The predicted probability of live birth in In Vitro Fertilization varies during important stages throughout the treatment: analysis of 114,882 first cycles - 01/08/20

Doi : 10.1016/j.jogoh.2020.101878

Antonio La Marca ^a,^b,⁎ , Martina Capuzzo ^a, Valeria Donno ^a, Mario Mignini Renzini ^b,^c, Cinzia Del Giovane ^d, Roberto D’Amico ^a, Sesh Kamal Sunkara ^f
^a Department of Medical and Surgical Sciences of the Mother, Children and Adults, University of Modena and Reggio Emilia, Policlinico, Via del Pozzo 71, 41124 Modena, Italy
^b Clinica EUGIN, Via Nobili 188/F, 41126, Modena, Italy
^c Biogenesi, Reproductive Medicine Centre, Monza, Italy
^d Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
^f Department of Women’s Health, Faculty of Life Sciences and Medicine, King’s College London, Strand Campus, Strand, London, UK

^⁎Corresponding author at: Department of Medical and Surgical Sciences of the Mother, Children and Adults, University of Modena and Reggio Emilia, Policlinico, Via del Pozzo 71, 41124, Modena, Italy.Department of Medical and Surgical Sciences of the Mother, Children and AdultsUniversity of Modena and Reggio EmiliaPoliclinico, Via del Pozzo 71Modena41124Italy

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Abstract

Research Question

How much the variability in patients’ response during in vitro fertilization (IVF) may add to the initial predicted prognosis based only on patients’ basal characteristics?

Design

Anonymous data were obtained from the Human Fertilization and Embryology Authority (HFEA). Data involving 114,882 stimulated fresh IVF cycles were retrospectively analyzed. Logistic regression was used to develop the models.

Results

Prediction of live birth was feasible with moderate accuracy in all of the three models; discrimination of the model based only on basal patients’ characteristics (AUROC 0.61) was markedly improved adding information of number of embryos (AUROC 0.65) and, mostly, number of oocytes (AUROC 0.66).

Conclusions

The addition to prediction models of parameters such as the number of embryos obtained and especially the number of oocytes retrieved can statistically significantly improve the overall prediction of live birth probabilities when based on only basal patients’ characteristics. This seems to be particularly true for women after the first IVF cycle. Since ovarian response affects the probability of live birth in IVF, it is highly recommended to add markers of ovarian response to models based on basal characteristics to increase their predictive ability.

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Keywords : prediction model, live birth, IVF, oocytes retrieved, embryos