Predicting the optimal timing for triggering in controlled ovarian stimulation: mature oocytes retrieval predictor

Kobanawa Reproductive Biology and Endocrinology https://doi.org/10.1186/s12958-025-01489-7 (2025) 23:139 Reproductive Biology and Endocrinology Open Access RESEARCH Predicting the optimal timing for triggering in controlled ovarian stimulation: mature oocytes retrieval predictor Masato Kobanawa1* Abstract Background The development of assisted reproductive technology (ART) has revolutionized infertility treatment; however, its success largely depends on effective controlled ovarian stimulation (COS) and the timing of oocyte retrieval. This study aimed to develop a regression equation model to optimize the timing of ovulation trigger in COS.. Methods We retrospectively analyzed 503 COS cycles (380 with follitropin alfa, 123 with follitropin delta) as training data. We modified the Follicle-To-Oocyte Index (FOI) and developed the Follicle-To-mature Oocyte Index (FmOI), which indicates how many mature oocytes (MII) were obtained for each antral follicle count. This index was used as an indicator for the retrieval of mature oocytes. When using FmOI as the objective variable, we selected relevant factors through Lasso regression analysis. Based on the obtained regression equations, the accuracy was compared and verified by predicting the number of MII in the test data. Results Lasso regression analysis resulted in the creation of an FmOI prediction model using Initial serum FSH, number of follicles ≥ 14 mm, and total gonadotropin dose as explanatory variables. The regression equation model achieved Median Absolute Error values of 1.90 and 1.80 MII counts in the test data for the Alfa and Delta groups, respectively. Concordance index for MII prediction were 0.98 for follitropin alfa and 0.87 for follitropin delta. Use of the model showed higher CLBR in Alfa and non-inferiority in Delta than control group. Conclusion This model reliably predicts the number of MII and optimizes trigger timing in COS. By considering key predictors, it provides a precise tool to enhance clinical outcomes in assistedreproductive technology . Keywords Assisted reproductive technology, Oocyte retrieval timing, Follicle-stimulating hormone, Controlled ovarian stimulation, Mature oocytes *Correspondence: Masato Kobanawa 1 Kobanawa Clinic, 169-3 Tagiya, Omitama-shi, Ibarak-ken, Japan © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Kobanawa Reproductive Biology and Endocrinology (2025) 23:139 Page 2 of 14 Introduction Assisted reproductive technology (ART) has led to significant advances in the treatment of infertility and has fundamentally transformed reproductive medicine [1]. However, the overall success of ART relies heavily on optimizing controlled ovarian stimulation (COS) and precise timing of the oocyte retrieval procedure [2]. Effective COS is crucial to maximize the number of mature oocytes (MII) obtained, which is a major determinant of the success rate of ART [3]. Cumulative pregnancy and live birth rates per oocyte retrieval cycle have attracted worldwide attention as key performance indicators of ART [4, 5]. Important factors related to cumulative pregnancy and live birth rates include the number of oocytes and embryos available for transfer [6–9]. The introduction of recombinant follicle-stimulating hormone (FSH) formulations, such as follitropin alfa and delta, has provided more targeted stimulation protocols, allowing for personalized ovarian stimulation tailored to individual patient characteristics [10]. Various factors, including age, baseline antral follicle count (AFC), and basal serum hormone levels influence ovarian response, necessitating accurate predictive models to enhance treatment outcomes [11–20]. Indices have been developed to assess ovarian response, such as the Follicle-to-Oocyte Index (FOI), which is used to evaluate the efficiency of follicular development [21]. However, the ability to predict the number of mature oocytes (MII) that can be retrieved has not been fully addressed. We have, therefore, modified FOI to Follicle-to-mature Oocyte Index (FmOI) that quantifies the number of MII obtained in relation to the AFC, serving as a robust indicator to optimize the timing of oocyte retrieval. We aimed to use retrospective data from 380 cycles of follitropin alfa and 123 cycles of follitropin delta to establish a predictive model for accurately estimating FmOI and the number of MII, which may help in making more informed decisions regarding the optimal timing for triggering and enhance the clinical outcomes of ART. It focused on the development of a predictive model to support optimal timing of trigger administration in COS , based on estimated FmOI and MII yield. Materials and methods Our study was approved by the Medical Corporation Kobanawa Clinic Ethic Screening Committee. After approval, this study was conducted with an opt-out disclosure of information with patients who provided informed consent. This study consisted of two parts: Study 1: Development of the Mature Oocyte Retrieval Prediction Model. Study 2: Validation of the Predictive Model in Clinical Practice. It aimed to evaluate the model’s accuracy and clinical utility by retrospectively comparing outcomes from a prospectively managed cohort with data from previously treated patients. Study 1 Of the COS performed between April 2022 and December 2023, 380 cycles of follitropin alfa and 123 cycles of follitropin delta were retrospectively examined as training data. We included treatment-naïve women with COS who were treated with recombinant FSH monotherapy using either follitropin delta or follitropin alfa. The required sample size was calculated based on Cohen’s effect size (f^2) for multiple regression analysis. Assuming a moderate effect size (f2 = 0.15), a significance level (α) of 0.05, and a statistical power of 0.8, with 9 explanatory variables (p) referred to later, the minimum sample size was estimated to be 120 using the following formula: n={(p + 1)+(Zα + Zβ)^2}/f^2 where Zα = 1.96 and Zβ = 0.84. This ensures sufficient power to detect significant predictors while avoiding overfitting [22]. Starting on days 1–3 of menstruation, patients were administered daily subcutaneous (...truncated)