Clinical data-based modeling of IVF live birth outcome and its application.

BACKGROUND: The low live birth rate and difficult decision-making of the in vitro fertilization (IVF) treatment regimen bring great trouble to patients and clinicians. Based on the retrospective clinical data of patients undergoing the IVF cycle, this study aims to establish classification models for predicting live birth outcome (LBO) with machine learning methods. METHODS: The historical data of a total of 1405 patients undergoing IVF cycle were first collected and then analyzed by univariate and multivariate analysis. The statistically significant factors were identified and taken as input to build the artificial neural network (ANN) model and supporting vector machine (SVM) model for predicting the LBO. By comparing the model performance, the one with better results was selected as the final prediction model and applied in real clinical applications. RESULTS: Univariate and multivariate analysis shows that 7 factors were closely related to the LBO (with P < 0.05): Age, ovarian sensitivity index (OSI), controlled ovarian stimulation (COS) treatment regimen, Gn starting dose, endometrial thickness on human chorionic gonadotrophin (HCG) day, Progesterone (P) value on HCG day, and embryo transfer strategy. By taking the 7 factors as input, the ANN-based and SVM-based LBO models were established, yielding good prediction performance. Compared with the ANN model, the SVM model performs much better and was selected as the final model for the LBO prediction. In real clinical applications, the proposed ANN-based LBO model can predict the LBO with good performance and recommend the embryo transfer strategy of potential good LBO. CONCLUSIONS: The proposed model involving all essential IVF treatment factors can accurately predict LBO. It can provide objective and scientific assistance to clinicians for customizing the IVF treatment strategy like the embryo transfer strategy.

What is the optimal number of embryos to transfer for POSEIDON group 1 and group 2? A retrospective study.

BACKGROUND: The 2016 Patient-Oriented Strategy Encompassing IndividualizeD Oocyte Number (POSEIDON) criteria redefined the poor responders as low prognosis patients. The embryo transfer strategy for POSEIDON patients remained to be addressed. This study aimed to investigate the optimized number of embryos to transfer for unexpected low-prognosis patients (POSEIDON Group 1 and Group 2) with blastocyst transfer in their first frozen cycle. METHODS: A retrospective cohort study of 2970 patients who underwent frozen-thawed embryo transfer (FET) between January 2018 and December 2021. Patients from POSEIDON Group 1 (N = 219) and Group 2 (N = 135) who underwent blastocyst transfer in their first FET cycles were included and divided into the elective single embryo transfer (eSET) group and the double embryo transfer (DET) group. RESULTS: For POSEIDON Group 1, the live birth rate per embryo transfer of the DET group was slightly higher than the eSET group (52.17% vs 46.15%, OR 0.786, 95% CI 0.462-1.337, P = 0.374; adjusted OR (aOR) 0.622, 95% CI 0.340-1.140, P = 0.124), while a significant increase of 20.00% in the multiple birth rate was shown. For Group 2, higher live birth rates were observed in the DET group compared to the eSET group (38.46% vs 20.48%, OR 0.412, 95% CI 0.190-0.892, P = 0.024; aOR 0.358, 95% CI 0.155-0.828, P = 0.016). The difference in the multiple birth rate was 20.00% without statistical significance. Univariate and multivariate analyses revealed that age (OR 0.759, 95% CI .624-0.922, P = 0.006 and OR 0.751, 95% CI 0.605-0.932, P = 0.009) and the number of transferred embryos (OR 0.412, 95% CI 0.190-0.892, P = 0.024 and OR 0.367, 95% CI 0.161-0.840, P = 0.018) were significant variables for the live birth rate in POSEIDON Group 2. CONCLUSIONS: The findings in the present study showed that eSET was preferred in the first frozen cycle for POSEIDON Group 1 to avoid unnecessary risks. Double embryo transfer strategy could be considered to improve the success rate for POSEIDON Group 2 with caution. Further stratification by age is needed for a more scientific discussion about the embryo transfer strategy for POSEIDON patients.

An elective single cleavage embryo transfer strategy to minimize twin live birth rate based on a prediction model from double cleavage embryos transfer patients.

OBJECTIVE: To minimize twin birth rate by establishing an elective single cleavage embryo transfer strategy based on a twin live birth prediction model from fresh double cleavage embryos transfer (cleavage DET) patients. METHODS: A total of 2478 patients underwent fresh cleavage DET in Nanjing Tower Hospital were enrolled to establish the twin live birth prediction model by logistic regression analysis and the cutoff value was calculated by ROC curve. Another 300 fresh cleavage DET patients and 550 cleavage single-embryo transfer (SET) patients were selected to testify the sensitivity, specificity and usefulness of this model. RESULTS: The twin live birth probability (TLBP) = eX/(eX + 1), e is a natural logarithm, X = -1.763 - (0.319 × female age) + (0.329 × endometrial thickness) + (0.282 × the number of transferred top embryos) - (0.314 × previous transfer times), and the cutoff value of TLBP was 24.2%. The sensitivity of this model for predicting twin live birth was 75.6%, while the specificity was 52.5% in the external validation of 300 DET patients. Furthermore, the validation of 550 SET patients showed that the live birth rate of TLBP value positive patients was significantly higher than that in negative patients (54.3% vs. 35.5%, p < .001). When adopted an elective single cleavage embryo transfer strategy, the patients with a positive TLBP value choose SET, while still undergo DET who with a negative TLBP value, the live birth rate would maintain as 56.7%; however, the twin birth rate would significantly decline to 7.4%. CONCLUSION: Female age, endometrial thickness, the number of transferred top embryos and previous embryo transfer times were critical variables for the twin live birth prediction model. An elective single cleavage embryo transfer strategy according to this model can maintain the relatively high live birth rate, meanwhile get the acceptable low twin birth rate.