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Updated: Jun 17, 2026

Protocol for Human Blastoids Modeling Blastocyst Development and Implantation

Protocol for Human Blastoids Modeling Blastocyst Development and Implantation

Published on: August 10, 2022

A cycle-based model to predict blastocyst transfer cancellation.

Lionel Dessolle¹, Thomas Fréour, Paul Barrière

¹Service de Biologie et Médecine de la Reproduction, Centre Hospitalier Universitaire de Nantes, 38 Bd Jean Monnet, 44093 Nantes Cedex 1, France. lionel.dessolle@tnn.aphp.fr

Human Reproduction (Oxford, England)

|December 22, 2009

Summary

This summary is machine-generated.

A new model predicts blastocyst transfer cancellation in in vitro fertilization (IVF) cycles. This tool helps reduce cancelled Day 5 transfers by identifying cycles unlikely to produce blastocysts, improving IVF success rates.

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Last Updated: Jun 17, 2026

Protocol for Human Blastoids Modeling Blastocyst Development and Implantation

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Published on: August 10, 2022

Human Blastocyst Biopsy and Vitrification

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Published on: July 26, 2019

Morphometric Protocol for the Objective Assessment of Blastocyst Behavior During Vitrification and Warming Steps

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Published on: February 28, 2019

Area of Science:

Reproductive Medicine
Embryology
In Vitro Fertilization (IVF)

Background:

Extended embryo culture to blastocyst stage aims to improve IVF implantation rates and reduce multiple pregnancies.
A significant challenge is the risk of transfer cancellation due to lack of blastocyst development by Day 5.
Predicting cancellation risk is crucial for managing IVF cycles.

Purpose of the Study:

To develop and validate a predictive model for blastocyst transfer cancellation in IVF.
To identify key factors associated with the absence of blastocyst development by Day 5.
To create a clinical tool for better cycle management.

Main Methods:

A predictive model was developed using multivariable logistic regression on 562 first IVF cycles.
Patient and cycle characteristics were analyzed for association with Day 5 blastocyst presence.
The model was validated on an independent cohort, with performance assessed by discrimination (AUC) and calibration.

Main Results:

Fertilization technique, Day 3 embryo quality/quantity, and female age were significant predictors of blastocyst development.
The model achieved an AUC of 0.75 in the training set and 0.80 in the validation set.
At a 0.55 threshold, the model demonstrated an 80.5% negative predictive value and 17% false positive rate.

Conclusions:

A cycle-based predictive model can be clinically relevant for reducing cancelled Day 5 transfers.
This tool may help optimize IVF treatment planning and resource allocation.
Improved prediction of blastocyst development can enhance patient counseling and cycle management.