Hostname: page-component-848d4c4894-hfldf Total loading time: 0 Render date: 2024-05-21T19:55:09.367Z Has data issue: false hasContentIssue false
  • English
  • Français

Screening the predictors for live birth failure in women after the first frozen embryo transfer based on the Lasso algorithm: a retrospective study

Published online by Cambridge University Press:  15 May 2023

Wumin Jin ,
Jia Lin ,
Peiyu Wang ,
Haiyan Yang  and
Congcong Jin Open the ORCID record for Congcong Jin [Opens in a new window]
Wumin Jin
Affiliation:
Department of Reproductive Medicine Centre, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
Jia Lin
Affiliation:
Department of Reproductive Medicine Centre, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
Peiyu Wang
Affiliation:
Department of Reproductive Medicine Centre, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
Haiyan Yang
Affiliation:
Department of Reproductive Medicine Centre, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
Congcong Jin*
Affiliation:
Department of Reproductive Medicine Centre, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
*
Corresponding author: Congcong Jin. 96 Fu Xue Road, Wenzhou, Zhejiang 325000, China. Tel: +86-577-55579125. Fax: +86-577-55579125. Email: congcongjWMC@163.com
Get access Rights & Permissions [Opens in a new window]

Summary

This study aimed to screen factors related to live birth outcomes of women with first frozen embryo transfer (FET). The enrolled women were divided into training and validation cohorts. The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning and the multiple regression model were then used to screen factors relevant to live birth failure (LBF) for the training dataset. A nomogram risk prediction model was established on the basis of the screened factors, and the consistency index (C-index) and calibration curve were derived for evaluating the model. The validation cohort was utilized to validate the nomogram model further. In total, 2083 women who accepted the first FET in our hospital were included and 44 factors were initially screened in this study. On the basis of the training cohort, the screened risk factors via multiple regression analysis with odds ratio (OR) values were female age (OR: 3.092, 95%CI: 1.065–4.852), body mass index (BMI; OR: 1.106, 95%CI: 1.015–1.546), caesarean section (OR: 1.909, 95%CI: 1.318–2.814), number of high-quality embryos (OR: 0.698, 95%CI: 0.599–0.812), and endometrial thickness (OR: 0.957, CI: 0.904–0.980). The nomogram model was generated based on five predictors. Furthermore, favourable results with C-indexes and calibration curves close to ideal curves indicated the accurate predictive ability of the nomogram. Female age, BMI, caesarean section, number of high-quality embryos, and endometrial thickness were independent predictors for LBF. The five factors of the risk assessment model may help to identify LBF with high accuracy in women who accept FET.

