ABSTRACT
Background: Normal reproductive function requires adequate regulation of follicle stimulating hormone (FSH) and luteinizing hormone (LH) secretion. During ovarian stimulation for in-vitro fertilization (IVF), some patients will demonstrate an early rise in LH despite being treated with a gonadotropin releasing-hormone (GnRH) antagonist, sometimes necessitating cycle cancellation. Previous studies have demonstrated a possible link between a premature LH rise with ovarian response to gonadotropins. We sought to determine what clinical parameters can predict this premature LH rise and their relative contribution. Methods: A retrospective study of 382 patients who underwent IVF treatment at Rambam Medical Center. The patients were stratified into age groups. A model predicting premature LH rise based on clinical and demographic parameters was developed using both multiple linear regression and a machine-learning-based algorithm. Results: LH rise was defined as the difference between pre-trigger and basal LH levels. The clinical parameters that significantly predicted an LH rise were patient age, BMI, LH levels at stimulation outset, LH levels on day of antagonist administration, and total number of stimulation days. Importantly, when analyzing the data of specific age groups, the model's prediction was strongest in young patients (age 25-30 years, R2 = 0.88, p < .001) and weakest in older patients (age > 41 years, R2 = 0.23, p = .003). Conclusions: Using both multiple linear regression and a machine-learning-based algorithm of patient data from IVF cycles, we were able to predict patients at risk for premature LH rise and/or LH surge. Utilizing this model may help prevent IVF cycle cancellation and better timing of ovulation triggering.
