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Predicting the ovarian response: towards a determinant model and implications for practice

  • Assisted Reproduction Technologies
  • Published:
Journal of Assisted Reproduction and Genetics Aims and scope Submit manuscript

Abstract

Objective

To improve the reliability of prediction models for ovarian response to stimulation in ART.

Design

A multicenter retrospective cohort study.

Setting

Twelve reproductive centers.

Patients

A total of 25,854 controlled ovarian stimulations between 2005 and 2016, including cycles cancelled for inadequate response, were included.

Intervention(s)

None.

Main outcome measure(s)

Precision of the prediction of the number of oocytes at ovarian pickup and of cancellation rate for poor ovarian response.

Results

Both AMH and antral follicle count exhibit a non-linear effect on the oocyte yield, with a linear relationship after log-transformation. After adjustment for age, BMI, and center, ovarian response observed in a previous stimulation was found to be the best predictor, followed by AMH and AFC. The zero-inflated binomial negative model showed that predictors of cycle cancellation and number of oocytes at retrieval were different, and assimilating cancellation to zero oocyte greatly reduces the determination of the model. Our model was characterized by the best ever reached determination (R2=0.505 for non-naïve women, 0.313 for all the women) and provided evidence of a very strong difference among centers. The results can be easily converted in the prediction of response levels (poor-medium-good-high). Finally, in case of partial report of the above predictors, we show that the univariate prediction based on the best predictor provides a good approximation.

Conclusion(s)

A substantial improvement of the ovarian response prediction is possible in modelling the possible cancellation decision, followed by the oocyte retrieval itself, according to an appropriate model based on previous stimulation and non-linear effects of AMH and AFC.

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Data availability

Raw data and statistical analysis will be made available to the editors of the journal for review or query upon request.

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Author information

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, Clinique La Sagesse, Rennes, France

    Philippe Arvis

  2. Department de Medecine de La Reproduction, Centre Medico-Chirurgical Et Obstetrical (CMCO), 19 rue Louis Pasteur, 67300 Schiltigheim, Strasbourg, France

    Catherine Rongières & Olivier Pirrello

  3. Faculty of Economics (P.L.), UCL Mons, Louvain, Belgium

    Philippe Lehert

  4. Faculty of Medicine (P.L.), The University of Melbourne, Melbourne, Australia

    Philippe Lehert

Authors
  1. Philippe Arvis
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  2. Catherine Rongières
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  4. Philippe Lehert
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Corresponding author

Correspondence to Philippe Arvis.

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Arvis, P., Rongières, C., Pirrello, O. et al. Predicting the ovarian response: towards a determinant model and implications for practice. J Assist Reprod Genet 41, 213–222 (2024). https://doi.org/10.1007/s10815-023-02975-w

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  • DOI: https://doi.org/10.1007/s10815-023-02975-w

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