P-700 Modelling individual follicle growth rates during ovarian stimulation in medically assisted reproduction cycles to enable prediction of final follicle profiles

Abstract

Abstract Study question Can the growth of individual follicles during ovarian stimulation in medically assisted reproduction (MAR) cycles be modelled to enable prediction of final follicle profiles? Summary answer Mean follicle growth rate is 1.35 mm per day. Our model reliably forecasts follicle size profiles on the final scan within ±2mm with 75.1% accuracy. What is known already Follicle growth rates during ovarian stimulation are believed to be linear with various mean growth rates reported between 1 to 4 mm per day. Follicles are believed to grow faster during ovarian stimulation than in the natural menstrual cycle. The change in mean follicle size was 1.69 mm per day with ovarian stimulation in 131 MAR cycles, and 1.4 mm per day in 50 natural cycles. However, there are only very limited data using individual follicle sizes to model follicle growth to enable prediction of future follicle size profiles. Study design, size, duration We conducted a retrospective cohort study of 12,950 patients from 11 European clinics (2005-2023). First IVF/ICSI cycles with at least three follicles and two scans were analysed. Using 8,564 cycles with scans one or two days apart, 98,029 follicles were modelled to estimate follicle growth rates per day. Stratification by age, weight, total antral follicle count (AFC), initial FSH dose, and suppressant protocol was performed. Predictive modelling was conducted using 39,698 scans including 434,082 follicles. Participants/materials, setting, methods Assessment of Follicle Growth: Follicles were ranked and the difference in corresponding follicle sizes over consecutive scans estimated, and the impact of baseline characteristics evaluated. Prediction of follicle size profile on final scan: A kernel density estimator and random forest model were developed using the same training data. Both approaches estimated future follicle growth from the initial scan. The two predictions were combined using maximum-likelihood adjustments. Generalisation error was estimated using independent test data. Main results and the role of chance The mean growth rate of individual follicles was 1.350 mm per day (95% CI 1.346 – 1.353 mm per day), which was lower than the previously reported mean follicle growth rate during ovarian stimulation (1.7 mm per day). The large number of patient-cycles and follicles assessed enabled a precise estimate of follicle growth per day during ovarian stimulation. Age, total AFC, initial FSH dose, bodyweight, and suppressant protocol had no statistically significant effect on the estimated mean growth rate. Using only the follicle sizes from the initial scan during ovarian stimulation, our model had 75.1% accuracy for predicting follicle sizes on the final scan within ±2mm. Including follicle size data from the first two scans improved the accuracy of the model to predict the follicle sizes on the final scan to 80.4%. In a confirmatory analysis, we cross-validated our findings across each of 11 individual clinics, and similar estimates for the generalisation error as for the standard test/train split were maintained, suggesting suitable generalisability of these findings. Limitations, reasons for caution A key simplifying assumption in this study is that follicles demonstrate monotonic growth patterns. Specifically, the relative ordering of follicle sizes is preserved over the entire cycle. This assumption enables straightforward extraction of growth statistics from the observed data but assumes that follicles do not regress in size. Wider implications of the findings We have used a large high-quality dataset, allied to advanced modern machine learning techniques, to precisely estimate follicle growth rates during ovarian stimulation, and use this to reliably predict future follicle size profiles during ovarian stimulation. This supports the design, personalisation, streamlining and success of future assisted conception cycles. Trial registration number None