A predictive model for women's assisted fecundity before starting the first IVF/ICSI treatment cycle

PURPOSE: To introduce a prognostic model for women's assisted fecundity before starting the first IVF/ICSI treatment cycle. METHODS: In contrast to previous predictive models, we analyze two groups of women at the extremes of prognosis. Specifically, 708 infertile women that had either a live birth (LB) event in the first autologous IVF/ICSI cycle ("high-assisted-fecundity women", n = 458) or did not succeed in having a LB event after completing three autologous IVF/ICSI cycles ("low-assisted-fecundity women", n = 250). The initial sample of 708 women was split into two sets in order to develop (n = 531) and internally validate (n = 177) a predictive logistic regression model using a forward-stepwise variable selection. RESULTS: Seven out of 32 initially selected potential predictors were included into the model: women's age, presence of multiple female infertility factors, number of antral follicles, women's tobacco smoking, occurrence of irregular menstrual cycles, and basal levels of prolactin and LH. The value of the c-statistic was 0.718 (asymptotic 95% CI 0.672-0.763) in the development set and 0.649 (asymptotic 95% CI: 0.560-0.738) in the validation set. The model adequately fitted the data with no significant over or underestimation of predictor effects. CONCLUSION: Women's assisted fecundity may be predicted using a relatively small number of predictors. This approach may complement the traditional procedure of estimating cumulative and cycle-specific probabilities of LB across multiple complete IVF/ICSI cycles. In addition, it provides an easy-to-apply methodology for fertility clinics to develop and actualize their own predictive models.