Related Experiment Video
Updated: Jul 4, 2026
12:31
In Vivo Modeling of the Morbid Human Genome using Danio rerio
Published on: August 24, 2013
Defining human embryo phenotypes by cohort-specific prognostic factors
Sunny H Jun1, Bokyung Choi, Lora Shahine
1Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, California, United States of America.
Plos One
|July 4, 2008
Summary
Defining human embryo phenotypes using cohort-specific variables improves in vitro fertilization (IVF) success prediction. Analyzing sibling embryo groups offers better developmental competence insights than individual embryo assessment for IVF outcomes.
Area of Science:
- Reproductive Medicine
- Embryology
- In Vitro Fertilization (IVF)
Background:
- High failure rates in human embryo development during in vitro fertilization (IVF).
- Lack of formal definition for human embryo phenotypes.
- Current embryo transfer criteria focus on individual embryo characteristics.
Purpose of the Study:
- To define human embryo phenotypes using cohort-specific variables.
- To determine if sibling embryo group characteristics predict developmental competence and IVF outcomes.
Main Methods:
- Analysis of 1117 IVF cycles and 4144 embryos from Stanford University Medical Center.
- Utilized regression tree models to analyze 30 variables against pregnancy outcomes.
- Identified key embryo cohort-specific variables predicting IVF success.
Main Results:
- IVF cycle outcomes were predicted with ~70% accuracy by four non-redundant, cohort-specific variables.
- Predictive variables included total embryo number, 8-cell embryo count, cleavage arrest rate, and day 3 follicle-stimulating hormone (FSH) level.
- Cleavage arrest rate was the only independent predictive variable.
Conclusions:
- Human embryo phenotypes should be defined by prognostic variables specific to sibling embryo cohorts.
- Cohort-specific variables are more informative for predicting developmental competence than individual embryo measures.
- This approach enhances the understanding and prediction of IVF cycle outcomes.
