A prediction algorithm for optimal number of oocytes to fertilize during in vitro fertilization treatment

体外受精治疗期间最佳受精卵母细胞数量的预测算法

• 批准号: 10369687
• 负责人: Katharine Fischer Berry Correia
• 金额: $ 6.56万
• 依托单位: AMHERST COLLEGE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-15 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10369687
• 关键词: Address; Algorithms; Assisted Reproductive Technology; Caring; Centers for Disease Control and Prevention (U.S.); Characteristics; Child; Clinic; Clinical; Contraceptive methods; Couples; Cryopreservation; Data; Database Management Systems; Detection; Effectiveness; Embryo; Embryo Transfer; Emotional; Ethics; Fertility; Fertilization in Vitro; Future; Goals; Gynecologic; Infertility; Intention; Lead; Live Birth; Logistics; Methods; Modeling; National Institute of Child Health and Human Development; Oocytes; Outcome; Patient Care; Patients; Prevention; Probability; Procedures; Provider; Public Health; Reporting; Reproductive Health; Research; Research Priority; Retrieval; Societies; Strategic Planning; Surveys; Technology; Time; United States; base; demographics; egg; embryo cryopreservation; embryo quality; fertility preservation; improved; innovation; interest; prediction algorithm; response; social; tool; zygote

Project Summary/Abstract In vitro fertilization (IVF) treatment has become increasingly more common and successful over the last decade. Extra frozen embryos (embryos that couples do not use right away, but continue to store) pose an emotional and financial dilemma to patients. The extra embryos also lead to a logistical and financial dilemma for clinics. One strategy to limit the number of extra embryos in storage is to limit the number of embryos created in the first place. However, there currently is no validated method to determine how many eggs should be fertilized during in vitro fertilization treatment such that enough embryos are formed to have the desired number of children while minimizing the number of extra embryos. Our objective is to develop a prediction tool to aid clinicians and patients in deciding how many eggs should be fertilized during IVF. In Specific Aim 1, we will develop an algorithm using existing data from the national Society for Assisted Reproductive Technology Clinical Outcome Reporting System (SART CORS) database. We propose to develop two separate models, one to predict the number of embryos that will need to be transferred to yield one live birth and the second to predict the proportion of fertilized eggs that will yield transferrable embryos. Both models will consider patient demographics and pre-retrieval cycle characteristics as possible predictors. The final algorithm will involve a function of the ratio of the two predictions. In Specific Aim 2, we will survey IVF patients and providers to assess their interest and perspective in utilizing the prediction tool. Survey responses will help elucidate possible barriers to utilization, informing us on how best to educate patients and providers on the value of the prediction tool. This proposal is of high importance because it has the potential to change the way IVF is conducted, thus limiting the number of eggs that are fertilized and creating less embryos. Having less embryos will minimize the emotional, financial, and logistical tolls that extra embryos pose to patients and providers. With successful completion of the proposed aims, we will have a tool that can help promote safer, effective, more responsible care for IVF patients.

项目总结/文摘

项目成果

Development of a Model to Estimate the Optimal Number of Oocytes to Attempt to Fertilize During Assisted Reproductive Technology Treatment.

• DOI: 10.1001/jamanetworkopen.2022.49395
• 发表时间: 2023-01-03
• 期刊: JAMA NETWORK OPEN
• 影响因子: 13.8
• 作者: Correia, Katharine F. B.;Missmer, Stacey A.;Weinerman, Rachel;Ginsburg, Elizabeth S.;Rossi, Brooke V.
• 通讯作者: Rossi, Brooke V.