Predictive models of recurrent implantation failure in patients receiving ART treatment based on clinical features and routine laboratory data
Abstract Study question The objective was to construct a model for predicting the probability of recurrent implantation failure (RIF) after assisted reproductive technology (ART) treatment based on the clinical characteristics and routine laboratory test data of infertile patients. Summary answer A...
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BMC
2024-03-01
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| Series: | Reproductive Biology and Endocrinology |
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| Online Access: | https://doi.org/10.1186/s12958-024-01203-z |
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| author | Qunying Fang Zonghui Qiao Lei Luo Shun Bai Min Chen Xiangjun Zhang Lu Zong Xian-hong Tong Li-min Wu |
| author_facet | Qunying Fang Zonghui Qiao Lei Luo Shun Bai Min Chen Xiangjun Zhang Lu Zong Xian-hong Tong Li-min Wu |
| author_sort | Qunying Fang |
| collection | DOAJ |
| description | Abstract Study question The objective was to construct a model for predicting the probability of recurrent implantation failure (RIF) after assisted reproductive technology (ART) treatment based on the clinical characteristics and routine laboratory test data of infertile patients. Summary answer A model was developed to predict RIF. The model showed high calibration in external validation, helped to identify risk factors for RIF, and improved the efficacy of ART therapy. What is known already Research on the influencing factors of RIF has focused mainly on embryonic factors, endometrial receptivity, and immune factors. However, there are many kinds of examinations regarding these aspects, and comprehensive screening is difficult because of the limited time and economic conditions. Therefore, we should try our best to analyse the results of routine infertility screenings to make general predictions regarding the occurrence of RIF. Study design, size, duration A retrospective study was conducted with 5212 patients at the Reproductive Center of the First Affiliated Hospital of USTC from January 2018 to June 2022. Participants/materials, setting, methods This study included 462 patients in the RIF group and 4750 patients in the control group. The patients’ basic characteristics, clinical treatment data, and laboratory test indices were compared. Logistic regression was used to analyse RIF-related risk factors, and the prediction model was evaluated by receiver operating characteristic (ROC) curves and the corresponding areas under the curve (AUCs). Further analysis of the influencing factors of live births in the first cycle of subsequent assisted reproduction treatment in RIF patients was performed, including the live birth subgroup (n = 116) and the no live birth subgroup (n = 200). Main results and the role of chance (1) An increased duration of infertility (1.978; 95% CI, 1.264–3.097), uterine cavity abnormalities (2.267; 95% CI, 1.185–4.336), low AMH levels (0.504; 95% CI, 0.275–0.922), insulin resistance (3.548; 95% CI, 1.931–6.519), antinuclear antibody (ANA)-positive status (3.249; 95% CI, 1.20-8.797) and anti-β2-glycoprotein I antibody (A-β2-GPI Ab)-positive status (5.515; 95% CI, 1.481–20.536) were associated with an increased risk of RIF. The area under the curve of the logistic regression model was 0.900 (95% CI, 0.870–0.929) for the training cohort and 0.895 (95% CI, 0.865–0.925) for the testing cohort. (2) Advanced age (1.069; 95% CI, 1.015–1.126) was a risk factor associated with no live births after the first cycle of subsequent assisted reproduction treatment in patients with RIF. Blastocyst transfer (0.365; 95% CI = 0.181–0.736) increased the probability of live birth in subsequent cycles in patients with RIF. The area under the curve of the logistic regression model was 0.673 (95% CI, 0.597–0.748). Limitations, reasons for caution This was a single-centre regression study, for which the results need to be evaluated and verified by prospective large-scale randomized controlled studies. The small sample size for the analysis of factors influencing pregnancy outcomes in subsequent assisted reproduction cycles for RIF patients resulted in the inclusion of fewer covariates, and future studies with larger samples and the inclusion of more factors are needed for assessment and validation. Wider implications of the findings Prediction of embryo implantation prior to transfer will facilitate the clinical management of patients and disease prediction and further improve ART treatment outcomes. Study funding/competing interest(s) This work was supported by the General Project of the National Natural Science Foundation of China (Nos. 82374212, 81971446, 82301871, and 82201792) and the Natural Science Foundation of Anhui Province (No. 2208085MH206). There are no conflicts of interest to declare. Trial registration number This study was registered with the Chinese Clinical Trial Register (Clinical Trial Number: ChiCTR1800018298 ). |
| format | Article |
| id | doaj-art-69048932a6da46da8d3100cb80cac8f9 |
| institution | OA Journals |
| issn | 1477-7827 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | BMC |
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| series | Reproductive Biology and Endocrinology |
