Analysis of related factors and prediction of poor ovarian response in patients with controlled ovarian stimulation
10.3760/cma.j.cn112141-20210930-00561
- VernacularTitle:控制性促排卵患者发生卵巢低反应的影响因素分析及其发生风险预测
- Author:
Xue WANG
1
;
Yingying FAN
;
Lei LI
;
Shaodi ZHANG
;
Cuilian ZHANG
Author Information
1. 河南省人民医院生殖医学研究所，郑州 450003
- Keywords:
Infeitility, female;
Fertilization in vitro;
Ovarian reserve;
Ovulation induction;
Nomograms;
Forecasting;
Root cause analysis;
Poor ovarian response
- From:
Chinese Journal of Obstetrics and Gynecology
2022;57(2):110-116
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the related factors of poor ovarian response (POR) in patients receiving controlled ovarian stimulation (COS) and to establish the nomogram for predicting POR in patients who received in vitro fertilization or intracytoplasmic sperm injection (IVF/ICSI).Methods:In this retrospective research, clinical data of 17 164 cycles of patients who received IVF/ICSI treatment at Henan Provincial People′s Hospital from September 1st, 2016 to September 1st, 2020 were analyzed. Independent correlative factors affecting the occurrence of POR were screened by logistic regression, which were the model enrollment variables in the prediction model. Totally 13 266 cycles with well-record of enrollment variables were screened, and these data were randomly divided into model group (9 896 patients) and validation group (3 370 patients) according to 3∶1. The nomogram was established according to the regression coefficient of the relevant variables. The prediction accuracy of the nomogram was evaluated by calculating area under the receiver operating characteristic curve (AUC).Results:Multivariate logistic regression analysis showed age, infertility type, body mass index, anti-Müllerian hormone, basal follicle stimulating hormone, basal estrogen, antral follicle number, previous times of POR, history of ovarian surgery, ovulation stimulation protocol and average amount of gonadotropin were independent correlative factors affecting the occurrence of POR (all P<0.05). In the model group, according to the above factors, the prediction model and nomogram of POR risk were constructed and the validation group verified the model. The AUC of the model group was 0.893 (95% CI: 0.885-0.900), and the AUC of the validation group was 0.890 (95% CI: 0.878-0.903). Conclusion:The influencing factors of POR after COS in patients treated by IVF/ICSI are screened, and the nomogram for predicting POR established in this study is proved to be effective, simple, intuitive and clear in predicting the occurrence of POR.
