Abstract
OBJECTIVE: There is limited study on predictive models for live births in patients with polycystic ovarian syndrome (PCOS). The study aimed to develop and validate a nomogram for predicting live births in Chinese women with PCOS, as well as to identify the risk factors affecting live births in this population. METHODS: Data for this study were obtained from a large clinical trial known as PCOSAct in mainland China. A total of 927 patients with PCOS were investigated using stratified random sampling. The mean-filling method was used to address missing data, and Lasso-Logistic regression was combined with machine learning models to identify the most significant predictors of live births. A nomogram was constructed based on a multivariate logistic regression model and was evaluated using receiver operating characteristic curves (ROC), calibration curves and clinical decision curves to assess model discrimination, calibration and clinical validity. RESULTS: In the training set, estradiol, potassium ion, total cholesterol, alanine aminotransferase, and free androgen index were identified as key risk factors influencing live birth rates in PCOS patients. In this study, we demonstrated that elevated levels of estradiol and potassium ions, coupled with reduced levels of total cholesterol, alanine aminotransferase, and free androgen index (FAI), significantly improved insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS). These biochemical alterations facilitated weight reduction and normalized endocrine function, thereby enhancing the live birth rate among PCOS patients. The nomogram developed from this multivariate model underwent validation by both internal (AUC:0.649; 95% CI [0.605~0.694]) and external (AUC:0.709; 95% CI [0.616~0.801]) assessments. The results have strong predictive accuracy, reliability, and generalizability for live birth. CONCLUSION: The nomogram developed in this study is a valid tool for assessing live births in patients with PCOS. This tool will assist clinicians in assessing the risks associated with live births in patients with PCOS and enable them to implement more effective preventive measures.