Oocyte retrieval in social fertility preservation: challenging traditional beliefs on gonadotropin dosing and follicular size.

OBJECTIVES: To investigate treatment approaches for fertility preservation patients, with a focus on timing of oocyte retrieval, and to determine whether their characteristics differ from those of other IVF patients. Additionally, to evaluate the significance of follicle size on triggering day in the context of fertility preservation. METHODS: This retrospective cohort study was conducted in a tertiary, university-affiliated medical center. It compared 140 matched patients undergoing social fertility preservation to 140 patients undergoing IVF treatment due to male factor infertility. RESULTS: Patients undergoing fertility preservation received a higher initial gonadotropin dose and had more oocytes retrieved than the control group. Within the fertility preservation cohort, a negative correlation was observed between the rate of large follicles and the number of retrieved oocytes. While there was no significant association between rate of large follicles and oocyte maturation rate in the entire group, age-stratified analysis revealed a negative relationship. Analysis revealed that although traditional treatment determinants such as follicular size and gonadotropin dosing were considered, peak estradiol levels were consistently identified as significant predictors of treatment outcomes. CONCLUSIONS: Physicians may modify treatments for fertility preservation, emphasizing a higher gonadotropin dosage to maximize oocyte retrieval. Elevated estradiol levels can serve as a real-time predictive marker for the number of mature oocytes. While treatment strategies can influence outcomes, intrinsic patient factors, particularly baseline ovarian function, remain crucial. These results challenge beliefs regarding the importance of larger follicles and suggest the need for a tailored approach, considering patient age and specific fertility preservation objectives.

Machine learning for predicting elective fertility preservation outcomes.

This retrospective study applied machine-learning models to predict treatment outcomes of women undergoing elective fertility preservation. Two-hundred-fifty women who underwent elective fertility preservation at a tertiary center, 2019-2022 were included. Primary outcome was the number of metaphase II oocytes retrieved. Outcome class was based on oocyte count (OC): Low (≤ 8), Medium (9-15) or High (≥ 16). Machine-learning models and statistical regression were used to predict outcome class, first based on pre-treatment parameters, and then using post-treatment data from ovulation-triggering day. OC was 136 Low, 80 Medium, and 34 High. Random Forest Classifier (RFC) was the most accurate model (pre-treatment receiver operating characteristic (ROC) area under the curve (AUC) was 77%, and post-treatment ROC AUC was 87%), followed by XGBoost Classifier (pre-treatment ROC AUC 74%, post-treatment ROC AUC 86%). The most important pre-treatment parameters for RFC were basal FSH (22.6%), basal LH (19.1%), AFC (18.2%), and basal estradiol (15.6%). Post-treatment parameters were estradiol levels on trigger-day (17.7%), basal FSH (11%), basal LH (9%), and AFC (8%). Machine-learning models trained with clinical data appear to predict fertility preservation treatment outcomes with relatively high accuracy.