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BackgroundSubchorionic hematoma (SCH) is common in early pregnancy achieved by in vitro fertilization-embryo transfer/frozen embryo transfer (IVF-ET/FET), and is associated with adverse obstetric outcomes. However, there are no methods known to accurately predict the occurrence of SCH.ObjectiveTo establish a nomogram prediction model for predicting the risk of SCH in early pregnancy with IVF-ET/FET and to analyze pregnancy outcomes of patients with SCH.MethodsPatients who underwent IVF-ET/FET treatment and were diagnosed with clinical pregnancy were enrolled in our study. A total of 256 patients with SCH were enrolled in the SCH group, and 526 patients without SCH in the control group. Logistic regression was used to screen risk factors for SCH, and the nomogram was developed according to the regression coefficient of relevant variables. Discrimination, effect, calibration, and the predictive model’s clinical usefulness were assessed using the C-index, the area under the receiver operating characteristic standard curve, calibration plot, and decision curve analysis. Internal validation was assessed using bootstrapping validation. The effects of SCH on pregnancy outcomes were analyzed.ResultsA multivariate logistic regression analysis showed that fresh embryo transfer, polycystic ovary syndrome, hydrosalpinx, and thin endometrium were risk factors affecting the occurrence of SCH. Based on the above factors, a predictive model for the risk of SCH was created. The model displayed good discrimination, with a C-index of 0.783 (95% confidence interval: 0.750–0.816), area under the receiver operating characteristic standard curve of 0.783, and good calibration. A high C-index value of 0.765 could still be reached in the interval validation. Decision curve analysis showed that the nomogram was clinically useful when the intervention was decided at the SCH possibility threshold of 4%–87%. For patients with successful deliveries, the occurrence of SCH did not influence the gestational weeks of delivery, mode of delivery, preterm birth, height, and weight of the newborn.ConclusionWe screened the risk factors for SCH in patients who underwent IVF-ET/FET treatment. Successful establishment of a nomogram can effectively predict the occurrence of SCH. Furthermore, the incidence of miscarriage is higher in patients with SCH.

ObjectiveTo 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.DesignSingle-center, retrospective study.SettingUniversity-affiliated reproductive medicine center.Patient(s)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.Intervention(s)None.Main outcome measure(s)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.Result(s)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.Conclusion(s)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.