OC12.07: *Development of a clinical prediction model for diagnosing adenomyosis

To develop a multivariate prediction model for diagnosing adenomyosis using predictors available through transvaginal ultrasonography (TVUS) and clinical examinations. Prospective observational single-centre study at a Norwegian teaching university hospital. One hundred premenopausal women aged 30–50 years; undergoing hysterectomy due to a benign condition and not using hormonal treatment were consecutively enrolled. Preoperative 2D and 3D TVUS investigations were performed and the results were documented in a standardised form. Clinical information was collected using a questionnaire. The diagnostic performance (sensitivity, specificity, area under the curve) of a multivariate prediction model for adenomyosis was evaluated. The independent diagnostic performance of single predictors and their quantitative effect (β) in the final model was assessed. The final model showed a good test quality [AUC=0.86 (95% CI=0.79–0.94), optimal cut-off 0.56, sensitivity of 85%, specificity of 78%]. The following nine predictors were included [(sensitivity, specificity, β) or (AUC, β)]: presence of myometrial cysts (51%, 86%, β=0.86), fan-shaped echo (36%, 92%, β=0.54), hyperechoic islets (51%, 78%, β=0.62), globular uterus (61%, 83%, β=0.2), normal uterine shape (83%, 61%, β=–0.75), thickest/thinnest ratio for uterine wall (0.61, β=0.26), maximum width of the junctional zone (JZ) in sagittal plane (0.71, β=0.1), regular appearance of JZ (31%, 92%, β=–1.0), and grade of dysmenorrhea measured on a verbal numerical rating scale (0.61, β=0.08). We present the development of a multivariate model for diagnosing adenomyosis that weights the predictors based on their diagnostic significance. The reported findings could aid clinicians who are interpreting the heterogeneous appearance of adenomyosis in ultrasonography.