Juan J Tarín1,2, Eva Pascual3, Miguel A García-Pérez4,5, Raúl Gómez4, Juan J Hidalgo-Mora4,6, Antonio Cano4,6,7. 1. Department of Cellular Biology, Functional Biology and Physical Anthropology, Faculty of Biological Sciences, University of Valencia, Dr. Moliner 50, Burjassot, 46100, Valencia, Spain. tarinjj@uv.es. 2. Institute of Health Research INCLIVA, Valencia, Spain. tarinjj@uv.es. 3. Department of Cellular Biology, Functional Biology and Physical Anthropology, Faculty of Biological Sciences, University of Valencia, Dr. Moliner 50, Burjassot, 46100, Valencia, Spain. 4. Institute of Health Research INCLIVA, Valencia, Spain. 5. Department of Genetics, Faculty of Biological Sciences, University of Valencia, Burjassot, Valencia, Spain. 6. Service of Obstetrics an,d Gynecology, University Clinic Hospital, Valencia, Spain. 7. Department of Pediatrics, Obstetrics and Gynecology, Faculty of Medicine, University of Valencia, Valencia, Spain.
Abstract
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.
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.
Entities:
Keywords:
Cumulative live birth; Fecundity; In vitro fertilization; Oocyte retrieval cycle; Predictive model
Authors: Juan J Tarín; Eva Pascual; Santiago Pérez-Hoyos; Raúl Gómez; Miguel A García-Pérez; Antonio Cano Journal: J Assist Reprod Genet Date: 2019-12-05 Impact factor: 3.412
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