Prediction of pregnancy outcomes of single vitrified-warmed blastocyst transfer using a combination of the Early Embryo Viability Assessment and morphological assessment
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DOI:
https://doi.org/10.15625/vjbt-22794Keywords:
Automated embryo assessment, blastocyst, Early Embryo Viability Assessment (EEVA), Morphology, Time-lapse Genea Embryo Review Incubator (GERI)Abstract
Morphological assessment is still the gold standard for embryo selection. Artificial Intelligence (AI) has been developed for embryo selection. However, AI is still a complementary method of supporting humans. This study aims to investigate a combination of the Early Embryo Viability Assessment (EEVA) AI and blastocyst morphological assessment as a predictor of pregnancy outcomes of single vitrified-warmed blastocyst transfer. The retrospective cohort study was conducted in a single center from 2020 to 2023 and included 511 single vitrified-warmed blastocyst transfer cycles. Blastocyst transfer quality was based on morphology. Embryos on Day 3 were evaluated using the EEVA system. The correlation between the EEVA system alone, blastocyst morphological assessment alone or a combination of the EEVA system and blastocyst morphological assessment, and pregnancy outcomes was qualified by GEEs. Comparison of 3 methods to evaluate the results of predicting pregnancy outcomes using the area under the curve (AUC): performed on the prediction probability scale of the model rule. The GEE model using the EEVA system showed a negative association between higher EEVA scores and the likelihood of achieving pregnancy outcomes. Embryos with the highest EEVA score (EEVA 5) have substantially lower odds of achieving successful implantation and ongoing pregnancy compared with those with the lowest score (EEVA 1). The OR of Score 5 vs 1 was 0.282 (95% CI 0.125–0.636, p < 0.001) for implantation and 0.228 (95% CI 0.092-0.563, p < 0.001) for ongoing pregnancy. The AUC of the GEE model using the EEVA system for implantation and ongoing pregnancy potential was 0.651 and 0.655, respectively. The AUC of the GEE model using the blastocyst morphological assessment for implantation and ongoing pregnancy potential was 0.703 and 0.700, respectively. The AUC of the GEE model combining both systems for implantation and ongoing pregnancy potential was 0.730 and 0.726. The differences were statistically significant (p < 0.001). The EEVA system can predict the success rates, especially when combining EEVA with blastocyst morphological assessment in blastocyst selection for transfer.
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References
Aparicio B., Cruz, M., & Meseguer M. (2013). Is morphokinetic analysis the answer? Reproductive Biomedicine Online, 27(6), 654–663. https://doi.org/10.1016/j.rbmo.2013.07.017
Aparicio-Ruiz B., Basile N., Pérez Albalá S., Bronet F., Remohí J., & Meseguer M. (2016). Automatic time-lapse instrument is superior to single-point morphology observation for selecting viable embryos: Retrospective study in oocyte donation. Fertility and Sterility, 106(6), 1379-1385.e10. https://doi.org/10.1016/j.fertnstert.2016.07.1117
Balaban B., Brison D., Calderon G., Catt J., Conaghan J., Cowan L., et al. (2011). Istanbul consensus workshop on embryo assessment: Proceedings of an expert meeting. Reproductive BioMedicine Online, 22(6), 632-646. https://doi.org/10.1016/j.rbmo.2011.02.001
Bartolacci A., de Girolamo S., Solano Narduche L., Rabellotti, E., De Santis L., Papaleo E., et al. (2024). Trophectoderm, inner cell mass, and expansion status for live birth prediction after frozen blastocyst transfer: The winner is trophectoderm. Life, 14(11), 1360. https://doi.org/10.3390/life14111360
