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Background: Advanced models including time-lapse imaging and artificial intelligence technologies have been used to predict blastocyst formation. However, the conventional morphological evaluation of embryos is still widely used. The purpose of the present study was to evaluate the predictive power of conventional morphological evaluation regarding blastocyst formation. Methods: Retrospective evaluation of data from 15,613 patients receiving blastocyst culture from January 2013 through December 2020 in our institution were reviewed. Generalized estimating equations (GEE) were used to establish the morphology-based model. To estimate whether including more features regarding patient characteristics and cycle parameters improve the predicting power, we also establish models including 27 more features with either LASSO regression or XGbosst. The predicted number of blastocyst were associated with the observed number of the blastocyst and were used to predict the blastocyst transfer cancellation either in fresh or frozen cycles. Results: Based on early cleavage and routine observed morphological parameters (cell number, fragmentation, and symmetry), the GEE model predicted blastocyst formation with an AUC of 0.779(95%CI: 0.77–0.787) and an accuracy of 74.7%(95%CI: 73.9%-75.5%) in the validation set. LASSO regression model and XGboost model based on the combination of cycle characteristics and embryo morphology yielded similar predicting power with AUCs of 0.78(95%CI: 0.771–0.789) and 0.754(95%CI: 0.745–0.763), respectively. For per-cycle blastocyst yield, the predicted number of blastocysts using morphological parameters alone strongly correlated with observed blastocyst number (r = 0.897, P < 0.0001) and predicted blastocyst transfer cancel with an AUC of 0.926((95%CI: 0.911–0.94). Conclusion: The data suggested that routine morphology observation remained a feasible tool to support an informed decision regarding the day of transfer. However, models based on the combination of cycle characteristics and embryo morphology do not increase the predicting power significantly. [ABSTRACT FROM AUTHOR]

Background: While single embryo transfer (SET) is widely advocated, double embryo transfer (DET) remains preferable in clinical practice to improve IVF success rate, especially in poor prognosis patients with only poor quality embryos (PQEs) available in addition to one or no good quality embryos (GQEs). Furthermore, previous studies suggest PQE might adversely affect the implantation of a GQE when transferred together. This study aims to evaluate the effect of transferring an additional PQE with a GQE on the outcomes in poor prognosis patients. Methods: A total of 5037 frozen-thawed blastocyst transfer (FBT) cycles between January 2012 and May 2019 were included. Propensity score matching was applied to control for potential confounders, and we used generalized estimating equations (GEE) models to identify the association between the effect of an additional PQE and the outcomes. Results: Overall, transferring a PQE with GQE (Group GP) achieved significantly higher pregnancy rate (PR), live birth rate (LBR) and multiple pregnancy rate (MPR) than GQE only (group G). The addition of a PQE increased LBR in patients aged 35 and over and in patients who received over 3 cycles of embryo transfer (ET) (48.1% vs 27.2%, OR:2.56, 95% CI: 1.3–5.03 and 46.6% vs 35.4%, OR:1.6, 95% CI: 1.09–2.35), but not in women under 35 and in women who received less than 3 cycles of ET (48.7% vs 43.9%, OR:1.22, 95% CI: 0.93–1.59 and 48.3% vs 41.4%, OR:1.33, 95% CI: 0.96–1.85). Group GP resulted in significantly higher MPR than group G irrespective of age and the number of previous IVF cycles. Conclusions: An additional PQE does not negatively affect the implantation potential of the co-transferred GQE. Nevertheless, the addition of a PQE contributes to both live birth and multiple birth in poor prognosis patients. Physicians should still balance the benefits and risks of DET. [ABSTRACT FROM AUTHOR]