Prediction of oocyte maturation rate in the GnRH antagonist flexible IVF protocol using a novel machine learning algorithm-A retrospective study

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 approx-imately 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 matu-ration 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.