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 women18-45 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.
RCT Entities:
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.
Authors: Jacques Donnez; Jean Squifflet; Françoise Casanas-Roux; Céline Pirard; Pascale Jadoul; Anne Van Langendonckt Journal: Obstet Gynecol Clin North Am Date: 2003-03 Impact factor: 2.844
Authors: Stacy L McAllister; Barbra K Giourgas; Elizabeth K Faircloth; Emma Leishman; Heather B Bradshaw; Eric R Gross Journal: Mol Cell Endocrinol Date: 2016-08-11 Impact factor: 4.102
Authors: Charles Chapron; Marie-Christine Lafay-Pillet; Pietro Santulli; Mathilde Bourdon; Chloé Maignien; Antoine Gaudet-Chardonnet; Lorraine Maitrot-Mantelet; Bruno Borghese; Louis Marcellin Journal: EClinicalMedicine Date: 2022-01-10