Scheinhardt MO, et al. - Using an independent dataset, researchers examined the performance of previously established regression models in predicting low and high ovarian response to 150 μg corifollitropin alfa/GnRH-antagonist ovarian stimulation. Findings suggest the validity of logistic regression models in forecasting the outcome of ovarian stimulation with 150 μg corifollitropin alfa in a fixed, multiple dose GnRH-antagonist protocol, with AMH being of paramount importance.

Methods

• Researchers performed an analysis of data from a prospective, multi-centre (n = 5), multi-national, investigator-initiated, observational cohort study.
• In a GnRH-antagonist multiple dose protocol for transvaginal oocyte retrieval for IVF, ovarian stimulation was performed with 150 μg corifollitropin alfa among infertile women [n = 211; body weight >60 kg].
• They prospectively assessed demographic, sonographic and endocrine parameters on cycle Day 2 or 3 of spontaneous menstruation before ovarian stimulation.
• Low (<6 oocytes) and high (>18 oocytes) ovarian response were the main outcomes.
• Firstly, they used the original parameters to test the previously established prediction models for low ovarian response (LOR) and high ovarian response (HOR).
• Secondly, they used the established models to test the re-estimated parameters generated from the present data.
• Thirdly, they estimated several logistic regression models for the development of new predictive models of both LOR and HOR.
• Comparison of resulting prediction models was performed by means of the area under the receiver operating characteristic curve (AUC) and bias-corrected Akaike’s Information Criterion (AICc) to identify the most reasonable model for each scenario.

Results

• Findings revealed remarkably good performance of the previously established prediction models for low and high response on this dataset (low response AUC 0.8879 (95% CI: 0.8185–0.9573) and high response AUC 0.8909 (95% CI: 0.8251–0.9568)).
• In this study, a newly developed simplified model for LOR with log-transformed AMH values and only age as another covariate displayed an AUC of 0.8920 (95% CI: 0.8237–0.9603) with the lowest AICc of all models compared.
• A simplified model using AMH, FSH and AFC is suggested for predicting HOR, (AUC of 0.8976, 95% CI: 0.8206–0.9746).