Nomogram Model to Predict the Probability of Ovarian Hyperstimulation Syndrome in the Treatment of Patients With Polycystic Ovary Syndrome.

Objective: The objective of this study was to explore the risk factors of ovarian hyperstimulation syndrome (OHSS) in patients with polycystic ovary syndrome (PCOS) undergoing Methods: We retrospectively analyzed clinical data from 4,351 patients with PCOS receiving IVF/ICSI in our reproductive medical center. The clinical cases were randomly divided into a modeling group (3,231 cases) and a verification group (1,120 cases) according to a ratio of about 3:1. The independent risk factors correlation with the occurrence of OHSS was identified by logistic regression analysis. Based on the selected independent risk factors and correlated regression coefficients, we established a nomogram model to predict the probability of OHSS in PCOS patients, and the predictive accuracy of the model was measured using the area under the receiver operating curve (AUC). Results: Univariate and multivariate logistic regression analyses showed that FSH (OR, 0.901; 95% CI, 0.847-0.958; P<0.001), AMH (OR, 1.259; 95% CI, 1.206-1.315; P<0.001), E2 value on the day of hCG injection (OR, 1.122; 95% CI, 1.021-1.253; P<0.001), total dosage of Gn used (OR, 1.010; 95% CI, 1.002-1.016; P=0.041), and follicle number on the day of hCG injection (OR, 0.134; 95% CI, 1.020-1.261; P=0.020) are the independent risk factors for OHSS in PCOS patients. The AUC of the modeling group is 0.827 (95% CI, 0.795-0.859), and the AUC of the verification group is 0.757 (95% CI, 0.733-0.782). Conclusion: The newly established nomogram model has proven to be a novel tool that can effectively, easily, and intuitively predict the probability of OHSS in the patients with PCOS, by which the clinician can set up a better clinical management strategies for conducting a precise personal therapy.