Risk factors for recurrent implantation failure as defined by the European Society for Human Reproduction and Embryology

Can Wang, Youhui Lu, Minglin Ou, Miaoxian Ou, Lingxuan Qian, L. X. Qian, Yingying Zhang, Yuxin Yang, Lu Luo, Qiong Wang
Human Reproduction · 2025 · vol. 40(6) , pp. 1138–1147 · doi:10.1093/humrep/deaf042 · PMID:40143620 · W4408921373
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⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

Anti-Müllerian hormone is the strongest predictor for recurrent implantation failure, followed by chronic endometritis, intrauterine adhesions, and BMI.

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Abstract

STUDY QUESTION: What are the unrecognized risk factors for recurrent implantation failure (RIF) as defined in the ESHRE recommendation? SUMMARY ANSWER: Anti-Müllerian hormone (AMH) is the strongest predictor for RIF, followed by chronic endometritis (CE), intrauterine adhesions, and BMI. WHAT IS KNOWN ALREADY: Advanced age is a well-known risk factor for implantation failure, and the definition of RIF was stratified by age in the 2023 ESHRE recommendation. However, the literature identifies other risk factors, including CE, endometriosis, BMI, endometrial polyps, intrauterine adhesions, hydrosalpinx, uterine malformation, submucosal myoma, polycystic ovary syndrome, thyroid dysfunction, rheumatic diseases, and hyperprolactinemia, to be associated with implantation failure. In addition, our clinical experience suggests AMH and a history of previous livebirth affect RIF. It remains unclear which of these factors are the best predictors of RIF. STUDY DESIGN, SIZE, DURATION: A cohort study drawn from ART cycles between June 2019 and June 2022. PARTICIPANTS/MATERIALS, SETTING, METHODS: Two hundred and ninety-eight RIF patients and 2056 controls (women who achieved successful embryo implantation within 1-2 transfer cycles) were identified from 15 329 ART cycles at the Reproductive Medical Center at the First Affiliated Hospital of Sun Yat-sen University. RIF was defined according to the recommendation of ESHRE 2023. Basic characteristics, reproductive history, laboratory indicators (autoantibodies and endocrine factors), ultrasound, laparoscopy, hysteroscopy, hysterosalpingography, biopsy, and immunohistochemistry results were collected from the electronic medical record system. The Random Forest procedure was applied to build a machine learning model for predicting RIF. Overall predictive accuracy was assessed by using the AUC of receiver-operator characteristic curve and calibration plots. The SHapley Additive exPlanations (SHAP) framework was used to interpret the model. MAIN RESULTS AND THE ROLE OF CHANCE: From 32 variables, elevated AMH level and greater number of live births were associated with lower risk of RIF, while CE, intrauterine adhesions, high FSH level, high testosterone level, advanced female age, polyps, history of recurrent pregnancy loss, history of cesarean section, polycystic ovary syndrome, and rheumatic diseases were associated with higher risk of RIF according to the established random forest model. The predictive model yielded AUCs of 0.83 (95% CI: 0.80-0.86) in training dataset and 0.78 (95% CI: 0.73-0.84) in testing dataset. The calibration curve indicated good predictive performance in both training and testing datasets. SHAP values indicated that AMH had the greatest influence on the RIF risks, whereas CE, intrauterine adhesions, and BMI were the second, third, and fourth most significant risk factors for predicting RIF, respectively. LIMITATIONS, REASONS FOR CAUTION: This research was limited by its retrospective design from a single reproductive medical center. Moreover, some diseases, such as polyps, submucosal myomas, and rheumatic diseases, had been treated before ART, which indicates that these factors impact RIF even after treatment. WIDER IMPLICATIONS OF THE FINDINGS: In addition to age, certain high-risk factors, such as AMH, should also be included in the considerations for RIF. Patients with a combination of these high-risk factors may require more attempts to achieve a successful pregnancy. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the Guangzhou Municipal Science and Technology Project under Grant (202206010003) and the National Natural Science Foundation of China under Grant (81871159). None of the authors has any conflict of interest. TRIAL REGISTRATION NUMBER: N/A.

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Condition tags

endometriosis

MeSH descriptors

Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation Embryo Implantation

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