Does conventional morphological evaluation still play a role in predicting blastocyst formation?

Which transfer day results in the highest live birth rate for PCOS patients undergoing in vitro fertilization?

BACKGROUND

Polycystic ovary syndrome (PCOS) has unusual levels of hormones. The hormone receptors in the endometrium have a hostile effect and make the microenvironment unfavorable for embryo implantation. The use of gonadotropin stimulation during in vitro fertilization (IVF) may have an impact on embryo implantation and live birth rate. According to recent data, the clinical results of day 4 embryo transfer (D4 transfer) were on par with those of day 5 embryo transfer (D5 transfer) in IVF-ET. There are few studies comparing the outcomes of transplants with various etiologies and days. The purpose of this study was to determine which transfer day had the best result for PCOS patients undergoing IVF.

METHODS

This retrospective cohort study was conducted in the Xingtai Infertility Specialist Hospital between January 2017 and November 2021. A total of 1,664 fresh ART cycles met inclusion criteria, including 242 PCOS transfers and 1422 tubal factor infertility transfers.

CONCLUSIONS

PCOS individuals had the highest live birth rate on D4 transferred. It was not need to culture embryos to blastocysts to optimize embryo transfer for PCOS women. This could be a novel approach to transplantation for PCOS.

Prediction model for day 3 embryo implantation potential based on metabolites in spent embryo culture medium

BACKGROUND

Metabolites in spent embryo culture medium correlate with the embryo's viability. However, there is no widely accepted method using metabolite dada to predict successful implantation. We sought to combine metabolomic profiling of spent embryo culture medium and clinical variables to create an implantation prediction model as an adjunct to morphological screening of day 3 embryos.

METHODS

This investigation was a prospective, nested case-control study. Forty-two day 3 embryos from 34 patients were transferred, and the spent embryo culture medium was collected. Twenty-two embryos implanted successfully, and the others failed. Metabolites in the medium relevant to implantation were detected and measured by Liquid Chromatography-Mass Spectrometry. Clinical signatures relevant to embryo implantation were subjected to univariate analysis to select candidates for a prediction model. Multivariate logistical regression of the clinical and metabolomic candidates was used to construct a prediction model for embryo implantation potential.

RESULTS

The levels of 13 metabolites were significantly different between the successful and failed groups, among which five were most relevant and interpretable selected by Least Absolute Shrinkage and Selection Operator regression analysis. None of the clinical variables significantly affected day 3 embryo implantation. The most relevant and interpretable set of metabolites was used to construct a prediction model for day 3 embryo implantation potential with an accuracy of 0.88.

CONCLUSIONS

Day 3 embryos'implantation potential could be noninvasively predicted by the spent embryo culture medium's metabolites measured by LC-MS. This approach may become a useful adjunct to morphological evaluation of day 3 embryos.

Predicting the unexpected total fertilization failure in conventional in vitro fertilization cycles: What is the role of semen quality?

Background: Male and female gametes factors might contribute to the total fertilization failure (TFF). In first in vitro fertilization (IVF) cycles, decision-making of insemination protocol was mainly based on semen quality for the contribution of female clinical characteristics to TFF remained obscure. The purpose of the study was to evaluate the role of semen quality in predicting unexpected TFF. Methods: A single-center retrospective cohort analysis was performed on 19539 cycles between 2013 and 2021. Two algorithms, a Least Absolute Shrinkage and Selection Operator (LASSO) and an Extreme Gradient Boosting (Xgboost) were used to create models with cycle characteristics parameters. By including semen parameters or not, the contribution of semen parameters to the performance of the models was evaluated. The area under the curve (AUC), the calibration, and the net reclassification index (NRI) were used to evaluate the performance of the models. Results: The prevalence of TFF were .07 (95%CI:0.07-0.08), and .08 (95%CI:0.07-0.09) respectively in the development and validation group. Including all characteristics, with the models of LASSO and Xgboost, TFF was predicted with the AUCs of .74 (95%CI:0.72-0.77) and .75 (95%CI:0.72-0.77) in the validation group. The AUCs with models of LASSO and Xgboost without semen parameters were .72 (95%CI:0.69-0.74) and .73 (95%CI:0.7-0.75). The models of LASSO and Xgboost with semen parameters only gave the AUCs of .58 (95%CI:0.55-0.61) and .57 (95%CI:0.55-0.6). For the overall validation cohort, the event NRI values were -5.20 for the LASSO model and -.71 for the Xgboost while the non-event NRI values were 10.40 for LASSO model and 0.64 for Xgboost. In the subgroup of poor responders, the prevalence was .21 (95%CI:0.18-0.24). With refitted models of LASSO and Xgboost, the AUCs were .72 (95%CI:0.67-0.77) and .69 (95%CI:0.65-0.74) respectively. Conclusion: In unselected patients, semen parameters contribute to limited value in predicting TFF. However, oocyte yield is an important predictor for TFF and the prevalence of TFF in poor responders was high. Because reasonable predicting power for TFF could be achieved in poor responders, it may warrant further study to prevent TFF in these patients.