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Related Experiment Video

Updated: Jul 11, 2025

Methods for Studying Uterine Contributions to Pregnancy Establishment in an Ovariectomized Mouse Model
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Predicting the ovarian response: towards a determinant model and implications for practice.

Philippe Arvis1, Catherine Rongières2, Olivier Pirrello2

  • 1Department of Obstetrics and Gynecology, Clinique La Sagesse, Rennes, France. dr-arvis@wanadoo.fr.

Journal of Assisted Reproduction and Genetics
|November 3, 2023
PubMed
Summary

Improving prediction models for ovarian response in assisted reproductive technology (ART) is crucial. Previous stimulation response, anti-Müllerian hormone (AMH), and antral follicle count (AFC) are key predictors for oocyte yield and cycle cancellation.

Keywords:
AFCAMHARTOvarian reserve testPrediction models

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Area of Science:

  • Reproductive endocrinology and infertility
  • Biostatistics in clinical research
  • Assisted Reproductive Technology (ART) outcomes

Background:

  • Accurate prediction of ovarian response is vital for optimizing ART success rates.
  • Existing models often lack the precision needed to guide clinical decisions effectively.
  • Identifying reliable predictors can improve patient counseling and treatment protocols.

Purpose of the Study:

  • To enhance the reliability of prediction models for ovarian response in ART.
  • To identify the most significant predictors of oocyte yield and cycle cancellation.
  • To develop a more accurate model for forecasting ART outcomes.

Main Methods:

  • A multicenter retrospective cohort study involving 25,854 controlled ovarian stimulations across twelve reproductive centers.
  • Utilized a zero-inflated binomial negative model to analyze predictors of cycle cancellation and oocyte retrieval.
  • Evaluated the non-linear effects of anti-Müllerian hormone (AMH) and antral follicle count (AFC), adjusting for age, BMI, and center.

Main Results:

  • Previous ovarian stimulation response was the strongest predictor, followed by AMH and AFC, which have non-linear effects.
  • The developed model achieved high determination coefficients (R²=0.505 for non-naïve women) and demonstrated significant inter-center variability.
  • Predictors for cycle cancellation and oocyte number differed, highlighting the importance of separate modeling.

Conclusions:

  • Substantial improvement in ovarian response prediction is achievable by modeling cancellation decisions and oocyte retrieval.
  • The model incorporates previous stimulation history and non-linear effects of AMH and AFC for enhanced accuracy.
  • This approach offers a more precise tool for predicting ART outcomes and guiding clinical management.