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

项目摘要/摘要 体外受精(IVF)治疗已变得越来越普遍， 在过去的十年里取得了成功。额外的冷冻胚胎(夫妇不使用的胚胎 立即使用，但继续存储)造成情感和经济困境 病人。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个策略是限制 胚胎首先被创造出来。然而，目前还没有经过验证的方法来 确定在体外受精治疗期间应受精的卵子数量 形成了足够多的胚胎，以产生所需数量的孩子 最大限度地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做出决定 试管受精期间应该受精多少个卵子？在具体目标1中，我们将制定一项 使用来自全国辅助生殖学会的现有数据的算法 技术临床结果报告系统(SART CORS)数据库。我们建议 为了开发两个独立的模型，一个是预测需要 被转移生产一个活产，第二个预测受精比例 将产生可移植胚胎的卵子。这两种模式都会考虑患者 人口学和检索前周期特征是可能的预测因素。决赛 算法将涉及两个预测的比率的函数。在具体目标2中，我们 将对试管婴儿患者和提供者进行调查，以评估他们对使用 预测工具。调查答复将有助于阐明利用可能存在的障碍， 告诉我们如何最好地教育患者和提供者 预测工具。这项提议非常重要，因为它有可能 改变体外受精的方式，从而限制受精卵的数量 并制造出更少的胚胎。胚胎越少，情感，经济， 以及额外胚胎对患者和提供者造成的后勤费用。随着成功 随着拟议目标的实现，我们将拥有一种工具，有助于促进更安全、 为试管受精患者提供更有效、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.