Background Infertility has become a global health issue with the number of couples seeking in vitro fertilization (IVF) worldwide continuing to rise. Some couples remain childless after several IVF cycles. Women undergoing IVF face greater risks and financial burden. A prediction model to predict the live birth chance prior to the first IVF treatment is needed in clinical practice for patients counselling and shaping expectations. Methods Clinical data of 7188 women who underwent their first IVF treatment at the Reproductive Medical Center of Shengjing Hospital of China Medical University during 2014–2018 were retrospectively collected. Machine-learning based models were developed on 70% of the dataset using pre-treatment variables, and prediction performances were evaluated on the remaining 30% using receiver operating characteristic (ROC) analysis and calibration plot. Nested cross-validation was used to make an unbiased estimate of the generalization performance of the machine learning algorithms. Results The XGBoost model achieved an area under the ROC curve of 0.73 on the validation dataset and showed the best calibration compared with other machine learning algorithms. Nested cross-validation resulted in an average accuracy score of 0.70 ± 0.003 for the XGBoost model. Conclusions A prediction model based on XGBoost was developed using age, AMH, BMI, duration of infertility, previous live birth, previous miscarriage, previous abortion and type of infertility as predictors. This study might be a promising step to provide personalized estimates of the cumulative live birth chance of the first complete IVF cycle before treatment.

Purpose This study aimed to explore whether the coronavirus disease (COVID-19) vaccination of both partners in infertile couples, different types of COVID-19 vaccines, and the interval between complete vaccination and oocyte retrieval or embryo transfer (ET) affect the quality of embryos and pregnancy rates in in vitro fertilization (IVF). Methods This was a prospective cohort study, comprising 735 infertile couples conducted between December 6, 2021, and March 31, 2022, in a single university hospital-based IVF center. The patients were divided into different groups according to the vaccination status of both partners in infertile couples, type of vaccine, and interval between complete vaccination and IVF treatment. The embryo quality and pregnancy rates were compared among different groups. Results The results showed that embryo quality and pregnancy rates had no significant differences among different groups. The multivariate regression model showed that the vaccination status of both infertile couples, types of vaccines, and intervals had no significant effects on the clinical pregnancy rate. Conclusions The vaccination status of both partners in infertile couples, different types of vaccines, and time intervals have no effect on embryo quality and pregnancy rates in IVF. This is the first study to compare the vaccination status of both partners in infertile couples and the impact of different vaccine types on pregnancy rates and embryo quality in detail. Our findings provide evidence of vaccine safety for infertile couples wishing to undergo IVF treatment. This evidence is crucial for decision-making by clinicians and policymakers involved in IVF cycles.

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