ArticleReproductive biology and endocrinology : RB&E2025
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.
Article in Reproductive biology and endocrinology : RB&E, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Risk factors and treatment strategies for live birth in women with high BMI undergoing ART.BMC medicine · 2026Article
- Clinical Outcomes of Postponing Oocyte Retrieval Beyond 40 h After Trigger With Diclofenac Pretreatment Compared With the Routine Paradigm: A Retrospective Propensity Score-Matched Study.Reproductive medicine and biologyArticle
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Authors and funding
7 authors.
Funding
Abstract
backgroundIn 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.
objectiveTo 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
designThis 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.
resultsFemale 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 E
conclusionThe 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 E
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