Keywords

Frozen embryo transferLasso regression algorithmLive birthNomogramRisk factor
Type
Research Article
Information
Zygote , Volume 31 , Issue 4 , August 2023 , pp. 350 - 358
Check for updates
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ashrafi, M., Jahangiri, N., Hassani, F., Akhoond, M. R. and Madani, T. (2011). The factors affecting the outcome of frozen–thawed embryo transfer cycle. Taiwanese Journal of Obstetrics and Gynecology, 50(2), 159–164. doi: 10.1016/j.tjog.2011.01.037 CrossRefGoogle ScholarPubMed
Beam, A. L. and Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317–1318. doi: 10.1001/jama.2017.18391 CrossRefGoogle ScholarPubMed
Boerma, T., Ronsmans, C., Melesse, D. Y., Barros, A. J. D., Barros, F. C., Juan, L., Moller, A. B., Say, L., Hosseinpoor, A. R., Yi, M., de Lyra Rabello Neto, D. and Temmerman, M. (2018). Global epidemiology of use of and disparities in caesarean sections. Lancet, 392(10155), 1341–1348. doi: 10.1016/S0140-6736(18)31928-7 CrossRefGoogle ScholarPubMed
Booth, T. C., Williams, M., Luis, A., Cardoso, J., Ashkan, K. and Shuaib, H. (2020). Machine learning and glioma imaging biomarkers. Clinical Radiology, 75(1), 20–32. doi: 10.1016/j.crad.2019.07.001 CrossRefGoogle ScholarPubMed
Bu, Z., Wang, K., Dai, W. and Sun, Y. (2016). Endometrial thickness significantly affects clinical pregnancy and live birth rates in frozen–thawed embryo transfer cycles. Gynecological Endocrinology, 32(7), 524–528. doi: 10.3109/09513590.2015.1136616 CrossRefGoogle ScholarPubMed
Cai, Q. F., Wan, F., Huang, R. and Zhang, H. W. (2011). Factors predicting the cumulative outcome of IVF/ICSI treatment: A multivariable analysis of 2450 patients. Human Reproduction, 26(9), 2532–2540. doi: 10.1093/humrep/der228 CrossRefGoogle ScholarPubMed
Chen, J. H. and Asch, S. M. (2017). Machine learning and prediction in medicine – Beyond the peak of inflated expectations. New England Journal of Medicine, 376(26), 2507–2509. doi: 10.1056/NEJMp1702071 CrossRefGoogle Scholar
Coates, A., Kung, A., Mounts, E., Hesla, J., Bankowski, B., Barbieri, E., Ata, B., Cohen, J. and Munné, S. (2017). Optimal euploid embryo transfer strategy, fresh versus frozen, after preimplantation genetic screening with next generation sequencing: A randomized controlled trial. Fertility and Sterility, 107(3), 723–730.e3 e723. doi: 10.1016/j.fertnstert.2016.12.022 CrossRefGoogle ScholarPubMed
Diao, J., Gao, G., Zhang, Y., Wang, X., Zhang, Y., Han, Y., Du, A. and Luo, H. (2021). Caesarean section defects may affect pregnancy outcomes after in vitro fertilization-embryo transfer: A retrospective study. BMC Pregnancy and Childbirth, 21(1), 487. doi: 10.1186/s12884-021-03955-7 CrossRefGoogle ScholarPubMed
El-Toukhy, T., Coomarasamy, A., Khairy, M., Sunkara, K., Seed, P., Khalaf, Y. and Braude, P. (2008). The relationship between endometrial thickness and outcome of medicated frozen embryo replacement cycles. Fertility and Sterility, 89(4), 832–839. doi: 10.1016/j.fertnstert.2007.04.031 CrossRefGoogle ScholarPubMed
Ghobara, T. and Vandekerckhove, P. (2008). Cycle regimens for frozen–thawed embryo transfer. Cochrane Database of Systematic Reviews, 23(1), CD003414. doi: 10.1002/14651858.CD003414.pub2 Google ScholarPubMed
Huang, Y. Q., Liang, C. H., He, L., Tian, J., Liang, C. S., Chen, X., Ma, Z. L. and Liu, Z. Y. (2016). Development and validation of a Radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. Journal of Clinical Oncology, 34(18), 2157–2164. doi: 10.1200/JCO.2015.65.9128 CrossRefGoogle ScholarPubMed
Insogna, I. G., Lee, M. S., Reimers, R. M. and Toth, T. L. (2017). Neutral effect of body mass index on implantation rate after frozen–thawed blastocyst transfer. Fertility and Sterility, 108(5), 770–776.e1 e771. doi: 10.1016/j.fertnstert.2017.08.024 CrossRefGoogle ScholarPubMed
Karlström, P. O., Bergh, T., Forsberg, A. S., Sandkvist, U. and Wikland, M. (1997). Prognostic factors for the success rate of embryo freezing. Human Reproduction, 12(6), 1263–1266. doi: 10.1093/humrep/12.6.1263 CrossRefGoogle ScholarPubMed
Kim, H. O., Sung, N. and Song, I. O. (2017). Predictors of live birth and pregnancy success after in vitro fertilization in infertile women aged 40 and over. Clinical and Experimental Reproductive Medicine, 44(2), 111–117. doi: 10.5653/cerm.2017.44.2.111 CrossRefGoogle ScholarPubMed
Miller, E. S., Hahn, K., Grobman, W. A. and Society for Maternal-Fetal Medicine Health Policy Committee. (2013). Consequences of a primary elective cesarean delivery across the reproductive life. Obstetrics and Gynecology, 121(4), 789–797. doi: 10.1097/AOG.0b013e3182878b43 CrossRefGoogle ScholarPubMed
Nelson, S. M. and Lawlor, D. A. (2011). Predicting live birth, preterm delivery, and low birth weight in infants born from in vitro fertilisation: A prospective study of 144,018 treatment cycles. PLOS Medicine, 8(1), e1000386. doi: 10.1371/journal.pmed.1000386 CrossRefGoogle Scholar