Bori L., Dominguez F., Fernandez E. I., Gallego R. D., Alegre L., Hickman C., et al. (2021). An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: A preliminary study. Reproductive BioMedicine Online, 42(2), 340–350. https://doi.org/10.1016/j.rbmo.2020.09.031
Bori L., Paya E., Alegre L., Viloria T. A., Remohi J. A., Naranjo V., et al. (2020). Novel and conventional embryo parameters as input data for artificial neural networks: An artificial intelligence model applied for prediction of the implantation potential. Fertility and Sterility, 114(6), 1232–1241. https://doi.org/10.1016/j.fertnstert.2020.08.023
Bormann C. L., Kanakasabapathy M. K., Thirumalaraju P., Gupta R., Pooniwala R., Kandula H., et al. (2020). Performance of a deep learning based neural network in the selection of human blastocysts for implantation. ELife, 9, e55301. https://doi.org/10.7554/eLife.55301
Carrasco B., Arroyo G., Gil Y., Gómez M. J., Rodríguez I., Barri P. N., et al. (2017). Selecting embryos with the highest implantation potential using data mining and decision tree based on classical embryo morphology and morphokinetics. Journal of Assisted Reproduction and Genetics, 34(8), 983–990. https://doi.org/10.1007/s10815-017-0955-x
Chen X., Zhang J., Wu X., Cao S., Zhou L., Wang Y., et al. (2014). Trophectoderm morphology predicts outcomes of pregnancy in vitrified-warmed single-blastocyst transfer cycle in a Chinese population. Reproductive Biology and Endocrinology, 12, 120. https://doi.org/10.1186/s12958-014-0120-2
Conaghan J., Chen A. A., Willman S. P., Ivani K., Chenette P. E., Boostanfar R., et al. (2013). Improving embryo selection using a computer-automated time-lapse image analysis test plus day 3 morphology: Results from a prospective multicenter trial. Fertility and Sterility, 100(2), 412-419.e5. https://doi.org/10.1016/j.fertnstert.2013.04.021
Desai N., Ploskonka S., Goodman L., Attaran M., Goldberg J. M., Austin C., et al. (2016). Delayed blastulation, multinucleation, and expansion grade are independently associated with live-birth rates in frozen blastocyst transfer cycles. Fertility and Sterility, 106(6), 1370–1378. https://doi.org/10.1016/j.fertnstert.2016.07.1095
Diamond M. P., Suraj V., Behnke E. J., Yang X., Angle M. J., Lambe-Steinmiller J. C. ,et al. (2015). Using the Eeva TestTM adjunctively to traditional day 3 morphology is informative for consistent embryo assessment within a panel of embryologists with diverse experience. Journal of Assisted Reproduction and Genetics, 32(1), 61–68. https://doi.org/10.1007/s10815-014-0366-1
Gallego R. D., Remohí J., & Meseguer M. (2019). Time-lapse imaging: The state of the art. Biology of Reproduction, 101(6), 1146–1154. https://doi.org/10.1093/biolre/ioz035
Gardner D. K., & Schoolcraft W. B. (1999). Culture and transfer of human blastocysts. Current Opinion in Obstetrics & Gynecology, 11(3), 307–311. https://doi.org/10.1097/00001703-199906000-00013
ESHRE Working Group on Time-Lapse Technology, Apter S., Ebner T., Freour T., Guns Y., Kovacic B., et al. (2020). Good practice recommendations for the use of time-lapse technology. Human reproduction open, 2020(2): 1-26. https://doi.org/10.1093/hropen/hoaa008
Goodman L. R., Goldberg J., Falcone T., Austin C., & Desai N. (2016). Does the addition of time-lapse morphokinetics in the selection of embryos for transfer improve pregnancy rates? A randomized controlled trial. Fertility and Sterility, 105(2), 275-285.e10. https://doi.org/10.1016/j.fertnstert.2015.10.013
He Y., Chen S., Liu J., Kang X., & Liu H. (2021). Effect of blastocyst morphology and developmental speed on transfer strategy for grade “C” blastocyst in vitrified‐warmed cycles. Journal of Ovarian Research, 14(51). https://doi.org/10.1186/s13048-021-00798-w