| spelling | doaj-art-69048932a6da46da8d3100cb80cac8f92025-08-20T02:13:55ZengBMCReproductive Biology and Endocrinology1477-78272024-03-0122111010.1186/s12958-024-01203-zPredictive models of recurrent implantation failure in patients receiving ART treatment based on clinical features and routine laboratory dataQunying Fang0Zonghui Qiao1Lei Luo2Shun Bai3Min Chen4Xiangjun Zhang5Lu Zong6Xian-hong Tong7Li-min Wu8Center for Reproduction and Genetics, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of ChinaCenter for Reproduction and Genetics, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of ChinaCenter for Reproduction and Genetics, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of ChinaCenter for Reproduction and Genetics, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of ChinaCenter for Reproduction and Genetics, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of ChinaCenter for Reproduction and Genetics, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of ChinaCenter for Reproduction and Genetics, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of ChinaCenter for Reproduction and Genetics, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of ChinaCenter for Reproduction and Genetics, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of ChinaAbstract Study question The objective was to construct a model for predicting the probability of recurrent implantation failure (RIF) after assisted reproductive technology (ART) treatment based on the clinical characteristics and routine laboratory test data of infertile patients. Summary answer A model was developed to predict RIF. The model showed high calibration in external validation, helped to identify risk factors for RIF, and improved the efficacy of ART therapy. What is known already Research on the influencing factors of RIF has focused mainly on embryonic factors, endometrial receptivity, and immune factors. However, there are many kinds of examinations regarding these aspects, and comprehensive screening is difficult because of the limited time and economic conditions. Therefore, we should try our best to analyse the results of routine infertility screenings to make general predictions regarding the occurrence of RIF. Study design, size, duration A retrospective study was conducted with 5212 patients at the Reproductive Center of the First Affiliated Hospital of USTC from January 2018 to June 2022. Participants/materials, setting, methods This study included 462 patients in the RIF group and 4750 patients in the control group. The patients’ basic characteristics, clinical treatment data, and laboratory test indices were compared. Logistic regression was used to analyse RIF-related risk factors, and the prediction model was evaluated by receiver operating characteristic (ROC) curves and the corresponding areas under the curve (AUCs). Further analysis of the influencing factors of live births in the first cycle of subsequent assisted reproduction treatment in RIF patients was performed, including the live birth subgroup (n = 116) and the no live birth subgroup (n = 200). Main results and the role of chance (1) An increased duration of infertility (1.978; 95% CI, 1.264–3.097), uterine cavity abnormalities (2.267; 95% CI, 1.185–4.336), low AMH levels (0.504; 95% CI, 0.275–0.922), insulin resistance (3.548; 95% CI, 1.931–6.519), antinuclear antibody (ANA)-positive status (3.249; 95% CI, 1.20-8.797) and anti-β2-glycoprotein I antibody (A-β2-GPI Ab)-positive status (5.515; 95% CI, 1.481–20.536) were associated with an increased risk of RIF. The area under the curve of the logistic regression model was 0.900 (95% CI, 0.870–0.929) for the training cohort and 0.895 (95% CI, 0.865–0.925) for the testing cohort. (2) Advanced age (1.069; 95% CI, 1.015–1.126) was a risk factor associated with no live births after the first cycle of subsequent assisted reproduction treatment in patients with RIF. Blastocyst transfer (0.365; 95% CI = 0.181–0.736) increased the probability of live birth in subsequent cycles in patients with RIF. The area under the curve of the logistic regression model was 0.673 (95% CI, 0.597–0.748). Limitations, reasons for caution This was a single-centre regression study, for which the results need to be evaluated and verified by prospective large-scale randomized controlled studies. The small sample size for the analysis of factors influencing pregnancy outcomes in subsequent assisted reproduction cycles for RIF patients resulted in the inclusion of fewer covariates, and future studies with larger samples and the inclusion of more factors are needed for assessment and validation. Wider implications of the findings Prediction of embryo implantation prior to transfer will facilitate the clinical management of patients and disease prediction and further improve ART treatment outcomes. Study funding/competing interest(s) This work was supported by the General Project of the National Natural Science Foundation of China (Nos. 82374212, 81971446, 82301871, and 82201792) and the Natural Science Foundation of Anhui Province (No. 2208085MH206). There are no conflicts of interest to declare. Trial registration number This study was registered with the Chinese Clinical Trial Register (Clinical Trial Number: ChiCTR1800018298 ).https://doi.org/10.1186/s12958-024-01203-zRecurrent implantation failureAssisted reproductive technologyLogistic regression analysisRisk factors |
| spellingShingle | Qunying Fang Zonghui Qiao Lei Luo Shun Bai Min Chen Xiangjun Zhang Lu Zong Xian-hong Tong Li-min Wu Predictive models of recurrent implantation failure in patients receiving ART treatment based on clinical features and routine laboratory data Reproductive Biology and Endocrinology Recurrent implantation failure Assisted reproductive technology Logistic regression analysis Risk factors |
| title | Predictive models of recurrent implantation failure in patients receiving ART treatment based on clinical features and routine laboratory data |
| title_full | Predictive models of recurrent implantation failure in patients receiving ART treatment based on clinical features and routine laboratory data |
| title_fullStr | Predictive models of recurrent implantation failure in patients receiving ART treatment based on clinical features and routine laboratory data |
| title_full_unstemmed | Predictive models of recurrent implantation failure in patients receiving ART treatment based on clinical features and routine laboratory data |
| title_short | Predictive models of recurrent implantation failure in patients receiving ART treatment based on clinical features and routine laboratory data |
| title_sort | predictive models of recurrent implantation failure in patients receiving art treatment based on clinical features and routine laboratory data |
| topic | Recurrent implantation failure Assisted reproductive technology Logistic regression analysis Risk factors |
| url | https://doi.org/10.1186/s12958-024-01203-z |
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