Comparative study between ovarian response prediction index versus anti-Müllerian hormone, antral follicular count as predictors of ovarian response in women undergoing IVF/ICSI cycles

Amin, Ahmed Hussein; Sherif, Nadeen; Askalani, Ahmed; SaadAlla, Shaza

doi:10.21608/ebwhj.2019.17205.1020

Cited by 1 publication

(1 citation statement)

References 25 publications

(40 reference statements)

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“…The risk of poor or excessive ovarian response after controlled stimulation remains a serious problem in reproductive medicine. Although the poor response (< 4 oocytes retrieved) occurs in 5 to 35% of women [ 24 ], higher doses of gonadotropins to retrieve more oocytes can lead to ovarian hyperstimulation syndrome [ 25 ]. Predictors providing a more accurate estimate of a woman’s ovarian response are being sought to optimize individual stimulation protocol before a given IVF cycle [ 10 ].…”

Section: Discussionmentioning

confidence: 99%

Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data

Zieliński¹,

Pukszta²,

Mickiewicz³

et al. 2023

PLoS Comput Biol

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Controlled ovarian stimulation is tailored to the patient based on clinical parameters but estimating the number of retrieved metaphase II (MII) oocytes is a challenge. Here, we have developed a model that takes advantage of the patient’s genetic and clinical characteristics simultaneously for predicting the stimulation outcome. Sequence variants in reproduction-related genes identified by next-generation sequencing were matched to groups of various MII oocyte counts using ranking, correspondence analysis, and self-organizing map methods. The gradient boosting machine technique was used to train models on a clinical dataset of 8,574 or a clinical-genetic dataset of 516 ovarian stimulations. The clinical-genetic model predicted the number of MII oocytes better than that based on clinical data. Anti-Müllerian hormone level and antral follicle count were the two most important predictors while a genetic feature consisting of sequence variants in the GDF9, LHCGR, FSHB, ESR1, and ESR2 genes was the third. The combined contribution of genetic features important for the prediction was over one-third of that revealed for anti-Müllerian hormone. Predictions of our clinical-genetic model accurately matched individuals’ actual outcomes preventing over- or underestimation. The genetic data upgrades the personalized prediction of ovarian stimulation outcomes, thus improving the in vitro fertilization procedure.

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Section: Discussionmentioning

confidence: 99%