A valuable predictive model for optimizing the timing of oocyte retrieval: a retrospective analysis of oocyte retrieval time in 49,961 oocyte pickup (OPU) cycles
Abstract Background In clinical practice, scheduling oocyte retrieval is a challenging issue that requires comprehensive consideration of factors such as the woman's age, ovarian response, hormone levels and other variables. Moreover, there is currently no consensus on how to effectively consid...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
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| Series: | Reproductive Biology and Endocrinology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12958-025-01441-9 |
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| Summary: | Abstract Background In clinical practice, scheduling oocyte retrieval is a challenging issue that requires comprehensive consideration of factors such as the woman's age, ovarian response, hormone levels and other variables. Moreover, there is currently no consensus on how to effectively consider these factors and their weights in order to optimize the scheduling of oocyte retrieval for obtaining more mature oocytes. Objective To effectively identify the key determinants of oocyte retrieval time through an extensive analysis of retrospective clinical data, and to develop a valuable predictive model for optimizing the timing of oocyte retrieval in assisted reproductive technology (ART). Study design This retrospective study included 49,961 oocyte pickup (OPU) cycles, as well as 5567 subsequent ET cycles and 45,198 FET cycles between January 2010 and August 2024. Multiple linear regression (MLR) and directed acyclic graphs (DAG) were employed to identify the key determinants associated with oocyte retrieval time. Oocyte pickup (OPU) cycles achieving a minimum of 70% oocyte retrieval rate and 80% oocyte maturation rate, indicating well-organized timing of oocyte retrieval, were assigned to Group 1, while the remaining cycles were assigned to Group 2. The data from Group 1 was randomly divided into training and validation sets using the "sample" function in R software. The training set data was utilized to develop a predictive model for oocyte retrieval time based on former identified determinants using the "lm" function in R software. Subsequently, the performance of this model was evaluated and visualised using the "performance" and "plot" function, and further validated with the validation set from Group 1 as well as the data from Group 2. Results Female age, AFC, COH protocol, number of follicles > 14 mm in diameter on the day of trigger, and hormone levels on the day of trigger (including E2, P, and LH) were key determinants for the timing of oocyte retrieval. A valuable predictive formula for determining the optimal timing of oocyte retrieval has been formulated and validated: 37.43–0.02219*Female age + 0.01383*AFC + 0.00006* E2 level on the trigger day-0.00939*P level on the trigger day-0.05194*LH level on the trigger day + 0.01497*Number of follicles > 14 mm in diameter on the trigger day + β (β = 0.0000 in Short agonist protocol, β = -0.3320 in PPOS protocol, β = 0.8361 in GnRH antagonist protocol, β = -1.2280 in Mild stimulation protocol, β = 0.4160 in Long agonist protocol). Conclusion The female age, antral follicle count (AFC), controlled ovarian hyperstimulation (COH) protocol, number of follicles measuring > 14 mm in diameter on the trigger day, as well as hormone levels including E2, P, and LH on the trigger day are crucial factors influencing oocyte retrieval time. A robust predictive model for oocyte retrieval time was successfully developed from these factors and validated within a well-organized timing group for oocyte retrieval (oocyte retrieval rate ≥ 70% and oocyte maturation rate ≥ 80%). |
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| ISSN: | 1477-7827 |