Nomogram to predict cumulative live birth rate following in vitro fertilization/intracytoplasmic sperm injection cycles in patients with endometriosis - Linling Zhu; Tongtong Lin; Xinyun Yang; Dan Zhang (BMC Pregnancy and Childbirth)
Abstract Background The success of in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) in endometriosis patients varies widely, and predicting the likelihood of achieving a live birth remains a clinical challenge. This study aims to develop a predictive nomogram for assessing the cumulative live birth (CLB) rate following IVF/ICSI cycles among patients with endometriosis. Method A retrospective cohort study was conducted to analyze the clinical data of 1457 patients with endometriosis after IVF/ICSI treatment from January 2017 to August 2022. The patients were divided into a training set (70%) and a validation set (30%) using a random number table. Univariate analysis and multifactorial logistic regression analysis were employed to identify relevant predictive factors affecting CLB rate. A predictive model was then established based on the identified factors. Results Univariate analysis and multifactorial logistic regression analysis revealed that patients with concurrent adenomyosis had a decreased CLB rate (OR = 0.51, 95% CI: 0.31—0.82). As the duration of infertility increased, the CLB rate decreased (OR = 0.93, 95% CI: 0.88—0.99). Higher numbers of fertilized oocytes and high-quality embryos were associated with an increased likelihood of CLB. A nomogram predictive model for CLB rate, based on age, concurrent adenomyosis, duration of infertility, number of fertilization, and number of high-quality embryos, was developed. The area under the curve (AUC) for the training set and validation set was 0.823 (95% CI: 0.798–0.849) and 0.773 (95% CI: 0.729–0.818), respectively. The stratified analysis demonstrated the applicability of the model in the validation cohort. Conclusion This predictive nomogram for CLB rate in patients with endometriosis provides valuable and precise information for personalized decision-making, which could be a visual and easily applied tool for IVF/ICSI counselling.