(12)00454-2/fulltext


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