Clinical Research Directory

Machine-Learning Prediction of Pregnancy Outcomes After Assisted Reproductive Treatments

Sponsor: BetaPlus Center for Reproductive Medicine

Summary

The goal of this observational study is to develop and validate a machine-learning model to predict pregnancy outcomes in women who have a positive pregnancy test following assisted reproductive treatment (ART), with the primary analysis focusing on in vitro fertilization (IVF) and frozen embryo transfer (FET). The study will also include an exploratory analysis of pregnancies following intrauterine insemination (IUI). The main questions it aims to answer are: Can early blood levels of beta-human chorionic gonadotropin (beta-hCG) predict the likelihood of live birth in an IVF or FET cycle? Can early beta-hCG levels help identify pregnancies at increased risk of biochemical pregnancy or early pregnancy loss? Participants will have clinical and treatment information, including beta-hCG measurements and pregnancy outcomes, collected from medical records or during prospective follow-up. No changes to participants' medical treatment will be made as part of the study.

Official title: Development and Validation of a Machine Learning Prognostic Model for Early Prediction of Pregnancy Outcomes Following Assisted Reproductive Treatment: A Retrospective and Prospective Cohort Study

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