1. Adaptive data-driven models to best predict the likelihood of live birth as the IVF cycle moves on and for each embryo transfer
- Author
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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, Clinique Mathilde [Rouen], Gamétogenèse et Qualité du Gamète - ULR 4308 (GQG), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Université de Lille, Centre hospitalier universitaire de Nantes (CHU Nantes), Team 2 : Cell and gene engineering in tolerance, fertility and regenerative medicine (U1064 Inserm - CR2TI), Centre de Recherche en Transplantation et Immunologie - Center for Research in Transplantation and Translational Immunology (U1064 Inserm - CR2TI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Nantes Université - UFR de Médecine et des Techniques Médicales (Nantes Univ - UFR MEDECINE), Nantes Université - pôle Santé, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Santé, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Nantes Université - UFR de Médecine et des Techniques Médicales (Nantes Univ - UFR MEDECINE), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), CHU Estaing [Clermont-Ferrand], CHU Clermont-Ferrand, Unité de Biologie Fonctionnelle et Adaptative (BFA (UMR_8251 / U1133)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), and Université Paris-Saclay
- Subjects
Pregnancy Rate ,Obstetrics and Gynecology ,Fertilization in Vitro ,General Medicine ,Embryo Transfer ,[SDV.BDLR.RS]Life Sciences [q-bio]/Reproductive Biology/Sexual reproduction ,Predictive models ,Reproductive Medicine ,Pregnancy ,In vitro fertilization ,Genetics ,Humans ,Live birth ,Female ,Birth Rate ,Genetics (clinical) ,Predictive factors ,Retrospective Studies ,Developmental Biology - Abstract
Purpose To dynamically assess the evolution of live birth predictive factors’ impact throughout the in vitro fertilization (IVF) process, for each fresh and subsequent frozen embryo transfers. Methods In this multicentric study, data from 13,574 fresh IVF cycles and 6,770 subsequent frozen embryo transfers were retrospectively analyzed. Fifty-seven descriptive parameters were included and split into four categories: (1) demographic (couple’s baseline characteristics), (2) ovarian stimulation, (3) laboratory data, and (4) embryo transfer (fresh and frozen). All these parameters were used to develop four successive predictive models with the outcome being a live birth event. Results Eight parameters were predictive of live birth in the first step after the first consultation, 9 in the second step after the stimulation, 11 in the third step with laboratory data, and 13 in the 4th step at the transfer stage. The predictive performance of the models increased at each step. Certain parameters remained predictive in all 4 models while others were predictive only in the first models and no longer in the subsequent ones when including new parameters. Moreover, some parameters were predictive in fresh transfers but not in frozen transfers. Conclusion This work evaluates the chances of live birth for each embryo transfer individually and not the cumulative outcome after multiple IVF attempts. The different predictive models allow to determine which parameters should be taken into account or not at each step of an IVF cycle, and especially at the time of each embryo transfer, fresh or frozen.
- Published
- 2022
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