Hill M.J., Richter K.S., Heitmann R.J., Graham J.R., Tucker M.J., DeCherney A.H., et al. (2013). Trophectoderm grade predicts outcomes of single-blastocyst transfers. Fertility and Sterility. 99(5), 1283-1289.e1. https://doi:10.1016/j.fertnstert.2012.12.003
Kaser D. J., & Racowsky C. (2014). Clinical outcomes following selection of human preimplantation embryos with time-lapse monitoring: A systematic review. Human Reproduction Update, 20(5), 617–631. https://doi.org/10.1093/humupd/dmu023
Kato K., Ezoe K., Yabuuchi A., Fukuda J., Kuroda T., Ueno, S., et al. (2018). Comparison of pregnancy outcomes following fresh and electively frozen single blastocyst transfer in natural cycle and clomiphene-stimulated IVF cycles. Human Reproduction Open, 2018(3), hoy006. https://doi.org/10.1093/hropen/hoy006
Kieslinger D. C., De Gheselle S., Lambalk C. B., De Sutter P., Kostelijk E. H., Twisk J. W. R., et al. (2016). Embryo selection using time-lapse analysis (Early Embryo Viability Assessment) in conjunction with standard morphology: A prospective two-center pilot study. Human Reproduction, 31(11), 2450–2457. https://doi.org/10.1093/humrep/dew207
Mizobe Y., Ezono Y., Tokunaga M., Oya N., Iwakiri R., Yoshida N., et al. (2017). Selection of human blastocysts with a high implantation potential based on timely compaction. Journal of Assisted Reproduction and Genetics, 34(8), 991–997. https://doi.org/10.1007/s10815-017-0962-y
Motato Y., de los Santos M. J., Escriba M. J., Ruiz B. A., Remohí J., & Meseguer M. (2016). Morphokinetic analysis and embryonic prediction for blastocyst formation through an integrated time-lapse system. Fertility and Sterility, 105(2), 376-384.e9. https://doi.org/10.1016/j.fertnstert.2015.11.001
Revelli A., Canosa S., Carosso A., Filippini C., Paschero C., Gennarelli G., et al. (2019). Impact of the addition of Early Embryo Viability Assessment to morphological evaluation on the accuracy of embryo selection on day 3 or day 5: A retrospective analysis. Journal of Ovarian Research, 12(1), 73. https://doi.org/10.1186/s13048-019-0547-8
Sundvall L., Ingerslev H. J., Breth Knudsen U., & Kirkegaard K. (2013). Inter- and intra-observer variability of time-lapse annotations. Human Reproduction, 28(12), 3215–3221. https://doi.org/10.1093/humrep/det366
Swain J., VerMilyea M. T., Meseguer M., Ezcurra D., Ezcurra D., Letterie G., et al. (2020). AI in the treatment of fertility: Key considerations. Journal of Assisted Reproduction and Genetics, 37(11), 2817–2824. https://doi.org/10.1007/s10815-020-01950-z
Valera M. A., Aparicio-Ruiz B., Pérez-Albalá S., Romany L., Remohí J., & Meseguer M. (2023). Clinical validation of an automatic classification algorithm applied on cleavage stage embryos: Analysis for blastulation, euploidy, implantation, and live-birth potential. Human Reproduction, 38(6), 1060–1075. https://doi.org/10.1093/humrep/dead058
VerMilyea M. D., Tan L., Anthony J. T., Conaghan J., Ivani K., Gvakharia M., et al. (2014). Computer-automated time-lapse analysis results correlate with embryo implantation and clinical pregnancy: A blinded, multi-centre study. Reproductive Biomedicine Online, 29(6), 729–736. https://doi.org/10.1016/j.rbmo.2014.09.005
Wong C., Chen A. A., Behr B., & Shen S. (2013). Time-lapse microscopy and image analysis in basic and clinical embryo development research. Reproductive Biomedicine Online, 26(2), 120–129. https://doi.org/10.1016/j.rbmo.2012.11.003
Yang L., Cai S., Zhang S., Kong X., Gu Y., Lu C., et al. (2018). Single embryo transfer by Day 3 time-lapse selection versus Day 5 conventional morphological selection: A randomized, open-label, non-inferiority trial. Human Reproduction, 33(5), 869–876. https://doi.org/10.1093/humrep/dey047
Zaninovic N., & Rosenwaks Z. (2020). Artificial intelligence in human in vitro fertilization and embryology. Fertility and Sterility, 114(5), 914–920. https://doi.org/10.1016/j.fertnstert.2020.09.157
