P-284 Time-lapse embryo deselection: development of an interpretable numerical prediction algorithm based on 10320 embryos
Authors: Chapple J , Chapple V , Bickendorf K , Peirce K , Natalwala J , Liu Y 
Published: June 2023

Study question

Is it possible to build a numerical Day 3 prediction model based on calculated weightings of time-lapse deselection measures?

Summary answer

A numerical Day 3 prediction model was developed incorporating computed weightings for a range of contributing factors, showing satisfactory performance.

What is known already

The transferability issue of time-lapse embryo selection algorithms is gaining wider attention, with growing evidence revealing altered embryo morphokinetics in response to different culture conditions and patients’ profiles. In fact embryo deselection using abnormal cleavage patterns, such as direct cleavage or reverse cleavage, have been shown to produce superior inter-laboratory reproducibility. However data is limited in the literature on the weightings between individual measures for prognosis prediction, where disagreement often exist amongst embryologists. In this study, we aimed to build a mathematical blastocyst prediction model for improved transferability and robustness, by incorporating computed weightings for a range of contributing factors.

Study design, size, duration

Time-lapse annotation data were retrospectively extracted from 10320 Day 3 embryos created at Fertility North between January 2017 and June 2022. A subset of 3432 embryos were excluded due to poor quality according to standard Day 3 criteria. Data training and model development were based on 6019 embryos with subsequent blastulation outcomes up to Day 6. Further validation was conducted using another 969 embryos with known implantation outcomes following single fresh Day 3 transfers.

Participants/materials, setting, methods

Embryos were assessed using both static morphology criteria and time-lapse annotation. Time-lapse deselection parameters included direct cleavage (DC), reverse cleavage (RC) and &lt;4 intercellular contact points at 4-cell stage (&lt;4ICCP). For DC and RC, the number of affected blastomeres was also recorded for the 1st (1-cell), 2nd (2-3-cell), and 3rd cleavage cycles (4-8-cell), respectively. Fivefold cross validation (n = 6019) was used to develop the numerical model, followed by additional validation (n = 969) via receiver operating characteristics (ROC).

Main results and the role of chance

Multivariate logistic regression identified 11 variables that were independently associated with blastulation, including insemination methods (IVF or ICSI, odds ratio or OR = 0.757, 95% confidence interval or CI 0.676-0.847, P &lt; 0.001), maternal age (OR = 0.937, 95% CI 0.926-0.949, P &lt; 0.001), &lt;4ICCP (OR = 0.733, 95% CI 0.599-0.895, P = 0.002), number of DC blastomeres at the 1st (OR = 0.036, 95% CI 0.022-0.058, P &lt; 0.001), 2nd (OR = 0.139, 95% CI 0.109-0.176, P &lt; 0.001) or 3rd cleavage cycle (OR = 0.375, 95% CI 0.270-0.521, P &lt; 0.001), number of RC blastomeres at the 1st (OR = 0.082, 95% CI 0.028-0.238, P &lt; 0.001), 2nd (OR = 0.304, 95% CI 0.231-0.400, P &lt; 0.001), or 3rd cleavage cycle (OR = 0.482, 95% CI 0.411-0.567, P &lt; 0.001), cell number (OR = 0.890, 95% CI 0.849-0.934, P &lt; 0.001) and degree of fragmentation (OR = 0.675, 95% CI 0.598-0.763, P &lt; 0.001) on Day 3. A mathematical model was constructed using coefficients of 11 variables via fivefold cross validation (AUCs ranged from 0.765 to 0.777 in 5 development subsets and 0.757 to 0.782 in 5 testing subsets), giving rise to a blastulation prediction score (range 0-10). Blastulation rates rose (5%, 11%, 28%, 47%, 70% to 84%) along score increments (&lt;4, 4-4.9, 5-5.9, 6-6.9, 7-7.9, 8+, respectively). Further validation of the proposed model using a separate dataset with known implantation outcomes also showed satisfactory performance (AUC = 0.626, 95% CI 0.590-0.662, P &lt; 0.001).

Limitations, reasons for caution

Our model was developed using a clinic-specific dataset so might not generalize to other clinics with different laboratory setups and patient profiles. Prospective validation, ideally using randomized controlled design, is required to validate its effectiveness in clinic practice.

Wider implications of the findings

In comparison to most machine learning or black box-based embryo selection algorithms, the development of our model only engaged mathematical methodologies with complete interpretability. We acknowledge the transferability issue of embryo selection algorithms and would invite external validation of our algorithm by sharing the full details of our mathematical formula.

Trial registration number

N/A