Nomogram to predict pregnancy outcomes of emergency oocyte freeze-thaw cycles.

Background Existing clinical prediction models for in vitro fertilization are based on the fresh oocyte cycle, and there is no prediction model to evaluate the probability of successful thawing of cryopreserved mature oocytes. This research aims to identify and study the characteristics of pre-oocyte-retrieval patients that can affect the pregnancy outcomes of emergency oocyte freeze-thaw cycles. Methods Data were collected from the Reproductive Center, Peking University Third Hospital of China. Multivariable logistic regression model was used to derive the nomogram. Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plots. Results The predictors in the model of "no transferable embryo cycles" are female age (odds ratio [OR] = 1.099, 95% confidence interval [CI] = 1.003-1.205, P = 0.0440), duration of infertility (OR = 1.140, 95% CI = 1.018-1.276, P = 0.0240), basal follicle-stimulating hormone (FSH) level (OR = 1.205, 95% CI = 1.051-1.382, P = 0.0084), basal estradiol (E2) level (OR = 1.006, 95% CI = 1.001-1.010, P = 0.0120), and sperm from microdissection testicular sperm extraction (MESA) (OR = 7.741, 95% CI = 2.905-20.632, P Conclusions The predictors in the final multivariate logistic regression models found to be significantly associated with poor pregnancy outcomes were increasing female age, duration of infertility, high basal FSH and E2 level, endometriosis, sperm from MESA, and low number of follicles with a diameter >10 mm on the day of hCG administration.