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Wu J, Li T, Xu L, Chen L, Liang X, Lin A, Zhang W, Huang R. Development of a machine learning-based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population. J Assist Reprod Genet 2024:10.1007/s10815-024-03153-2. [PMID: 38819714 DOI: 10.1007/s10815-024-03153-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024] Open
Abstract
PURPOSE This study aimed to evaluate the effectiveness of a random forest (RF) model in predicting clinical pregnancy outcomes from intrauterine insemination (IUI) and identifying significant factors affecting IUI pregnancy in a large Chinese population. METHODS RESULTS: A total of 11 variables, including eight from female (age, body mass index, duration of infertility, prior miscarriage, and spontaneous abortion), hormone levels (anti-Müllerian hormone, follicle-stimulating hormone, luteinizing hormone), and three from male (smoking, semen volume, and sperm concentration), were identified as the significant variables associated with IUI clinical pregnancy in our Chinese dataset. The RF-based prediction model presents an area under the receiver operating characteristic curve (AUC) of 0.716 (95% confidence interval, 0.6914-0.7406), an accuracy rate of 0.6081, a sensitivity rate of 0.7113, and a specificity rate of 0.505. Importance analysis indicated that semen volume was the most vital variable in predicting IUI clinical pregnancy. CONCLUSIONS The machine learning-based IUI clinical pregnancy prediction model showed a promising predictive efficacy that could provide a potent tool to guide selecting targeted infertile couples beneficial from IUI treatment, and also identify which parameters are most relevant in IUI clinical pregnancy.
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Affiliation(s)
- Jialin Wu
- Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China
- Guangdong Engineering Technology Research Center of Fertility Preservation, Guangzhou, 510000, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510000, China
- School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan Second Road, Guangzhou, 510000, China
| | - Tingting Li
- Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China
- Guangdong Engineering Technology Research Center of Fertility Preservation, Guangzhou, 510000, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Linan Xu
- Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China
- Guangdong Engineering Technology Research Center of Fertility Preservation, Guangzhou, 510000, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Lina Chen
- Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China
- Guangdong Engineering Technology Research Center of Fertility Preservation, Guangzhou, 510000, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Xiaoyan Liang
- Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China
- Guangdong Engineering Technology Research Center of Fertility Preservation, Guangzhou, 510000, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Aihua Lin
- School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan Second Road, Guangzhou, 510000, China
| | - Wangjian Zhang
- School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan Second Road, Guangzhou, 510000, China.
| | - Rui Huang
- Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China.
- Guangdong Engineering Technology Research Center of Fertility Preservation, Guangzhou, 510000, China.
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510000, China.
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Effect of Gonadotropin Types and Indications on Homologous Intrauterine Insemination Success: A Study from 1251 Cycles and a Review of the Literature. BIOMED RESEARCH INTERNATIONAL 2017; 2017:3512784. [PMID: 29387719 PMCID: PMC5745683 DOI: 10.1155/2017/3512784] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 11/14/2017] [Accepted: 11/21/2017] [Indexed: 12/23/2022]
Abstract
Objective To evaluate the IUI success factors relative to controlled ovarian stimulation (COS) and infertility type, this retrospective cohort study included 1251 couples undergoing homologous IUI. Results We achieved 13% clinical pregnancies and 11% live births. COS and infertility type do not have significant effect on IUI clinical outcomes with unstable intervention of various couples' parameters, including the female age, the IUI attempt rank, and the sperm quality. Conclusion Further, the COS used seemed a weak predictor for IUI success; therefore, the indications need more discussion, especially in unexplained infertility cases involving various factors. Indeed, the fourth IUI attempt, the female age over 40 years, and the total motile sperm count <5 × 106 were critical in decreasing the positive clinical outcomes of IUI. Those parameter cut-offs necessitate a larger analysis to give infertile couples more chances through IUI before carrying out other ART techniques.
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Jeon YE, Jung JA, Kim HY, Seo SK, Cho S, Choi YS, Lee BS. Predictive factors for pregnancy during the first four intrauterine insemination cycles using gonadotropin. Gynecol Endocrinol 2013; 29:834-8. [PMID: 23862582 DOI: 10.3109/09513590.2013.808324] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
PURPOSE Although a variety of factors have been reported as affecting pregnancy rates after intrauterine insemination (IUI), there have been conflicting results on prognostic factors. This study aimed to determine predictive factors for pregnancy in patients undergoing the first four IUI cycles. METHODS A total of 348 IUI cycles using clomiphene citrate or letrozole combined with gonadotropin, or gonadotropin only were analyzed. Baseline clinical characteristics, variables related to ovulation induction and sperm parameters were compared between pregnant (n = 54) and non-pregnant groups (n = 294). Logistic regression analysis was performed to identify factors that could predict a pregnancy. RESULTS The overall clinical pregnancy rate was 15.5% (54/348) per cycle and 30.0% (54/180) per couple. During the first four IUI cycles, logistic regression analysis revealed that woman who were 39 years or older (OR: 0.263, 95% CI: 0.076-0.906, p = 0.034), longer duration of infertility (OR: 0.967, 95% CI: 0.942-0.993, p = 0.012), endometriosis (versus unexplained infertility; OR: 0.177, 95% CI: 0.040-0.775, p = 0.022) and endometrial thickness below 7 mm (OR: 0.114, 95% CI: 0.015-0.862, p = 0.035) were unfavorable factors to predict clinical pregnancy. CONCLUSIONS Women with old age, longer duration of infertility, the presence of endometriosis or thin endometrium in the preovulatory phase may have unfavorable outcomes during the first four IUI cycles.
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Affiliation(s)
- Young Eun Jeon
- Department of Obstetrics and Gynecology, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu Seoul, Republic of Korea
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