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The Current State of IVF Success Models--Are They Predictive?

The Steps Towards Developing Reliable Predictive Models

June 15, 2020
Teresa Dietrich-O'Donnell

Understanding the underlying factors that lead to IVF failure is essential for providing the best possible counseling for those looking to conceive. A new model formulated in a Rutger’s-led study represents a new effort to improve the field of fertility research.

IVF addresses conception difficulties resulting from poor egg quality, genetic disease, and many more issues. But in order to predict the success of IVF, we need to address the factors contributing to continued difficulties in carrying out a viable pregnancy.

Nearly 20% of clinically recognized pregnancies result in a miscarriage, but research addressing the underlying causes of early-term miscarriages is shockingly scarce. New efforts at modeling IVF success rates have begun to develop a powerful and useful tool for couples looking to conceive that will potentially guide clinicians in their efforts to reduce the chance of miscarriage during IVF.

Understanding Pregnancy and IVF Failures

Modeling IVF Success Rates Using New Research

Aneuploidy, a condition in which eggs contain the wrong number of chromosomes, is one of the primary causes of early miscarriages. It’s also the leading cause of IVF failure. Because the risk of chromosomal disorders is dependent on errors in cell division (meiosis) that increase as a woman ages, age is one of the most important components of models predicting IVF success.

Despite the known association between aneuploidy and IVf failure, until very recently there was a significant lack of research dedicated to understanding exactly when and how aneuploidy occurs. Researchers used prior scientific studies connecting aneuploidy and meiosis to develop a new study to address this lack of information.

A graph showing clinical pregnancy rate per embryo transfer
Age represents a significant factor in determining IVF success. Image Courtesy of Coastal Fertility Specialists.

Data collected during the IVF cycle at multiple points was then incorporated into a model of factors contributing to success at each stage of the IVF cycle. At each stage, the probability of successful pregnancy changes depending on the various factors that contribute to aneuploidy at each step.

While creating a mathematical model of aneuploidy caused by errors in cell division, researchers discovered connections between IVF failure and meiosis errors occurring at specific times during the division cycle.

Incorporating the underlying mechanisms driving aneuploidy into a mathematical model enabled researchers to better predict when and how IVF failure would occur. This in turn led to a more precise model of IVF success rates.

Practical Uses for the Modeling Framework

What We Learn From IVF Success Rates

A woman talking to a couple on a couch
Fertility specialists seek to provide the best possible counsel for their patients and rely on cutting-edge models and technology to do so.

Couples seeking counsel regarding IVF and conception depend on their clinicians being able to provide accurate and useful information regarding the possibility of success versus failure. Developing accurate prediction models is the first step in providing reliable and accurate counsel.

Models predicting IVF results are useful for clinicians developing guidelines that will influence the number of eggs collected during an IVF cycle. For those with a greater risk of aneuploidy, clinicians will use these guidelines to decide how many eggs must be collected in order to ensure at least one with no chromosomal abnormalities.

The success of IVF modeling depends on developing a deeper understanding of aneuploidy and its diagnosis. Thankfully, there are researchers pushing to expand our knowledge of aneuploidy and working to contribute to prediction models that will eventually help clinicians assess the possibility of IVF success.

Moving forward, models could be made more comprehensive by addressing male infertility and errors resulting from dysfunctional sperm.


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