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Related Concept Videos

In Vitro Fertilization01:24

In Vitro Fertilization

594
In vitro fertilization (IVF) is a form of assisted reproductive technology where an egg is fertilized with sperm in a controlled laboratory environment before transferring the resulting embryo into the uterus. This process is designed to help individuals and couples experiencing difficulties conceiving.
The IVF process begins with ovarian stimulation, during which reproductive endocrinologists prescribe hormonal medications to stimulate the ovaries to produce multiple eggs instead of the single...
594

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A validated model for predicting live birth after embryo transfer.

Michael S Awadalla1, Kristin A Bendikson2, Jacqueline R Ho2

  • 1Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Keck School of Medicine, University of Southern California, LAC+USC Medical Center, 2020 Zonal Avenue, IRD Room 533, Los Angeles, CA, 90033, USA. michael.awadalla@med.usc.edu.

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|May 25, 2021
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Summary
This summary is machine-generated.

Predicting live birth and multiple gestation rates is crucial for in vitro fertilization. A new model uses patient age, embryo stage, and cycle type to accurately forecast outcomes, aiding safe embryo transfer decisions.

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Area of Science:

  • Reproductive Medicine
  • Biostatistics
  • In Vitro Fertilization

Background:

  • Accurate prediction of live birth and multiple gestation is vital for safe embryo transfer protocols in in vitro fertilization (IVF).
  • Current models may not fully account for all relevant factors influencing IVF success rates.
  • Optimizing embryo transfer numbers minimizes risks associated with multiple pregnancies.

Purpose of the Study:

  • To develop and validate a quantitative model for predicting singleton, twin, and total live birth rates following human embryo transfer.
  • To provide a tool for individual clinics to analyze their data and guide embryo transfer decisions.
  • To improve the safety and efficacy of IVF by reducing multiple gestations.

Main Methods:

  • Development of a predictive model incorporating patient age, embryo stage (cleavage or blastocyst), and transfer cycle type (fresh or frozen).
  • Inclusion of uterine/universal factors in the model.
  • Validation using a tenfold cross-validation study with data from a single clinic to compare predicted and observed singleton and twin delivery rates.

Main Results:

  • The model demonstrated normally distributed standardized errors for singleton and twin delivery rates, with mean errors not significantly different from zero (p > 0.05).
  • Live birth rates per embryo varied significantly, ranging from 43% for fresh blastocysts in 35-year-olds to 1% for frozen cleavage stage embryos in 43-year-olds.
  • The model accurately predicted observed singleton and twin delivery rates.

Conclusions:

  • The developed quantitative model accurately predicts live birth and multiple gestation probabilities after embryo transfer.
  • This model can be utilized by clinics to personalize embryo transfer strategies, thereby minimizing the risk of multiple pregnancies.
  • The findings support data-driven decision-making in IVF to enhance patient outcomes and safety.