Development and validation of a live birth prediction model for expected poor ovarian response patients during IVF/ICSI - Gong, Xiaoyun; Zhang, Yunian; Zhu, Yuejie; Wang, Peng; Wang, Zhihui; Liu, Chen; Zhang, Manli; La, Xiaolin (Frontiers in Endocrinology, 2023)
Background:A number of live birth predictive model during assisted reproductive technology (ART) treatment have been available in recent years, but few targeted evaluating the chances of live birth in poor ovarian response(POR) patients.The aim of this study was to develop a nomogram based on POSEIDON criteria to predict live birth in patients with expected POR. Methods:This retrospective cohort study using clinical data from 657 patients in POSEIDON Groups 3 and 4 (antral follicle count [AFC] ≤5 and AMH <1.2 ng/ml) in the Center for Reproductive Medicine, First Affiliated Hospital of Xinjiang Medical University, and Construction a nomogram model t Results:Among 657 expected POR patients, 111 (16.89%) had live births, and 546 (83.11%) did not have live births.These were divided into a training set(n=438) and a validation set (n=219). Multivariate logistic regression analysis showed that the age (OR = 0.91, 95% CI: 0.86–0.97), BMI (OR = 1.98, 95% CI: 1.09–3.67), AMH (OR = 3.48, 95% CI: 1.45–8.51), normal fertilized oocytes (OR = 1.40, 95% CI: 1.21–1.63), and the basal FSH (OR = 0.89, 95% CI: 0.80–0.98) of the female were independent factors predicting live birth in patients with expected POR. Then, an individualized nomogram prediction model was built from these five factors. The AUC value of the live birth prediction model was 0.820 in the training set and 0.879 in the validation set. Conclusion:We have developed a nomogram combining clinical and laboratory factors to predict the probability of live birth in patients with an expected POR during IVF/ICSI, which can provide some valuable reference for treats POR patients in clinical work. However, the data comes from the same center, needs a prospective multicenter study for further in-depth evaluation and validation of this prediction model.