Nomogram to predict the probability of clinical pregnancy in women with poor ovarian response undergoing in vitro fertilization/ intracytoplasmic sperm injection cycles

Poor ovarian response (POR) is associated with decreased clinical pregnancy rates, emphasizing the need for developing clinical prediction models. Such models can improve prognostic accuracy, personalize medical interventions, and ultimately enhance live birth rates among patients with POR.

This study aims to develop and validate a prognostic model for predicting clinical pregnancy outcomes in individuals with POR undergoing in vitro fertilization/ intracytoplasmic sperm injection (IVF/ICSI) cycles.

A retrospective cohort of 969 patients with POR undergoing fresh embryo transfer cycles at the Reproductive Center of Fujian Maternal and Child Health Center from January 2018 to January 2022 was included. The cohort was randomly divided into model (n = 678) and validation (n = 291) groups in a 7:3 ratio. A single-factor analysis was performed on the model group to identify variables influencing clinical pregnancy. Optimal variables were selected using LASSO regression, and a clinical prediction model was constructed using multivariate logistic regression analysis. The model's calibration and discrimination were assessed using receiver operating characteristic (ROC) and calibration curves, while the clinical utility was evaluated using decision curve analysis.

Multivariate logistic regression analysis revealed that the age of the women (odds ratio [OR] 0.936, 95% confidence interval [CI] 0.898–0.976, P = 0.002), body mass index (BMI) ≤ 24 (OR 2.748, 95% CI 1.724–4.492, P < 0.001), antral follicle count (AFC) (OR 1.232, 95% CI 1.073–1.416, P = 0.003), anti-Müllerian hormone (AMH) (OR 1.67, 95% CI 1.178–2.376, P = 0.004), number of mature oocytes (OR 1.227, 95% CI 1.075–1.403, P = 0.003), number of embryos transferred (OR 1.692, 95% CI 1.132–2.545, P = 0.011), and transfer of high-quality embryos (OR 3.452, 95% CI 1.548–8.842, P = 0.005) were independent predictors of clinical pregnancy in patients with POR. According to the receiver operating characteristic (ROC) analysis, the prediction model exhibited an area under the curve (AUC) of 0.752 (0.714, 0.789) in the model group and 0.765 (0.708, 0.821) in the validation group. The clinical decision curve demonstrated that the model held maximum clinical utility in both cohorts when the threshold probability of clinical pregnancy ranged from 6–81% to 12–82%, respectively.

Clinical pregnancy outcomes in patients with POR who underwent IVF/ICSI treatment were influenced by several independent factors, including the age of the women, BMI, AFC, AMH, number of mature oocytes, number of embryos transferred, and transfer of high-quality embryos. A clinical prediction model based on these factors exhibited favorable clinical predictive and applicative value. Therefore, this model can serve as a valuable tool for clinical prognosis, intervention, and facilitating personalized medical treatment.