The Influencing Factors and Predictive Algorithm of Pregnancy Outcomes in IVF/ICSI-ET Patients.

Purpose: To explore the influencing factors of clinical pregnancy outcomes for in vitro fertilization/intracytoplasmic single sperm injection and embryo transfer (IVF/ICSI-ET) patients, and to establish a predictive algorithm to predict the rate of clinical pregnancy. Patients and Methods: A single-center retrospective analysis was performed on 1183 treatment cycles of patients undergoing IVF/ICSI-ET at Hangzhou Women's Hospital, covering the period from April 2018 to March 2023. All cases were categorized into clinical pregnancy and non-pregnant groups. Totally 24 clinical and laboratory indicators were analyzed by logistic regression model to analyze the factors affecting clinical pregnancy outcome in IVF/ICSI-ET treated couples. Furthermore, by stratifying the influencing factors and quantitatively assigning scores, a predictive algorithm was established to predict the clinical pregnancy outcomes by calculating the total score. Results: The results of multivariate logistic regression analysis showed that the male age (OR=0.965, 95% CI: 0.949~0.980) and progesterone (P) level on hCG day (OR=0.687, 95% CI: 0.500~0.944) were negatively correlated with clinical pregnancy in IVF/ICSI-ET couples, and that AMH (OR=1.085, 95% CI: 1.022~1.151), the number of high-quality embryos (OR=1.094, 95% CI: 1.039~1.152), and the number of transferred embryos (OR=2.218, 95% CI: 1.684~2.922) were positively associated with clinical pregnancy. Our multivariate logistic regression model reached a sensitivity of 64.55%, a specificity of 58.42%, and an AUC of 0.644 (95% CI: 0.614-0.673). A simple predictive algorithm of clinical pregnancy outcome was then developed using the five variables, both internal and external validations have been taken. The total score of the algorithm is between 0 and 23, and couples with total score of 10 or higher are highly likely to achieve clinical pregnancy. Conclusion: Factors affecting clinical pregnancy in infertile couples mainly included male age, AMH, P level on hCG day, number of high-quality embryos, and number of embryos transferred. Clinicians can use predictive algorithms to predict clinical pregnancy outcomes more simpler and convenient, and develop personalized embryo transfer strategies more precisely.