Department of Obstetrics, Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, 1176 5th Ave, New York, NY, 10029, USA.
Reproductive Medicine Associates of New York, 635 Madison Ave, 9th Floor, New York, NY, 10022, USA.
J Assist Reprod Genet. 2021 Jul;38(7):1647-1653. doi: 10.1007/s10815-021-02203-3. Epub 2021 May 1.
To assess whether utilization of a mathematical ranking algorithm for assistance with embryo selection improves clinical outcomes compared with traditional embryo selection via morphologic grading in single vitrified warmed euploid embryo transfers (euploid SETs).
A retrospective cohort study in a single, academic center from September 2016 to February 2020 was performed. A total of 4320 euploid SETs met inclusion criteria and were included in the study. Controls included all euploid SETs in which embryo selection was performed by a senior embryologist based on modified Gardner grading (traditional approach). Cases included euploid SETs in which embryo selection was performed using an automated algorithm-based approach (algorithm-based approach). Our primary outcome was implantation rate. Secondary outcomes included ongoing pregnancy/live birth rate and clinical loss rate.
The implantation rate and ongoing pregnancy/live birth rate were significantly higher when using the algorithm-based approach compared with the traditional approach (65.3% vs 57.8%, p<0.0001 and 54.7% vs 48.1%, p=0.0001, respectively). After adjusting for potential confounding variables, utilization of the algorithm remained significantly associated with improved odds of implantation (aOR 1.51, 95% CI 1.04, 2.18, p=0.03) ongoing pregnancy/live birth (aOR 1.99, 95% CI 1.38, 2.86, p=0.0002), and decreased odds of clinical loss (aOR 0.42, 95% CI 0.21, 0.84, p=0.01).
Clinical implementation of an automated mathematical algorithm for embryo ranking and selection is significantly associated with improved implantation and ongoing pregnancy/live birth as compared with traditional embryo selection in euploid SETs.
评估在单玻璃化冷冻解冻整倍体胚胎移植（整倍体 SET）中，与传统的形态学评分胚胎选择相比，使用数学排名算法辅助胚胎选择是否能改善临床结局。
这是一项 2016 年 9 月至 2020 年 2 月在单家学术中心进行的回顾性队列研究。共有 4320 个整倍体 SET 符合纳入标准并纳入研究。对照组包括所有由高级胚胎学家根据改良 Gardner 分级（传统方法）进行胚胎选择的整倍体 SET。病例组包括使用基于自动算法的方法（基于算法的方法）进行胚胎选择的整倍体 SET。我们的主要结局是着床率。次要结局包括持续妊娠/活产率和临床流产率。
与传统方法相比，使用基于算法的方法时着床率和持续妊娠/活产率显著提高（65.3%比 57.8%，p<0.0001 和 54.7%比 48.1%，p=0.0001）。调整潜在混杂变量后，使用算法与提高着床的几率显著相关（优势比 1.51，95%置信区间 1.04-2.18，p=0.03）、持续妊娠/活产（优势比 1.99，95%置信区间 1.38-2.86，p=0.0002），并降低临床流产的几率（优势比 0.42，95%置信区间 0.21-0.84，p=0.01）。
与传统的胚胎选择相比，在整倍体 SET 中，胚胎分级和选择的自动化数学算法的临床应用与着床率和持续妊娠/活产率的提高显著相关。
