Prediction of oocyte maturation rate in the GnRH antagonist flexible IVF protocol using a novel machine learning algorithm – A retrospective study. (May 2023)

• Record Type: Journal Article
• Authors: Houri, Ohad; Gil, Yotam; Danieli-Gruber, Shir; Shufaro, Yoel; Sapir, Onit; Hochberg, Alyssa; Ben-Haroush, Avi; Wertheimer, Avital
• Is Part Of: European journal of obstetrics, gynecology, and reproductive biology. Volume 284(2023)
• Page Start: 100
• Page End: 104
• Publication Date: 2023-05
• Subjects: GnRH antagonist -- Oocyte maturation -- IVF outcome -- Machine learning -- Artificial Intelligence; Obstetrics -- Periodicals; Gynecology -- Periodicals; Reproductive health -- Periodicals; Gynecology -- Periodicals; Obstetrics -- Periodicals; Reproduction -- Periodicals; Obstétrique -- Périodiques; Gynécologie -- Périodiques; Reproduction -- Périodiques; Verloskunde; Gynaecologie; Voortplanting (biologie); Gynecology; Obstetrics; Reproduction; Electronic journals; Periodicals; Electronic journals; 618.05
• DOI: 10.1016/j.ejogrb.2023.03.022
• Languages: English
• ISSNs: 0301-2115

Abstract

Highlights: This novel machine-learning algorithm demonstrates oocyte maturation rate prediction ability in IVF/ICSI protocols. Application of machine learning allows for the integration of multiple parameters. A machine learning algorithm for oocyte maturation prediction allows for development of a prognostic tool before final trigger for ovulation and retrieval. Abstract: Oocyte maturation is affected by various patient and cycle parameters and has a key effect on treatment outcome. A prediction model for oocyte maturation rate formulated by using machine learning and neural network algorithms has not yet been described. A retrospective cohort study that included all women aged ≤ 38 years who underwent their first IVF treatment using a flexible GnRH antagonist protocol in a single tertiary hospital between 2010 and 2015. 462 patients met the inclusion criteria. Median maturation rate was approximately 80%. Baseline characteristics and treatment parameters of cycles with high oocyte maturation rate (≥80%, n = 236) were compared to cycles with low oocyte maturation rate (<80%, n = 226). We used an XGBoost algorithm that fits the training data using decision trees and rates factors according to their influence on the prediction. For the machine training phase, 80% of the cohort was randomly selected, while rest of the samples were used to evaluate our model's accuracy. We demonstrated an accuracy rate of 75% in predicting high oocyte maturation rate in GnRH antagonist cycles. Our model showed an operating characteristic curve with AUC of 0.78 (95% CI 0.73–0.82). The most predictive parameters were peak estradiol level on trigger day, estradiol level on antagonist initiation day, average dose of gonadotropins per day and progesterone level on trigger day. A state-of-the-art machine learning algorithm presented promising ability to predict oocyte maturation rate in the first GnRH antagonist flexible protocol using simple parameters before final trigger for ovulation. A prospective study to evaluate this model is needed.