Development of a Symptom-Based, Screening Tool for Early-Stage Endometriosis in Patients with Chronic Pelvic Pain

Patrick Yeung, Caroline Bazinet, Jeffrey A. Gavard
In: Journal of Endometriosis and Pelvic Pain Disorders · 2014 · vol. 6(4) , pp. 174–189 · doi:10.5301/je.5000200 · W2055855341
article OA: closed CC0 ⤵ 8 in-corpus citations
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A symptom-based screening tool combining patient questionnaire variables was developed with high discriminatory ability to predict early-stage endometriosis in women with chronic pelvic pain.

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Abstract

Purpose The aim of this study was to develop a noninvasive screening tool for patients with chronic pelvic pain, for early-stage endometriosis, with a discriminatory ability of the area under the curve (AUC) for the receiver operator characteristic (ROC) curve of over 0.8. Methods Participants were women aged 13-55 years, with chronic pelvic pain (for more than 6 months despite medical therapy), seen at the Saint Louis University Center for Endometriosis, from January 2012 to June 2013, who were found to have early-stage endometriosis or not, based on histology. All patients received the same surgical approach and goal of surgery, which was excision of all visible abnormal peritoneum. Potential variables used for generating the screening tool came from the pre-operative questionnaire with standard pain symptoms and quality-of-life questions. Individual categorical variables were tested. In addition, new binary variables were created to test specific combinations of answers to 2 individual questions. A backwards-stepping multiple logistic regression model was used to develop the screening tool. Results Ninety patients found to have early-stage disease completed pre-operative surveys. Of these, 70 (77.8%) had histologically confirmed endometriosis and 20 (22.2%) were confirmed not to be affected. Combinations of 2 variables made it possible to create a predictive model for early-stage endometriosis with excellent discriminatory ability (AUC = 0.822, p<0.001). This model was generated using a multi-staged, backwards stepping logistic regression analysis with all variables that were univariately significant included in the initial model (p<0.025). The final model had 80.5% sensitivity and 57.7% specificity. The model allows for an individual probability of disease to be calculated for each patient. Conclusions A clinically useful, symptom-based screening tool was developed for early-stage endometriosis in patients with chronic pelvic pain seen at a referral center. This approach to developing a screening tool for endometriosis should be tested further.

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endometriosischronic_pelvic_pain

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