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

项目总结/摘要 体外受精（IVF）治疗变得越来越普遍， 在过去的十年里取得了成功。额外冷冻胚胎（夫妇没有的胚胎） 立即使用，但继续存储）造成情感和财务困境， 患者额外的胚胎也给诊所带来了后勤和财务上的困境。 限制储存中额外胚胎数量的一种策略是限制 胚胎是第一个被创造出来的。然而，目前还没有经过验证的方法来 确定在体外受精治疗过程中应该受精多少个卵子， 足够的胚胎形成，有所需数量的孩子， 减少额外胚胎的数量。 我们的目标是开发一种预测工具，以帮助临床医生和患者决定 IVF期间需要受精多少个卵子在具体目标1中，我们将开发一个 算法使用国家辅助生殖协会的现有数据 技术临床结果报告系统（SART CORS）数据库。我们提出 开发两个独立的模型，一个是预测需要移植的胚胎数量， 转移到生产一个活产和第二个预测受精的比例 能产生可移植胚胎的卵子。两种型号都将考虑患者 人口统计学和检索前周期特征作为可能的预测因素。最终 算法将涉及两个预测的比率的函数。在具体目标2中， 将调查IVF患者和提供者，以评估他们对利用IVF的兴趣和观点。 预测工具。调查答复将有助于阐明可能存在的利用障碍， 告知我们如何最好地教育患者和提供者关于 预测工具这一建议非常重要，因为它有可能 改变试管婴儿的方式，从而限制受精卵的数量 创造更少的胚胎。少生几个胚胎会减少你的情感，经济， 以及额外的胚胎给患者和供应商带来的后勤费用。与成功 完成拟议的目标，我们将有一个工具，可以帮助促进更安全， 为IVF患者提供更有效、更负责任的护理。

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.