Prognostic value of oocyte quality in assisted reproductive technology outcomes: a systematic review

Review Article

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VOLUME 2, ISSUE 2, P120-139, APRIL 01, 2021

Authors:

Nicole M. Fischer, M.P.H., Ha Vi Nguyen, M.D., Bhuchitra Singh, M.D., M.P.H., M.H.S., Valerie L. Baker, M.D., James H. Segars, M.D. 

Abstract:

Objective

To survey and assess modern methodologies used to test oocyte quality that have prognostic value in predicting assisted reproductive technology outcomes


Evidence Review

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we surveyed the English-language literature between January 1, 2010, and December 31, 2019, using PubMed, Scopus, and Embase databases. Two reviewers screened for articles focusing on oocyte quality markers that predict assisted reproductive technology outcomes, including embryo quality as well as fertilization, implantation, pregnancy, continued pregnancy, and live birth rates. Articles that did not mention oocytes or those that focused on nonhuman subjects, oocyte aging, oocyte maturation, embryo quality, interventions, or specific clinical diagnoses (endometriosis and polycystic ovarian syndrome) were deemed outside the scope of this analysis and excluded.


Results

Twenty-six relevant articles were identified, including 19 prospective and 7 retrospective studies (n = 2,210 patients). We identified 3 general approaches for oocyte quality assessment: morphological evaluation (11 articles), genomics and proteomics (13 articles), and artificial intelligence (2 articles). Morphological assessment did not show a consistent pattern of predictive value of predicting in vitro fertilization outcomes (7 articles in favor of its predictive value, 4 against). A considerable proportion of genomic and proteomic articles identified promising biomarkers that may predict pregnancy and live birth (12 in favor, 1 against). Machine learning is a rapidly growing frontier that minimizes subjectivity while potentially improving predictive ability (2 in favor).


Conclusion

Although there remains a lack of consensus on optimal methods to predict reproductive success, machine learning and genomics demonstrate promise in improving the understanding of oocyte quality assessment and prognostication.

Fertility and Sterility

Editorial Office, American Society for Reproductive Medicine

Fertility and Sterility® is an international journal for obstetricians, gynecologists, reproductive endocrinologists, urologists, basic scientists and others who treat and investigate problems of infertility and human reproductive disorders. The journal publishes juried original scientific articles in clinical and laboratory research relevant to reproductive endocrinology, urology, andrology, physiology, immunology, genetics, contraception, and menopause. Fertility and Sterility® encourages and supports meaningful basic and clinical research, and facilitates and promotes excellence in professional education, in the field of reproductive medicine.