A Model for Predicting Polycystic Ovary Syndrome Using Serum AMH, Menstrual Cycle Length, Body Mass Index and Serum Androstenedione in Chinese Reproductive Aged Population: A Retrospective Cohort Study.

Background: A clinical diagnosis of polycystic ovary syndrome (PCOS) can be tedious with many different required tests and examinations. Furthermore, women with PCOS have increased risks for several metabolic complications, which need long-term health management. Therefore, we attempted to establish an easily applicable model to identify such women at an early stage. Objective: To develop an easy-to-use tool for screening PCOS based on medical records from a large assisted reproductive technology (ART) center in China. Materials and Methods: A retrospective observational cohort from Peking University Third Hospital was used in the study. Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression with 10-fold cross-validation was applied to construct the model. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity values were used to evaluate and compare the models. Design Setting and Participants: This retrospective cohort study included 21,219 ovarian stimulation cycle records from January to December 2019 in Peking University Third Hospital. Main Outcomes and Measures: The main outcome was whether there was a clinical diagnosis of PCOS. The independent variables included were age, body mass index (BMI), upper limit of menstrual cycle length (UML), basal serum levels of anti-Müllerian hormone (AMH), testosterone androstenedione, antral follicle counts et al. Results: We have established a new mathematical model for diagnosing PCOS using serum AMH and androstenedione levels, UML, and BMI, with AUC values of 0.855 (0.838-0.870), 0.848 (0.791-0.891), 0.846 (0.812-0.875) in the training, validation, and testing sets, respectively. The contribution of each predictor to this model were: AMH 41.2%; UML 35.2%; BMI 4.3%; and androstenedione 3.7%. The top 10 groups of women most predicted to develop PCOS were demonstrated. An online tool (http://121.43.113.123:8888/) has been developed to assist Chinese ART clinics. Conclusions: The models and online tool we established here might be helpful for screening and identifying women with undiagnosed PCOS in Asian populations and could assist in the long-term management of related metabolic disorders.