O-004 The bias is out of the bag: IVF culture dish well number influences embryo selection decision-making and implantation outcome
Authors: Seidman D , Maor R , Shapiro M , Howles C M , Meseguer M , Gilboa D 
Published: June 2022

Study question

Is there a selection bias against embryos placed in higher-numbered wells inside a multi-well IVF culture dish. Does this selection bias alone impact implantation outcomes?

Summary answer

Top-quality embryos present in higher-numbered wells are statistically less likely to be selected for transfer, independent of any differences in quality or development between wells.

What is known already

Substantial intra-and inter-observer variability in embryo selection, as well as differences in quality assessment and laboratory environment, have been shown to affect IVF success. Currently many clinics have adopted stringent guidelines to control for human errors and workflow variation. Still, the impact of errors in laboratory and medical procedures was reported as high as 12%. This is particularly relevant for the IVF lab, where high workload and stress influence rate of errors and patient outcome. This groundbreaking study emphasizes how cognitive tendencies are inherent to the embryo selection process.

Study design, size, duration

This study used a retrospective dataset from three highly experienced fertility clinics (1 US and 2 European clinics). A total of 4,275 Fresh IVF cycles were analyzed. For each treatment cycle, embryo quality grades, corresponding embryo well numbers, day 5 selection and implantation outcomes were documented. All cycles were performed using the EmbryoSlide 12-well culture dish and a time-lapse system. All three datasets were analyzed separately and also combined.

Participants/materials, setting, methods

For each dataset, three analyses were conducted: (1) total number of selected embryos were calculated for each corresponding well number; (II) the proportion of implanted embryos, relative to the total number of selected embryos, were quantified to calculate the “success rate” for each well number; (II) the distribution of top-quality embryos between wells were quantified and compared. Results were normalized by total number of transferred embryos and IVF implantation success rates reported for each clinic.

Main results and the role of chance

A negative trend was found between well number, ranging from 1-12, and number of embryos selected for transfer. This trend was significant (p &lt; 0.05) and occurred independently in each dataset. Odds ratios (OR) for the relation between selecting embryos for transfer from wells 1-5, and from 8-12 = Clinic A: 2.16, Clinic B: 1.78, Clinic C: 2.45. Alternative hypotheses were tested: (1) top-quality embryos are clustered in lower-numbered wells during culture; (2) enhanced embryo quality and conditions are found in lower-numbered wells, which should manifest in higher rates of implantation. Results for each clinic showed a statistically even distribution of top-quality embryos between wells (within 2 standard deviations from the mean; not significant), yet ‘success rate’ for transferred embryos increased by well number (by 12-30% between wells 1-5 and wells 8-12; OR = 1.19, 1.06, 1.08 for Clinic A, B, and C, respectively). An inverse trend existed between an embryo’s likelihood of being selected for transfer, and its likelihood of implanting. We conclude that embryologists may tend to select the first acceptable embryo for transfer. Embryos from higher-numbered wells were significantly more likely to implant, since they overcame this bias when equitably evaluated and selected for transfer.

Limitations, reasons for caution

Though our findings were significant, they need to be repeated on larger datasets with more inter-centre variation, and key embryo culture and outcome variables recorded.

Wider implications of the findings

This study emphasizes the inherent human error that exists inside IVF clinics. Machine learning systems that reduce human bias and increase objective standardization, even if they are not inherently better than embryologists, would improve implantation rates. Future studies should be directed toward AI based technologies that can accomplish this.

Trial registration number

Not Applicable