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. 2023 Jun;23(2):632-639.
doi: 10.4314/ahs.v23i2.73.

Factors influencing clinical pregnancy outcome of in vitro fertilization/intracytoplasmic sperm injection in older women

Jinyu Gao  1 , Zhaohua Liu  1 , Yao Zhong  1 , Nannan Li  1 , Tingting Tang  1
Affiliations

Factors influencing clinical pregnancy outcome of in vitro fertilization/intracytoplasmic sperm injection in older women

Jinyu Gao et al. Afr Health Sci. 2023 Jun.

Abstract

Objective: To analyse the factors influencing clinical pregnancy outcome of in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) in older women, and to establish a risk prediction model.

Methods: A total of 425 patients receiving IVF/ICSI from March 2018 to March 2020 were divided into pregnancy group (n=194) and non-pregnancy group (n=231). The factors affecting the outcomes of IVF/ICSI were explored by univariate and multivariate logistic regression analyses. A nomogram prediction model was constructed.

Results: The two groups had significantly different age, body mass index, dysmenorrhea, parity, times of full-term births, history of cesarean section, basal follicle stimulating hormone, basal antral follicle count (AFC), number of high-quality embryos, and basal estradiol, luteinizing hormone and endometrial thickness on the day of human chorionic gonadotropin (HCG) administration (P<0.05). Age ≥40 years old, dysmenorrhea, history of cesarean section, basal AFC<9, number of high-quality embryos <4, and endometrial thickness on the day of HCG administration <11 mm led to IVF/ICSI failure. The established model exhibited high calibration and discrimination degrees in predicting the outcome of IVF/ICSI.

Conclusion: The risk prediction model for the pregnancy outcome of IVF/ICSI in older women helps evaluate the fertility probability and risk, providing references for formulating reasonable assisted reproduction plans.

Keywords: Older pregnant woman; in vitro fertilization/intracytoplasmic sperm injection; nomogram prediction model; pregnancy outcome; risk factor.

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Conflict of interest statement

There is no conflict of interest.

Figures

Figure 1
Figure 1
Multivariate logistic regression analysis results of factors affecting pregnancy outcome
Figure 2
Figure 2
Construction of nomogram prediction model
Figure 3
Figure 3
Evaluation of nomogram prediction model. A: Calibration curve in the nomogram prediction model of training set, B: Calibration curve in the nomogram prediction model of validation set, C: ROC curves of training set and validation set
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