Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validation

Abstract

Background: The predictive capability of time-lapse monitoring (TLM) selection algorithms is influenced by patient characteristics, type and quality of data included in the analysis and the used statistical methods. Previous studies excluded DET cycles of which only one embryo implanted, introducing bias into the data. Therefore, we wanted to develop a TLM prediction model that is able to predict pregnancy chances after both single- and double embryo transfer (SET and DET).

Methods: This is a retrospective study of couples (n = 1770) undergoing an in vitro fertilization cycle at the Erasmus MC, University Medical Centre Rotterdam (clinic A) or the Reinier de Graaf Hospital (clinic B). This resulted in 2058 transferred embryos with time-lapse and pregnancy outcome information. For each dataset a prediction model was established by using the Embryo-Uterus statistical model with the number of gestational sacs as the outcome variable. This process was followed by cross-validation.

Results: Prediction model A (based on data of clinic A) included female age, t3-t2 and t5-t4, and model B (clinic B) included female age, t2, t3-t2 and t5-t4. Internal validation showed overfitting of model A (calibration slope 0.765 and area under the curve (AUC) 0.60), and minor overfitting of model B (slope 0.915 and AUC 0.65). External validation showed that model A was capable of predicting pregnancy in the dataset of clinic B with an AUC of 0.65 (95% CI: 0.61-0.69; slope 1.223, 95% CI: 0.903-1.561). Model B was less accurate in predicting pregnancy in the dataset of clinic A (AUC 0.60, 95% CI: 0.56-0.65; slope 0.671, 95% CI: 0.422-0.939).

Conclusion: Our study demonstrates a novel approach to the development of a TLM prediction model by applying the EU statistical model. With further development and validation in clinical practice, our prediction model approach can aid in embryo selection and decision making for SET or DET.

Keywords: Embryo transfer; In vitro fertilisation; Prediction model; Statistical models; Time-lapse imaging.

Fig. 1

Prediction of pregnancy chances of model A after single embryo transfer using female age and (a) t3-t2, given that t5-t4 is 13 h, and (b) t5-t4 given that t3-t2 is 11 h. The different coloured lines depict different female ages

Fig. 2

Prediction of pregnancy chances of model B after single embryo transfer using female age and (a) t2, given that t3-t2 is 11 h and t5-t4 is 13 h; b t3-t2 given that t2 is 25 h and t5-t4 is 13 h and (c) t5-t4 given that t2 is 25 h and t3-t2 is 11 h. The different coloured lines depict different female ages

Fig. 3

a Predicted probabilities by model B are plotted against the actual probability in the dataset of clinic A (solid black lines). b Predicted probabilities by model A are plotted against the actual probability in the dataset of clinic B (solid black line). The grey lines represent perfect calibration

Fig. 4

Illustration of the predicted probability of pregnancy after transfer of embryos originating from 10 patients of clinic A, where a double embryo transfer (DET) was performed. Patients were selected according to at least a 30% pregnancy chance predicted by our model A (according to morphokinetic parameters and female age), of both embryos. The light blue and dark blue bars represent the individual predicted probability of pregnancy after single embryo transfer (SET) for the first and second embryo. The white dots indicate the predicted probability of a twin pregnancy after transfer of both embryos originating from one patient; the black dots indicate the predicted probability of a singleton pregnancy after DET. Abbreviations: DET, double embryo transfer; SET, single embryo transfer