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

Autor: Juan J. Tarín, Juan José Hidalgo-Mora, Miguel Angel García-Pérez, Antonio Cano, Eva C. Pascual, Raúl Gómez
Rok vydání: 2019
Předmět:
Zdroj: Journal of Assisted Reproduction and Genetics. 37:171-180
ISSN: 1573-7330
1058-0468
DOI: 10.1007/s10815-019-01642-3
Popis: To introduce a prognostic model for women’s assisted fecundity before starting the first IVF/ICSI treatment cycle. 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. 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. 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.
Databáze: OpenAIRE
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