comparison of three clinical models for estimating the risk of endometrial cancer in post-menopausal women with abnormal vaginal bleeding

Background: given the prevalence of abnormal post-menopausal bleeding and the importance of its early examination for ensuring the timely diagnosis of any malignancies, the present study was conducted to investigate uterine pathologies in relation to post-menopausal bleeding patterns and endometrial thickness as observed in transvaginal ultrasounds and to offer models for predicting endometrial cancer. methods: the present descriptive-analytical cross sectional study was conducted on 112 post-menopausal women presenting to ayatollah rouhani hospital in babol, iran. the patients underwent a transvaginal ultrasound and hysteroscopy and their samples were sent for pathological examination. the logistic regression model and the receiver operating characteristic (roc) curve were used. this study presents 3 models for predicting endometrial cancer, including am30 [which considers the subject’s amount of bleeding, menopause age and bmi (body mass index) for assessing her risk of endometrial cancer], amd30 (which additionally considers the subject’s history of diabetes) and amdi30 (which additionally considers the subject’s history of internal diseases). results: menopause age, amount of bleeding, bmi, and history of internal diseases were significantly linked to endometrial cancer in post-menopausal women with abnormal bleeding; that is, the variables were higher in this group than in those without cancer (p = 0.007, p = 0.004, p = 0.001, and p = 0.02). the three models defined, i.e. am30, amd30, and ami30 had a high area under the roc curve and could predict endometrial cancer with a proper sensitivity and specificity in post-menopausal women with vaginal bleeding. there were no statistically significant differences among these models, although the ami30 model had a higher area under the roc curve compared to the other two models (p = 0.29). conclusions: the present study recommends these three predictive models as alternatives for predicting endometrial cancer in post-menopausal women with vaginal bleeding.