Literature DB >> 32747217

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

Antonio La Marca1, Martina Capuzzo2, Valeria Donno2, Mario Mignini Renzini3, Cinzia Del Giovane4, Roberto D'Amico2, Sesh Kamal Sunkara5.   

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.
Copyright © 2020 Elsevier Masson SAS. All rights reserved.

Entities:  

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

Year:  2020        PMID: 32747217     DOI: 10.1016/j.jogoh.2020.101878

Source DB:  PubMed          Journal:  J Gynecol Obstet Hum Reprod        ISSN: 2468-7847


  2 in total

1.  Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation.

Authors:  Jack Wilkinson; Andy Vail; Stephen A Roberts
Journal:  Diagn Progn Res       Date:  2021-01-21

2.  Adaptive data-driven models to best predict the likelihood of live birth as the IVF cycle moves on and for each embryo transfer.

Authors:  Véronika Grzegorczyk-Martin; Julie Roset; Pierre Di Pizio; Thomas Fréour; Paul Barrière; Jean Luc Pouly; Michael Grynberg; Isabelle Parneix; Catherine Avril; Joe Pacheco; Tomasz M Grzegorczyk
Journal:  J Assist Reprod Genet       Date:  2022-06-29       Impact factor: 3.357

  2 in total

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