Prediction models in in vitro fertilization; where are we? A mini review

Abstract

Since the introduction of in vitro fertilization (IVF) in 1978, over five million babies have been born worldwide using IVF. Contrary to the perception of many, IVF does not guarantee success. Almost 50% of couples that start IVF will remain childless, even if they undergo multiple IVF cycles. The decision to start or pursue with IVF is challenging due to the high cost, the burden of the treatment, and the uncertain outcome. In optimal counseling on chances of a pregnancy with IVF, prediction models may play a role, since doctors are not able to correctly predict pregnancy chances. There are three phases of prediction model development: model derivation, model validation, and impact analysis. This review provides an overview on predictive factors in IVF, the available prediction models in IVF and provides key principles that can be used to critically appraise the literature on prediction models in IVF. We will address these points by the three phases of model development.

Repeated implantation failure at the crossroad between statistics, clinics and over-diagnosis

“…In clinical practice, however, precisely discerning between good-and poor-prognosis couples is not always simple. The chances of pregnancy depend on patients' characteristics, such as age, ovarian reserve and cause of infertility, and IVF local policy, such as preimplantation genetic screening, extended culture, and number of transferred embryos; however, a precise personalized estimate remains difficult (van Loendersloot et al, 2014;McLernon et al, 2016b;Sarais et al, 2016). Most importantly, the definition of good-prognosis women, and thus the reliability of RIF diagnosis, should also be adapted to the local context given the elevated inter-centre variability of the effectiveness of IVF (Lintsen et al, 2010).…”

Public-Private Interaction Model; A Simple Strategy towards Making Assisted Reproduction Accessible in a Poor Resource Setting: A Prospective Descriptive Study

“…The rising trend of utilization has been observed in the developed countries, particularly where there is public funding for ART [20] . However in the developing countries access to ART remainsnothing but a dream to be realised [21] , largely because of the high cost and unaffordability of treatment [6,7,16,22] .…”

Computational prediction of implantation outcome after embryo transfer

“…19,26 Moreover, the lack of categorization and ordering of the features in the previous studies led to difficult interpretation and comparison of the results. 27 In a recent publication, 28 we introduced 20 prediction models on ART. The prediction target of all these models is pregnancy (i.e.…”