Establishment of a predictive model and analysis of risk factors for live birth outcomes in PCOS patients undergoing IVF/ICSI-ET
10.3760/cma.j.cn101441-20250403-00168
- VernacularTitle:多囊卵巢综合征患者IVF/ICSI-ET活产结局的危险因素分析与预测模型建立
- Author:
Sihan WANG
1
;
Yuexin YU
1
;
Xijing ZHANG
1
;
Xue BAI
1
Author Information
1. 北部战区总医院生殖医学科，沈阳 110016
- Publication Type:Journal Article
- Keywords:
Polycystic ovary syndrome;
In vitro fertilization;
Embryo transfer;
Risk factors;
Live birth outcome
- From:
Chinese Journal of Reproduction and Contraception
2025;45(9):917-923
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the risk factors affecting live birth outcomes in patients with polycystic ovary syndrome (PCOS) undergoing in vitro fertilization/intracytoplasmic sperm injection and embryo transfer (IVF/ICSI-ET) and to establish a predictive model. Methods:A retrospective case-control study was conducted by collecting data from 563 patients with PCOS who were treated at the Reproductive Medicine Department of the General Hospital of Northern Theater Command between June 2018 and January 2023. Patients were divided into live birth ( n=341) and non-live birth ( n=222) groups based on pregnancy outcomes. Univariate and multivariate logistic regression analyses were performed to identify risk factors, followed by construction of a nomogram prediction model based on values with P<0.05 in multiple regression analysis. The model's predictive performance was evaluated using receiver operating characteristic curve analysis, calibration curves, Hosmer-Lemeshow (H-L) goodness-of-fit test, and decision curve analysis. Results:1) Univariate analysis revealed that there were statistically significant differences in age, body mass index (BMI), insulin level, the number of high-quality embryos, and the rate of high-quality embryos between the two groups (all P<0.05). 2) After adjusting for confounding factors, the results of multivariate logistic regression analysis on variables associated with live birth outcomes in the live birth group showed that: age ( OR=1.151, 95% CI: 1.061-1.249, P=0.001), body mass index ( OR=1.141, 95% CI: 1.074-1.214, P<0.001), and insulin level ( OR=1.206, 95% CI: 1.149-1.266, P<0.001) were independent risk factors for live birth outcome; top-quality embryo rate ( OR=0.101, 95% CI: 0.033-0.310, P<0.001) was a protective factor; and the number of top-quality embryos ( OR=0.949, 95% CI: 0.887-1.014, P=0.104) showed no statistically significant association with live birth outcome. 3) A predictive model for the live-birth outcome after IVF/ICSI-ET in PCOS patients was established. The area under the curve (AUC) values for predicting the live-birth outcome based on female age, BMI, the rate of high-quality embryos, and insulin level were 0.581, 0.747, 0.725, and 0.813, respectively. The combined model of these four factors had an AUC value of 0.846 for predicting the live-birth outcome. 4) A nomogram predictive model for the live-birth outcome after IVF/ICSI-ET in PCOS patients was established. The slope of the model's calibration curve was close to 1, and the H-L test yielded a P>0.05, indicating a high consistency between predicted and actual events. The decision analysis curve confirmed the clinical practicality of the predictive model. Conclusion:Age, BMI and insulin level are independent risk factors for live birth outcomes in PCOS patients undergoing IVF/ICSI-ET, while the high-quality embryo rate serves as a protective factor. The established predictive model demonstrates excellent performance and may facilitate clinical decision-making.
