Effect and Relationship of Seasons on the High Risk of Ovarian Hyperstimulation Syndrome After Oocyte Retrieval in Patients With Polycystic Ovary Syndrome

Objective To investigate the effect of seasons on the incidence of high risk of ovarian hyperstimulation syndrome (OHSS) after in oocyte retrieval in patients with polycystic ovarian syndrome (PCOS) and to establish a nomogram to predict the risk of OHSS. Design Single-center, retrospective study. Setting University-affiliated reproductive medicine center. Patients A total of 2,030 infertility patients with PCOS underwent the follicular phase long-acting long protocol IVF/ICSI in the reproductive medicine center from January 2017 to December 2019. Interventions None. Main outcome measures Logistic regression analysis was used to analyze the factors associated with a high risk of OHSS. We established a nomogram to predict the risk of OHSS in infertility patients with PCOS after oocyte retrieval. Results The incidence of patients at high risk of OHSS was significantly different from season-to-season and was especially higher in the summer and winter. Multivariate logistic analysis showed that gonadotropin dosage, number of retrieved oocytes, estradiol level, average bilateral ovarian diameter on the day human chorionic gonadotropin was administered, type of infertility, and average temperature were independent risk factors for OHSS after oocyte retrieval in PCOS patients. Based on the above independent risk factors, we constructed a prediction model for OHSS risk. To evaluate the efficiency of the prediction model, we calculated the C-index (0.849), area under the receiver operating characteristic curve (0.849), and internal validation C-index (0.846). Decision curve analysis suggested that the prediction model exhibited significant net benefits. Conclusions The incidence of PCOS patients at high risk for OHSS after oocyte retrieval fluctuated with seasonal temperature changes, and was significantly higher in extreme climates. The prediction model had favorable predictive performance and clinical application value.