Patients who developed ovarian hyperstimulation syndrome had significantly higher baseline anti-Müllerian hormone levels, antral follicle counts, and number of oocytes retrieved compared to those who did not.
Observational (n=10,206)
Yes
Machine learning models can effectively predict the number of oocytes retrieved and the risk of early-onset moderate-to-severe OHSS to help individualize FSH starting doses in IVF.
Background Accurately predicting ovarian response and determining the optimal starting dose of follicle-stimulating hormone (FSH) remain critical yet challenging for effective ovarian stimulation. Currently, there is a lack of a comprehensive model capable of simultaneously forecasting the number of oocytes retrieved (NOR) and assessing the risk of early-onset moderate-to-severe ovarian hyperstimulation syndrome (OHSS). Objective This study aimed to establish an integrated mode capable of forecasting the NOR and assessing the risk of early-onset moderate-to-severe OHSS across varying starting doses of FSH. Methods This prognostic study included patients undergoing their first ovarian stimulation cycles at 2 independent in vitro fertilization clinics. Automated classifiers were used for variable selection. Machine learning models (11 for NOR and 11 for OHSS) were developed and validated using internal (n=6401) and external (n=3805) datasets. Shapley additive explanation was applied for variable interpretation. The best-performing models were incorporated into a web-based prediction tool. Results For NOR prediction, 17 variables were selected, with the gradient boosting regressor achieving the highest performance (internal dataset: R2=0.7978; external dataset: R2=0.7924). For OHSS prediction, 19 variables were identified, and the LightGBM model demonstrated superior performance (internal dataset: area under the receiver operating characteristic curve=0.7588; external dataset: area under the receiver operating characteristic curve=0.7287). Shapley additive explanation analysis highlighted the FSH starting dose to BMI ratio and baseline antral follicle count as key predictors for NOR and OHSS, respectively. Dose-response curves were generated to visualize predicted outcomes with varying FSH starting doses. The models were implemented in a user-friendly, research-oriented online prototype, individualized ovarian stimulation guide (InOvaSGuide). Conclusions This study introduces an integrated framework for predicting NOR and early-onset moderate-to-severe OHSS risk across different FSH doses. Future prospective evaluation is needed before clinical implementation.
Chen et al. (Tue,) conducted a observational in Ovarian hyperstimulation syndrome (OHSS) (n=10,206). Machine learning prediction models (InOvaSGuide) was evaluated on Ovarian hyperstimulation syndrome (OHSS) and number of oocytes retrieved (NOR). Patients who developed ovarian hyperstimulation syndrome had significantly higher baseline anti-Müllerian hormone levels, antral follicle counts, and number of oocytes retrieved compared to those who did not.