Predicting IVF live -birth probability using time-lapse data: Implications of including or excluding age in a day 2 embryo transfer model

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 impact of incorporating age into the model. The” Base Model, not incorporating age, achieved an AUC of 0.641, while the “Age Model”, using maternal age, significantly enhanced AUC to 0.745, as estimated through bootstrap analysis. However, when the Age Model was subjected to average ages across three respective age intervals, the AUC values were comparable to the Base Model, rather than the original Age Model scores. Adjusting the Intracytoplasmic Sperm Injection (ICSI) timing by ± 2 hours, purely as a theoretical exercise, has minimal impacts on model predictions. This highlights the value of including t2 despite fertilisation timing variations between ICSI and IVF. The Age Model did not show superiority in predicting live birth within single treatment cohorts. However, given its distinct AUC values for broader age ranges, the Age Model can serve as a counselling tool on live-birth probabilities. With further validation, we suggest only using the Age Model for general counselling, while the Base Model is preferable for the embryo selection decision support.