Pan, Y., Hao, G., Wang, Q., Liu, H., Wang, Z., Jiang, Q., Shi, Y. and Chen, Z. J. (2020). Major factors affecting the live birth rate after frozen embryo transfer among young women. Frontiers in Medicine, 7, 94. doi: 10.3389/fmed.2020.00094 CrossRefGoogle ScholarPubMed
Patounakis, G., Ozcan, M. C., Chason, R. J., Norian, J. M., Payson, M., DeCherney, A. H. and Yauger, B. J. (2016). Impact of a prior cesarean delivery on embryo transfer: A prospective study. Fertility and Sterility, 106(2), 311–316. doi: 10.1016/j.fertnstert.2016.03.045 CrossRefGoogle ScholarPubMed
Radhakrishnan, A., Damodaran, K., Soylemezoglu, A. C., Uhler, C. and Shivashankar, G. V. (2017). Machine learning for nuclear Mechano-morphometric biomarkers in cancer diagnosis. Scientific Reports, 7(1), 17946. doi: 10.1038/s41598-017-17858-1 CrossRefGoogle ScholarPubMed
Reed, M. L., Said, A. H., Thompson, D. J. and Caperton, C. L. (2015). Large-volume vitrification of human biopsied and non-biopsied blastocysts: A simple, robust technique for cryopreservation. Journal of Assisted Reproduction and Genetics, 32(2), 207–214. doi: 10.1007/s10815-014-0395-9 CrossRefGoogle ScholarPubMed
Riemma, G., De Franciscis, P., Torella, M., Narciso, G., La Verde, M., Morlando, M., Cobellis, L. and Colacurci, N. (2021). Reproductive and pregnancy outcomes following embryo transfer in women with previous cesarean section: A systematic review and meta-analysis. Acta Obstetricia et Gynecologica Scandinavica, 100(11), 1949–1960. doi: 10.1111/aogs.14239 CrossRefGoogle ScholarPubMed
Roque, M., Valle, M., Sampaio, M. and Geber, S. (2018). Obstetric outcomes after fresh versus frozen–thawed embryo transfers: A systematic review and meta-analysis. JBRA Assisted Reproduction, 22(3), 253–260. doi: 10.5935/1518-0557.20180049 Google ScholarPubMed
Salumets, A., Suikkari, A. M., Mäkinen, S., Karro, H., Roos, A. and Tuuri, T. (2006). Frozen embryo transfers: Implications of clinical and embryological factors on the pregnancy outcome. Human Reproduction, 21(9), 2368–2374. doi: 10.1093/humrep/del151 CrossRefGoogle ScholarPubMed
Sauerbrei, W., Royston, P. and Binder, H. (2007). Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Statistics in Medicine, 26(30), 5512–5528. doi: 10.1002/sim.3148 CrossRefGoogle ScholarPubMed
Shi, Y., Sun, Y., Hao, C., Zhang, H., Wei, D., Zhang, Y., Zhu, Y., Deng, X., Qi, X., Li, H., Ma, X., Ren, H., Wang, Y., Zhang, D., Wang, B., Liu, F., Wu, Q., Wang, Z., Bai, H., et al. (2018). Transfer of fresh versus frozen embryos in ovulatory women. New England Journal of Medicine, 378(2), 126–136. doi: 10.1056/NEJMoa1705334 CrossRefGoogle ScholarPubMed
Tiitinen, A., Halttunen, M., Härkki, P., Vuoristo, P. and Hyden-Granskog, C. (2001). Elective single embryo transfer: The value of cryopreservation. Human Reproduction, 16(6), 1140–1144. doi: 10.1093/humrep/16.6.1140 CrossRefGoogle ScholarPubMed
Trounson, A. and Mohr, L. (1983). Human pregnancy following cryopreservation, thawing and transfer of an eight-cell embryo. Nature, 305(5936), 707–709. doi: 10.1038/305707a0 CrossRefGoogle ScholarPubMed
Van Royen, E., Mangelschots, K., De Neubourg, D., Valkenburg, M., Van de Meerssche, M., Ryckaert, G., Eestermans, W. and Gerris, J. (1999). Characterization of a top quality embryo, a step towards single-embryo transfer. Human Reproduction, 14(9), 2345–2349. doi: 10.1093/humrep/14.9.2345 CrossRefGoogle ScholarPubMed
Veleva, Z., Orava, M., Nuojua-Huttunen, S., Tapanainen, J. S. and Martikainen, H. (2013). Factors affecting the outcome of frozen–thawed embryo transfer. Human Reproduction, 28(9), 2425–2431. doi: 10.1093/humrep/det251 CrossRefGoogle ScholarPubMed
Wang, J. X., Yap, Y. Y. and Matthews, C. D. (2001). Frozen–thawed embryo transfer: Influence of clinical factors on implantation rate and risk of multiple conception. Human Reproduction, 16(11), 2316–2319. doi: 10.1093/humrep/16.11.2316 CrossRefGoogle ScholarPubMed
Wei, D., Liu, J. Y., Sun, Y., Shi, Y., Zhang, B., Liu, J. Q., Tan, J., Liang, X., Cao, Y., Wang, Z., Qin, Y., Zhao, H., Zhou, Y., Ren, H., Hao, G., Ling, X., Zhao, J., Zhang, Y., Qi, X., et al. (2019). Frozen versus fresh single blastocyst transfer in ovulatory women: A multicentre, randomised controlled trial. Lancet, 393(10178), 1310–1318. doi: 10.1016/S0140-6736(18)32843-5 CrossRefGoogle ScholarPubMed
Zhang, T., Li, Z., Ren, X., Huang, B., Zhu, G., Yang, W. and Jin, L. (2018). Endometrial thickness as a predictor of the reproductive outcomes in fresh and frozen embryo transfer cycles: A retrospective cohort study of 1512 IVF cycles with morphologically good-quality blastocyst. Medicine (Baltimore), 97(4), e9689. doi: 10.1097/MD.0000000000009689 CrossRefGoogle ScholarPubMed