Factors Affecting Embryo Aneuploidy

Presented at: ASRM Scientific Congress, 2021

Authors: Bruce Shapiro, Leah Kaye, Melody Rasouli, Ankita Raman, Forest Garner, Carrie Bedient

Materials and Methods:

In this prospective observational study, patients underwent routine ovarian stimulation with exogenous gonadotropins. During oocyte collection, each punctured follicle was sonographically measured. Embryos were group-cultured to the blastocyst stage according to follicle diameter. Trophectoderm biopsies were followed by pre-implantation genetic testing for aneuploidy (PGT-A). Multiple logistic regression was used to determine if patient age, the ovulatory trigger agent (agonist, agonist+hCG, or hCG alone), follicle diameter at retrieval, the total number of eggs collected, and the day of blastocyst formation (5, 6, or 7) correlated with abnormal PGT-A results. P<0.05 was considered significant.

Results:

A total of 344 blastocysts were biopsied in the study period. Patient age at retrieval ranged from 21.0 to 43.6 years. A logistic regression model was developed predicting abnormal PGT-A results. The only significant predictor of an abnormal (aneuploid) test result in this model was patient age at retrieval (P=0.0378). Increasing patient age corresponded with increased risk of an abnormal PGT-A result. The ovulatory trigger agent, follicle diameter at retrieval, the total number of eggs collected, and the day of blastocyst formation were not significantly correlated with embryo ploidy.

Conclusions:

Only patient age significantly correlated with embryo ploidy. The ovulatory trigger agent, follicle diameter at retrieval, the total number of eggs collected, and the day of blastocyst formation were not significantly correlated with embryo ploidy.

Impact Statement:

While this study was not large enough to rule out weak correlations, it was reassuring to find the trigger agent, follicle size, cohort size, and day of blastocyst formation were not significantly correlated with embryo ploidy among these 344 biopsied blastocysts.

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Factors Affecting Embryo Developmental Pace

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Identifying Potential Sources of Bias in Deep Learning Models for Embryo Assessment