Assessing efficacy of day 3 embryo time-lapse algorithms retrospectively: Impacts of dataset type and confounding factors
Taylor and Francis
School of Medical and Health Sciences
This study investigated the efficacy of four published day 3 embryo time-lapse algorithms based on different types of datasets (known implantation data [KID] and single embryo transfer [SET]), and the confounding effect of female age and conventional embryo morphology. Four algorithms were retrospectively applied to three types of datasets generated at Fertility North between February 2013 and December 2014: (a) KID dataset (n = 270), (b) a subset of SET (n = 144, end-point = implantation), and (c) SET (n = 144, end-point = live birth), respectively. All four algorithms showed progressively reduced predictive power (expressed as area under the receiver operating characteristics curve and 95% confidence interval [CI]) after application to the three datasets (a–c): Liu (0.762 [0.701–0.824] vs. 0.724 [0.641–0.807] vs. 0.707 [0.620–0.793]), KIDScore (0.614 [0.539–0.688] vs. 0.548 [0.451–0.645] vs. 0.536 [0.434–0.637]), Meseguer (0.585 [0.508–0.663] vs. 0.56 [0.462–0.658] vs. 0.549 [0.445–0.652]), and Basile (0.582 [0.505–0.659] vs. 0.519 [0.421–0.618] vs. 0.509 [0.406–0.612]). Furthermore, using KID dataset, the association (expressed as odds ratio and 95% CI) between time-lapse algorithms and implantation outcomes lost statistical significance after adjusting for conventional embryo morphology and female age in 3 of the 4 algorithms (KIDScore 1.832 [1.118–3.004] vs. 1.063 [0.659–1.715], Meseguer 1.150 [1.021–1.295] vs. 1.122 [0.981–1.284] and Basile 1.122 [1.008–1.249] vs. 1.038 [0.919–1.172]). In conclusion, SET is a preferred dataset to KID when developing or validating time-lapse algorithms, and day 3 conventional embryo morphology and female age should be considered as confounding factors.
Multidisciplinary biological approaches to personalised disease diagnosis, prognosis and management