Exploring gonadotropin dosing effects on MII oocyte retrieval in ovarian stimulation

Purpose Ovarian stimulation with gonadotropins is crucial for obtaining mature oocytes for in vitro fertilization (IVF). Determining the optimal gonadotropin dosage is essential for maximizing its effectiveness. Our study aimed to develop a machine learning (ML) model to predict oocyte counts in IVF...

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Veröffentlicht in:Journal of assisted reproduction and genetics 2024-06, Vol.41 (6), p.1557-1567
Hauptverfasser: Zieliński, Krystian, Kloska, Anna, Wygocki, Piotr, Zieleń, Marcin, Kunicki, Michał
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Sprache:eng
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Zusammenfassung:Purpose Ovarian stimulation with gonadotropins is crucial for obtaining mature oocytes for in vitro fertilization (IVF). Determining the optimal gonadotropin dosage is essential for maximizing its effectiveness. Our study aimed to develop a machine learning (ML) model to predict oocyte counts in IVF patients and retrospectively analyze whether higher gonadotropin doses improve ovarian stimulation outcomes. Methods We analyzed the data from 9598 ovarian stimulations. An ML model was employed to predict the number of mature metaphase II (MII) oocytes based on clinical parameters. These predictions were compared with the actual counts of retrieved MII oocytes at different gonadotropin dosages. Results The ML model provided precise predictions of MII counts, with the AMH and AFC being the most important, and the previous stimulation outcome and age, the less important features for the prediction. Our findings revealed that increasing gonadotropin dosage did not result in a higher number of retrieved MII oocytes. Specifically, for patients predicted to produce 4–8 MII oocytes, a decline in oocyte count was observed as gonadotropin dosage increased. Patients with low (1–3) and high (9–12) MII predictions achieved the best results when administered a daily dose of 225 IU; lower and higher doses proved to be less effective. Conclusions Our study suggests that high gonadotropin doses do not enhance MII oocyte retrieval. Our ML model can offer clinicians a novel tool for the precise prediction of MII to guide gonadotropin dosing.
ISSN:1058-0468
1573-7330
1573-7330
DOI:10.1007/s10815-024-03102-z