Predicting IVF live -birth probability using time-lapse data: Implications of including or excluding age in a day 2 embryo transfer model
Shabana Sayed, Bjørn Molt Petersen, Marte Myhre Reigstad, Arne Schwennicke, Jon Wegner Hausken, Ritsa Storeng

TL;DR
This study uses time-lapse data to predict live birth chances after IVF, showing that adding maternal age improves prediction accuracy.
Contribution
The study introduces a model that incorporates maternal age to enhance live-birth prediction from embryo time-lapse data.
Findings
Including maternal age in the model increased AUC from 0.641 to 0.745.
Age-adjusted models perform similarly to base models when averaged across age groups.
The Age Model is suggested for counselling, while the Base Model is better for embryo selection.
Abstract
The primary objective of this study was to develop predictive models for the likelihood of live births following In Vitro Fertilisation (IVF) treatment, based on a retrospective analysis of time-lapse data from Day 2 embryo transfers at Klinikk Hausken, Norway. This analysis encompassed 1,506 IVF treatment cycles, which included 865 single and 641 double embryo transfer cycles, totalling 2,147 embryos transferred. The model covariates included nucleation error, timing of two-cell stage (t2) and duration between t2 and the three-cell stage (t3). The predictive ability was assessed using Area Under Curve (AUC). Generalised Additive Mixed Models (GAMM) were utilised to address clustering effects from Single Embryo Transfers (SET) and Double Embryo Transfers (DETs), as well as the non-linear effects of female age and t2 timings. A stratification of age and model scores demonstrated the…
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Taxonomy
TopicsReproductive Biology and Fertility · Assisted Reproductive Technology and Twin Pregnancy · Ovarian function and disorders
