Construction and validation of a prediction model to minimize twin rates at preserved high live birth rates after IVF

Vaegter, Katarina Kebbon; Berglund, Lars; Tilly, Johanna; Hadziosmanovic, Nermin; Brodin, Thomas; Holte, Jan

doi:10.1016/j.rbmo.2018.09.020

Cited by 9 publications

(11 citation statements)

References 38 publications

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“…Vaegter et al [16] also constructed a predictive model for an embryo transfer strategy, in which only one embryo was transferred if the predictive risk of twin implantation was above 15%, using the two highest scored embryos. While validating this predictive model, the actual twin rate was 3.8%, which was far lower than the expected 15% setting, accompanied by a decreased live birth rate.…”

Section: Discussionmentioning

confidence: 99%

“…This principle was previously discussed in an embryo-uterus modeling framework [32][33][34]. A higher twin rate than expected would be achieved if the implantation chances of the embryos transferred together were completely independent of one another [16]. Considering the variability of confounding factors, a two-level algorithm was introduced in our model for DET prediction to achieve a satisfying predictive model.…”

Section: Discussionmentioning

confidence: 99%

“…To minimize twin risk, some researchers also constructed a predictable model and validated it in subsequent IVF cycles, and the results showed a signi cantly reduced twin rate (from 25.2-3.8%) [16].…”

Section: Discussionmentioning

confidence: 99%

“…Previous research to establish similar predictive models were problematic due to the limited number of involved features or dramatic distinction between the predicted value and the actual situation [16,29].…”

Section: Discussionmentioning

confidence: 99%

“…According to previous studies, the outcome of DET is not a simple binomial distribution with respect to two transferred embryos [16]. Therefore, a hierarchical model with two levels was established to learn single embryo implantation potential and the impact of double embryos transferred simultaneously.…”

Section: Model Construction Feature Selection and Validationmentioning

confidence: 99%

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Individualized Embryo Selection Strategy Developed by Stacking Machine Learning Model for Better In Vitro Fertilization Outcomes: An Application Study.

Yang

Wang

et al. 2021

Preprint

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Background: To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing. Methods: This was an application study including 7887 patients with 8585 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes.Results: For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1+P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1×P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree. Conclusion: Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

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Section: Discussionmentioning

confidence: 99%