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

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-assistedfecundity 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.

Pretreatment prediction for IVF outcomes: generalized applicable model or centre-specific model?

“…Our study also established ‘centre-specific models’ which were based on a single-centre dataset. While several IVF prediction models have been constructed for a similar purpose ( Cai et al , 2011 ; Qiu et al , 2019 ; Vogiatzi et al , 2019 ; Tarin et al , 2020 ; Chen et al , 2022 ; Qu et al , 2022 ; Wang et al , 2022 ), many of them included treatment parameters and intermediate outcomes such as oocyte yield and therefore could not predict the patients’ prognosis before treatment ( Cai et al 2011 ; Vogiatzi et al , 2019 ; Chen et al , 2022 ; Wang et al , 2022 ). A few recent studies reported pretreatment models comparable to ours.…”

“…Both studies are based on validations of random split data, rather than validations in a more recent cohort or other clinics/countries. The work of Tarin et al (2020) included the so-called ‘high-assisted-fecundity women’ who achieved live birth following their first attempt and ‘low-assisted-fecundity women’ who failed to have a live birth after completing three treatment cycles. The model reported an AUC of 0.718 and included a temporal validation.…”

“…National-wide registry data and models based on them are not always available, especially for low- and middle-income countries where cost and efficacy are of significant concern ( Chiware et al , 2021 ). For regions where national-wide data were not available, prediction models based on local data including only one or a few clinics have been established ( Cai et al , 2011 ; Qiu et al , 2019 ; Vogiatzi et al , 2019 ; Tarin et al , 2020 ; Zhu et al , 2021 ; Chen et al , 2022 ; Qu et al , 2022 ; Wang et al , 2022 ). Although it is recommended that individual clinics first validate and recalibrate national-wide data-based models using their datasets before developing their clinic-specific prediction models ( McLernon et al , 2022 ), the centre-specific models are more specific to their patients and may include potentially important predictors that are not recorded in a national registry database.…”

Prolactin Relationship with Fertility and In Vitro Fertilization Outcomes—A Review of the Literature

“…Other authors demonstrated that untreated transient anaesthesia-induced hyperprolactinemia is associated with a lower proportion of fertilized oocytes advancing to cleaving embryos in patients undergoing IVF, although oocyte fertilization and pregnancy rate were unaffected [ 56 ]. Tarín et al developed a predictive model for women’s assisted fecundity before starting the first IVF/ICSI treatment cycle which included prolactin as a prognostic factor which was negatively associated with IVF success [ 73 ].…”

In Vitro Fertilisation and Intracytoplasmic Sperm Injection predictive factors: A review of the effect of female age, ovarian reserve, male age, and male factor on IVF/ICSI treatment outcomes

“…In addition, research is directed towards the development of models that can predict IVF/ICSI outcomes. These include female age (Nelson & Lawlor, 2011;Khader et al, 2013;Hamdine et al, 2015;Dhillon et al, 2016;McLernon et al, 2016;Vaegter et al, 2017;Leijdekkers et al, 2018;Metello et al, 2019;Tarín et al, 2020;Wen et al, 2021) and/or ovarian markers (Khader et al, 2013;Hamdine et al, 2015;Dhillon et al, 2016;Leijdekkers et al, 2018;Tarín et al, 2020) as one of their predictors.…”